architecting experience · 2019-06-21 · 9 x6 b2242 architecting experience: a marketing science...

Architecting ExperienceA Marketing Science and

Digital Analytics Handbook

9607hc_9789814678414_tp.indd 1 13/8/15 9:33 am

Advances and Opportunities with Big Data and Analytics (AOBDA)

Series Editor: Russell Walker (Northwestern University, USA)

Published:

Vol. 1: Architecting Experience: A Marketing Science and Digital Analytics Handbook by Scot R. Wheeler

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NEW JERSEY • LONDON • SINGAPORE • BEIJING • SHANGHAI • HONG KONG • TAIPEI • CHENNAI • TOKYO

World Scientific

Advances and Opportunities with Big Data and Analytics

Architecting ExperienceA Marketing Science and

Digital Analytics Handbook

Scot R WheelerMedill-Northwestern University, USA

9607hc_9789814678414_tp.indd 2 13/8/15 9:33 am

Published by

World Scientific Publishing Co. Pte. Ltd.5 Toh Tuck Link, Singapore 596224USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE

Library of Congress Cataloging-in-Publication DataNames: Wheeler, Scot R.Title: Architecting experience : a marketing science and digital analytics handbook / Scot R Wheeler, Medill-Northwestern University, USA.Description: | Series: Advances and opportunities with big data and analytics; 1 | Includes bibliographical references and index.Identifiers: LCCN 2015028389| ISBN 9789814678414 (hardcover : alk. paper) | ISBN 9814678414 (hardcover : alk. paper) | ISBN 9789814725651 (softcover : alk. paper) | ISBN 9814725651 (softcover : alk. paper) Subjects: LCSH: Communication in marketing. | Digital media.Classification: LCC HF5415.123 .W48 2016 | DDC 658.8/02--dc23LC record available at http://lccn.loc.gov/2015028389

British Library Cataloguing-in-Publication DataA catalogue record for this book is available from the British Library.

Copyright © 2016 by World Scientific Publishing Co. Pte. Ltd.

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In-house Editor: Philly Lim

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Printed in Singapore

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b2242 Architecting Experience: A Marketing Science And Digital Analytics Handbook9”x6”

v

Contents

About the Author viii

Introduction ix

The Foundations of Personalization 1

1.1 The New Business Value: Analytics Increase Relevance 4

1.2 Introducing the “Demand Chain” 71.3 The Customer Journey 81.4 Research and Analytics 16

Strategy, Technology, Science & Art 21

2.1 Paid, Earned, or Owned Breakdown 262.2 The Changing Nature of Marketing Data 322.3 The Fundamental Analytics Architecture:

The Analytics Pyramid 37

The Applied Digital Analytics Playbook (ADAP) Part One 49

3.1 ADAP Section One: Problem Definition 503.2 ADAP Section Two: Solution Definition 55

Chapter ONE

Chapter TWO

Chapter THREE

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b2242 Architecting Experience: A Marketing Science And Digital Analytics Handbook 9”x6”

vi Contents

The Changing World of Owned Media 71

4.1 Web Architecture & Web Data Collection 734.2 Client-side Tagging 794.3 Tagging Design & Deployment 874.4 Mobile Marketing 954.5 Email Marketing 964.6 Introducing Cookies 1024.7 Applying Owned Channel Metrics 105

Earned Media: Organic Social & SEO 115

5.1 History 1155.2 Organic vs. Paid Social Media 1195.3 Organic Social Media Strategy 1215.4 Inbound Organic Social

Data Sources for Key Objectives 1235.5 Applying Social Metrics 1275.6 Search Engine Optimization 146

Paid Media Analytics 151

6.1 Digital Paid Media Touch-points 1526.2 The Paid Media Ecosystem 1536.3 Targeting & Retargeting 1596.4 DSPs and Programmatic Real-time

Bidding (RTB) 168

Testing & Optimization. Marketing Automation. Attribution 173

7.1 Prescriptive Analytics: Testing & Optimization 173

7.2 Marketing Automation 1877.3 Cross-channel Attribution 196

Chapter FOUR

Chapter FIVE

Chapter SIX

Chapter SEVEN

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Contents vii

Data Management, Models, and Algorithms 199

8.1 The Applied Digital Analytics Playbook (ADAP) Part Two 199

8.2 Data Mining & Data Visualization 2058.3 Predictive Analytics & Machine Learning 208

The Cultural and Organizational Impact of Data 221

9.1 Visualization 2219.2 The Information Society: Media

Cycles & Feedback Loops 2299.3 Organizational Change for Effective

Digital Analytics 250

Conclusion 263

Index 267

Chapter EIGHT

Chapter NINE

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viii


About the Author

Scot Wheeler is a leader in digi-tal analytics delivery, overseeing a team which develops consumer intelligence, prospect conver-sion propensity scoring, cross-channel performance evaluation, environmental trend analysis, testing, targeting and optimiza-tion, and predictive modeling

for budget allocation and response forecasts.

He is also an adjunct lecturer in Northwestern University’s Master’s Degree program in Integrated Marketing Communications, where he teaches Digital Analytics and Statistics.

Scot received his MBA in Strategy, Finance and Marketing from Northwestern University’s Kellogg School of Management. Prior to his current roles, Wheeler was Group Director of Marketing Science for the digital agency Critical Mass. Before that, he ran product development, marketing and sales for the social media analytics platform Evolve24. Wheeler’s professional background spans a variety of technology, consulting and agency roles. From his start in software development, Scot’s 20 years of experience at the inter-section of technology and marketing includes work with Yahoo!, GE, Electronic Arts, AT&T, MasterCard, State Farm, USAA and HP.

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ix


Introduction

In the Integrated Marketing Communications approach taught at Northwestern University, the con-sumer is placed at the center of all marketing prac-tice. Unfortunately, this customer-centricity is not always as common in practice as it should be in the real-world of digital marketing. In actual practice, brands often place concern for awareness of their message at the center of their marketing practice, and much “digital strategy” is simply an effort to ensure consistent branding and “messaging” across digital channels. However, any digital marketing practice that is focused on brand message and struc-tured primarily by channel and function (the paid media team, the social media team, the web team) will typically fail to create a truly integrated and rel-evant experience as a consumer moves across digital channels. The capability to capture and use data from any consumer’s digital engagement, and the growing expectation for content personalization that consumers have as a result (beginning with each user’s Amazon and Netflix experience), means that the disconnection of data across channels will be felt by the user and will adversely impact their experi-ence with the brand. Conversely, the effective collec-tion and connection of data across channels will play a significant role in creating and maintaining brand relationships with the digitally embedded consumer.

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x Architecting Experience


Thus, the question this book sets out to answer is the biggest question facing digital marketers today: how do I deliver content and experience around my brand that is relevant enough to drive engagement in the user’s current context? The quick answer to this is of course through the application of data and ana-lytics to drive highly relevant, contextual targeted content and adaptive experience, but since this answer is not as easy to achieve as it is to say (and it is a mouthful), this book has been designed to help you develop the understanding and skills required to make this happen.

The path to delivering relevant, contextual and even adaptive digital experiences is not one for the mar-keter to walk alone, and this book will explore the relationships that must emerge between marketing, technology, research and operations to bring about truly effective 21st digital experience delivery. At the end of the day however, the envisioned reader of this book has the strongest interest in the marketing per-spective on these conversations, with a 21st century marketing mindset that understands marketing as innovation and technology driven customer-centric relationship building for long-term customer value versus message dissemination for the masses myopi-cally focused on driving business transactions above all else.

Digital communications long-ago turned mass-media on its head, a fall from which mass-media as the top effective communications form will never recover. In a world with a seemingly infinite amount of content and scores of methods for consuming that content, communication today is about appealing to individu-als, person by person, and appeal requires relevance

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Introduction xi


in context. In any conversation, delivering relevance in context requires understanding the person you’re speaking with. This is true for digital marketing as well.

This book will focus on the impact of data and tech-nology on marketing both within businesses and for consumers as well. It will allow you to guide your organization in a necessary process of continuous evolution to effectively collect and use the right data, analytics, technology platforms and algorithms to achieve valuable outcomes.

This evolution is built around a six-stage process which is facilitated through an Applied Digital Ana lytics Plan (ADAP), which is introduced in Chapter 3:

1. Define the problems that data can solve.2. Identify sources of data (existing and potential).3. Collect, manage and analyze data.4. Overcome organizational and cultural inertia.5. Apply data and analysis to solve the problems.6. Evaluate the outcomes.

This book will explain how evolution within the pro-cess detailed above is achieved through the follow-ing activities:

1. Data-driven problem identification and data-ori-ented strategic communications design (design research).

2. Strategic alignment of customer and business objectives.

3. KPI development and documentation from objectives.

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xii Architecting Experience


4. Marketing channel digital data collection strategy and implementation.

5. Multi-channel data integration. 6. Testing and optimization across all channels.7. Integrated planning models and performance

reporting.8. Predictive analytics and adaptive digital experi-

ence enablement.

These activities build upon and interact across each other and are discussed in detail through the remaining chapters, with the first several chapters focusing on the specifics of data and collection and analysis for owned media, earned media and paid media channels, and with the later chapters focused on integrating data across channels and applying it to continual optimization of results in omni-channel engagement with customers through applied analysis and technology. Before proceeding into these activities however, it is worth our while to begin with a deeper examination of the question of relevance in digital marketing. What is relevant to our customers at any given point in time? How do we know? And how do we take that knowledge and use it to deliver better experiences that in turn yield bet-ter results?

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1


The Foundations of Personalization

As the digitization of life proceeds with seemingly exponential progress, we continually find ourselves in an ever changing cultural landscape, where each day the amount of new information recorded is greater than all of the world’s recorded information prior to the digital age, where the average citizen of nearly all nations has unprecedented access to knowledge, entertainment and opinion, and of course where those same citizens are exposed to hundreds of adver-tisements a day on screens both stationary and mobile.

We find ourselves in a world where social life increas-ingly means digital life, and where success in busi-ness and marketing require advanced capabilities to access and interpret data. In short, we’ve crossed the horizon into a world where it can be argued that culture (i.e. work, arts and entertainment, customs, habits and pastimes) has largely become a product of information technology, and that correspond-ingly, information has become the core of culture in the developed world.

Living in a technologically and digitally driven world means living with constant change. In the 20th century, the economic and cultural base of the developed world transformed from being agriculturally (and

ChapterONE

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2 Architecting Experience


land) based to manufacturing and technology based over half a century, with the Second World War finally cementing the rise of the technocrat over the gentry as culture’s new elite. The transformations that have occurred as developed culture has then shifted from capital intensive analog technology to skill and infor-mation intensive digital technology have come more and more rapidly, and many businesses are still trying to catch up with the changes in technology and society that have come at them over the last two decades.

The continual development of information technol-ogy and applications arises from a human drive to continually expand both our knowledge and conveni-ence, a drive that has been at the core of advancement in science and technology for centuries. In the middle of the 20th century, the study of such advancement in communication technology was taken up by a profes-sor of communications at the University of Toronto in a way that forever changed our understanding of the relationship between media and society.

Given the breadth and depth of any individual’s exposure to media today, it seems inconceivable that the phrase “Media” and the concepts associated with it would at one time have required an introduction and development within popular thinking, but in fact there was such a time not that long ago, and the man who made the introduction was Professor Marshall McLuhan.

McLuhan’s 1965 book Understanding Media: The Extensions of Man introduced the concept of Media where previously there had simply been notions of independent communication technologies such as ‘the press’, ‘television’ and ‘advertising’ which were

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recognized to synthesize into a single unit in practice, but were nonetheless typically evaluated individually with regards to their impact on culture.

McLuhan observed that these and many other infor-mation and communication technologies and prac-tices not only work together and proceed from one another, but that in doing so they actually extend the perceptual power of individuals and mediate com-munication and thinking in ways that significantly influence culture and human affairs; thus his appli-cation of the term Media to these mediating ‘exten-sions of man’.

Perhaps the most lasting convention introduced by McLuhan (and the most useful for the subject of this book) is the notion of “hot” and “cool” mediums, or elements of culture. Very few people wonder who should be credited for coining the use of ‘cool’ in social context, e.g. a ‘cool’ new band or the ‘cool’ kids at school, since the expression seems to have always been a part of the vernacular. Equally, the idea of a ‘hot’ new sound or a person with a ‘hot’ body are commonly used in American parlance without con-sideration of origin. Today, these notions of “cool” and “hot” seem natural in the ways they are applied, but it was just as recently as the beginning of the Cold War that Marshall McLuhan observed that different media exerted different influences on people’s per-ceptions and engagement with those media, and clas-sified those media into “hot” and “cold” categories.

McLuhan’s theory has held up incredibly well into the early 21st century media environment, and still provides an excellent framework for understanding the influence of mediated information on culture,

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and the countervailing response of culture in the subsequent development of new information tech-nology. Everyone wants their content to be “hot”, “sticky” and “viral”, and these metaphors for informa-tion owe much to McLuhan’s understanding of how people engage with messages in media. McLuhan’s thinking is worth consideration by anyone interested in the science of marketing communications, and will be explored in detail in Chapter 9. But to begin with, we’ll first boil down the application of McLuhan’s ideas to marketing to a fundamental principle — the message is either relevant in the receiver’s current context, or it is not.

1.1 The New Business Value: Analytics Increase Relevance

In 20th century marketing, messages from the brand were thought to be uni-directional, and consumer interaction with these messages was thought of as passive receipt and absorption of the message. Digital communications turned that idea on its head. Digital communications are multi-directional, as consumers have the capability to respond and interact directly to and/or about the brand through both shared and owned media channels. This interaction between brand and consumer or by consumers about/around a brand in all digital channels is commonly referred to as “engagement”, and effective engagement is the objective of all digital marketing. Engagement result-ing from digital marketing may be as simple as click-ing on a link or liking/sharing content (and thus passing it along through a network), or it may be more involved, such as returning to a brand’s digital experience, going deeper into content and tools,

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adding their own new content, attending an event, completing a form, making a purchase or sharing a referral.

When it comes to engagement, one rule stands out over all others: relevance drives results. We are no more likely to engage with activities and conversa-tions that have no appeal or value to us in digital than we are in the real world. In fact, digital gives us much better ways than we have in real-life to filter out irrelevant and uninteresting content. Digital also gives us much more content to filter than we face in real-life, which for most users creates a high thresh-old between what is potentially viewable for them and what actually elicits engagement.

This returns us to the largest problem that the digital marketer faces today: how do I deliver content around my brand that is relevant enough to drive engagement in the user’s current context? The answer to this is of course through the application of data and analytics to drive highly relevant, contextual targeted content and adaptive experience.

The figure below tells a story about the rise of rele-vance in digital communications channels over the past two decades.

As we see in Figure 1.1 in the earliest days of main-stream digital communication, marketing was con-ducted through email, display advertising and websites. Of course, the dawn of email marketing brought the immediate dawn of spam, since in the early days, sim-ply having an email address qualified you for targeting by anyone who could get that email. In early digital marketing, display advertising was made more relevant than email by virtue of some occasional effort to align

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advertising with the content of the page on which it was advertised, at least by marketers who didn’t want to throw their digital budget down a black hole. And marketing on websites was the most relevant content on the web for those exposed to it since they were qualified to see it based on having sought it out.

Search marketing arrived on the scene in earnest in 1999 to take the top position for delivering relevant marketing content through algorithms that matched expressed inter-est or intent with digital content results. Being based on explicit interest cues and algorithmic matching of content to those cues, search has remained toward the top of the relevance ladder ever since, being surpassed recently only by content that has interest cues, algorithmic content tar-geting and memory of user history in a more specific con-text than the blank page of a new search. However, Google’s interest in having more user context data from across all platforms will likely see the return of “predictive” search (exhibited currently in the Google Now application)

Figure 1.1 The Rise of

Relevance

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as the most relevant form of content delivery around any users immediate content needs in context.

What Figure 1.1 shows us — beginning with search then extending to social content, social ads and eventually lifting all boats — that increasing data about context and algorithms for matching content with context are helpful in delivering relevance. But where is this data, and how do we use it to discover what is relevant, and apply that understanding to driving results?

1.2 Introducing the “Demand Chain”

A company’s supply chain and the practice of supply chain management is critical to that company’s ability to produce and deliver its goods to its customers. An organization’s supply chain is the linkage of material, processes and people that proceeds from the initial procurement of the raw materials needed to product a product, all the way through production to the final delivery of that product to the end customer. Without careful management of the supply chain, among other problems, materials required for production might not be available when needed, warehouses could be overflowing with un-needed raw material or finished product, or more product than needed could be produced to sit on shelves in stores without buyers. Supply chain management begins with pro-jections around the demand for products, then puts into motion all of the gears required to produce and then distribute the right amount of product in the right places at the right time based on that demand.

While a complex array of production inputs, outputs and logistics provide the material for supply-chain management, it is the projection or forecast of

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demand from the market that provides the impetus. If the forecasted demand for product is incorrect, then the best a highly effective supply-chain manage-ment process can do is try to adjust to the estimating error once it becomes apparent.

Creating perfectly precise demand forecasts is nearly impossible, so producers of goods and services have options. If the good is something packaged and sold in a store, then a target for sales is set, and produc-tion (in actual units or in production cost-to-profit ratio for something like software) is run to that target. Once produced, the product is put up for sale in stores and/or online. Once the product is up for sale in some location, the supply chain has done its job until more product is needed — which can be quickly for made-to-order goods or services. However, once the product is up for sale, the product has entered the “demand chain” — a less recognized and less understood area of the marketing equation.

If the supply chain is the process that pushes a prod-uct out to where it can be bought, the demand chain is the counterpart process through which the cus-tomer ultimately pulls the product into their basket. Similarly, if the supply chain is the process that pro-duces product supply, then the demand chain is the process that produces demand.

1.3 The Customer Journey

While marketing strategy and marketing communica-tions tries to understand and tap into the demand chain, it is a common and disadvantageous mistake to think that marketing drives the demand chain, and is

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the source of product demand. Demand for products starts with a need or desire in a consumer. It is very true that advertising applies psychology to evoke needs and desires, but it is also true that most goods also fulfill an actual need or demand. Advertising can be used to cultivate the perception of a certain brand of clothing as more sexy or sophisticated than another, but the demand for clothes was already there. Demand also requires a stimulus to buy — I may be exposed to advertising that guides me to fully perceive a brand as tied to some characteristic or quality, but perception is not purchase. To become a customer, there must be some kind of trigger prompting me to buy some-thing in that product category. Only then will my pre-established perceptions of the characteristics of various options begin to matter.

So, the demand chain begins with a “trigger” to con-sider a purchase. This can be as simple as running out of toilet paper, or as complex as recognizing the need to determine a care plan for an aging parent. The most traditional concept of the consumer path to purchase (along the demand chain) envisioned marketing as a funnel that brought the consumer from awareness, to interest, to desire and then finally to action, or purchase.

This way of thinking of customer engagement has guided generations of marketing planners and mar-keting campaigns, with no expense spared on aware-ness and branding campaigns based on the idea that more volume at the top of the funnel has to translate to more volume out the bottom of the funnel. Of course, the funnel was never a funnel as there was never 100% retention of what went into the top. Instead, it was more of a sieve, with much of what went into the top

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spilling out before it ever reached the bottom. Thus, the idea that increasing volume at the top of the funnel would increase sales at the bottom has never been guaranteed. In fact, depending on how many and how large the holes in the process of moving consum-ers from awareness to purchase, there has always been strong potential to waste huge amounts of time and effort moving people into the top of a process from which they would immediately fall out.

This recognition of prospect attrition throughout the traditional “funnel” to purchase and the question about how to decrease such attrition necessitated a new way of thinking about the path to purchase. In 2009, McKinsey Consulting introduced the idea of the Customer Decision Journey, which has subse-quently become the new standard in thinking about the path consumers take from awareness through purchase and importantly, even after purchase. Since its introduction, it has gone through several stages of evolution and refinement, such as the version of the journey we will reference throughout the pages of this book.

Figure 1.2 takes the original notions of the McKinsey Customer Decision Journey and adds two additional dimensions: the role of external “life events” as trig-gers to the customer decision journey, and the inter-action points between customers on the journey and data about those customers.

The process begins on the far left with a “life event”, which is some piece of context that provides the impetus to take action in our product category. Life events are diverse, and relevant life events for any business will vary based on the nature of that busi-ness. Life events range from major events such as

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marriage, a new job, a new house or the birth of a child to everyday events such as hosting a party or even just having time for lunch, reaching the week-end, getting off from work in the afternoon, or need-ing toilet paper. Life events do not need to be major in order to be significant triggers for the customer decision process. The size and scope of the life event is not what matters in itself. What matters most is that we, as marketers, are cognizant of the fact that there is always an external context to a customer’s entry into a decision journey — that the customer has a life out-side the decision process, and that something about that life brought them in to the decision process. Understanding this, the marketer should treat every life event as something important enough to their customer to trigger the expenditure of thought and energy through the decision process, and should of course recognize that the more significant the life event, the more significant the customer problems, objectives and needs.

From the triggering event, we proceed clockwise through the diagram. Customers in the first round of

Figure 1.2 The Customer

Decision Journey

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the journey will travel around the outside path labeled “active evaluation”. While on this path of active evaluation, they are developing an “initial con-sideration set” with regard to the options available to them to address the problems, objectives and needs established with the triggering event. Awareness of their options is of course very important at this stage, so it is good that awareness building is already a sig-nificant piece of most marketing programs. But ini-tial consideration extends well beyond just awareness and is in fact a process of comparison and evaluation leading to a decision.

To survive the active evaluation stage, brands must not only stay within consideration throughout the process, but must also stand-out from other options by the time the process reaches a decision point. While understanding the importance of awareness has led to a focus on that stage, with data being used in increasingly sophisticated ways to optimize spend-ing on targeted impressions for awareness develop-ment, the entire active consideration process is still under-addressed by most marketing programs. This is changing with the evolution of marketing auto-mation software, and those firms that are address-ing this change with the most focus are poised to reap the rewards. To repeat one of the most com-mon themes of this book, relevance delivers results, and if there is a critical time to establish relevance, it is during the period of time when your company is being considered against competitors for a fit with your customers’ needs. The means to establish-ing relevance is of course by generating meaningful points of engagement from insights about each customer’s context as drawn from your data. The method for this will emerge through the rest of the

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book, so suffice it to say here that knowing when and why to apply data to engagement is at least as critical as knowing how to do so.

Moving clockwise along the diagram, the customer moves through active evaluation and ultimately reaches a decision. If the trigger was not strong enough to overcome dissatisfaction with all of the options considered, then the customer may decide not to make a purchase at all. If the trigger was strong but no option was truly satisfactory, then the cus-tomer will begrudgingly select the least dissatis-factory option. With the long-tail of options ranging from standard to niche presented today in most mar-kets, this is increasingly less of a concern to most consumers, who are happy for example to switch from hotels to Airbnb to meet a set of requirements that the hotels could not match, but which they had to accept before Airbnb was an option. And if during active consideration one option differentiated itself according to the customer’s needs, at this point the decision will be made to purchase from that brand.

Continuing clockwise around the consumer decision journey, the next stage focuses on the post-purchase, or post-decision, experience that the customer has with a brand. Typically, this is thought of as “cus-tomer service” for customers who have bought some-thing from us, and that is a large segment of the population that a company will engage at this stage in the journey. But those customers who decision led to an option other than ours still can, and should, also be engaged here.

For those potential customers who chose another option, our post purchase engagement will be

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focused largely around paid media. Perhaps most importantly — and currently unfortunately uncommon — we need to recognize when these prospects have made a decision and are no longer in active evaluation, and we need to suppress serving them ads around our product. Most readers of this book, as users of the web, will know how annoying it is to be served ads about something we have already purchased. So, even if we have lost a sale, it is impor-tant that we do not further dissuade the prospect from engaging with us by continuing to try and get them to buy something they are no longer shopping for. Having provided that level of relevance in their experience, we then use data (primarily from paid media and Data Management Platforms [DMPs]) to anticipate their future needs and to be ready to deliver greater relevance when they reach their next trigger.

For active customers, engagement in the post-decision stage of the journey is the key to guiding them into the “loyalty loop”. This loyalty loop comes from establishing a strong preference for our brand with the customer so that at the next trigger, the customer’s active evaluation will default to our com-pany’s options and will bypass the active evaluation of our competitors. Suppression of no longer rele-vant paid media and conversion-oriented email is equally important here to avoid alienation from our brand post-purchase. Nothing seems sillier (or is more wasteful to the marketing budget) than seeing targeted advertising from a brand for a product recently purchased from that same brand. Having shown that we can achieve minimal context recogni-tion by knowing that this customer has purchased from us, we then use 3rd party DMP data as well as first-party data from the customer’s purchase

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experience and post-purchase engagement with our earned and owned media channels (e.g. app, web, email, social, customer service) to ensure satis-faction with their decision, and to anticipate and prepare relevant responses to their potential upcom-ing needs. If we’ve done this effectively (through methods this book will provide in subsequent chap-ters), we are then much better poised to respond faster and with more relevance to the customers next round of needs than any other options, elevating our brand in their next round of consideration.

The Customer Decision Journey (CDJ) provides a very helpful sense of flow from trigger to purchase and post-purchase behavior that allows more refined marketing strategy within the demand chain. It is worth noting that even the most helpful models tend to oversimplify the reality they seek to repre-sent, and as two of my Northwestern University Integrated Marketing Communications (IMC) pro-gram colleagues have observed, the customer deci-sion journey is no different. At any level of perspective on a process, there is a usually a more complex layer beneath. Professors Ed Malthouse and Tom Collinger in the IMC program conduct research into drivers of the decision process through the Spiegel Research Institute, and have developed a conceptualization of customer demand that consid-ers the process as less of a flow and more of a con-tinuous interaction of multiple drivers to the purchase decision.

The visualization (see Figure 1.3) represents the inter-dependence of several factors in the purchase deci-sion, as movement in any of the gears in the diagram will turn all the other gears. This conception of the deeper mechanics behind the decision to purchase

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from a brand fits perfectly with the customer decision journey with the understanding that the gears are the mechanism by which the consumer is driven through the CDJ flow.

1.4 Research and Analytics

The Spiegel Institute is one of many academic organiza-tions dedicated to research that helps organizations better understand the consumer and the type of brand engagement that produces business outcomes. And certainly every company that survives or thrives in its market has some person or organization dedicated to that same understanding. As digital marketing channels and the collection and application of data in those chan-nels continues to evolve, market research, customer insights and analytics are playing an integrated role in digital marketing design and execution. In a market moved by the facilitation of relevant experiences, the organization with the best understanding of their

Figure 1.3 Spiegel

Customer Engagement Eco System

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customers, the demand for their products, and the driv-ers through the path to purchase have a clear advantage over competitors with less understanding of any of these drivers of sales and loyalty.

I am often asked to clarify how digital analytics differs from the legacy market research function extant in most organizations. Figure 1.4 shows the way in which research and analytics wrap around and through the marketing process from pre-trigger to post-purchase.

As discussed, the consumer decision journey starts with a trigger — which we see at the bottom center of Figure 1.4. The trigger itself occurs within an indivi dual, some-one with needs, wants, interests, attitudes, beliefs family and friends. The individual is also of a certain age, gen-der, ethnicity, income range, geography, education level and acting within a set of current circumstances. In the center of the diagram above we see a flow from trigger to the decision to purchase or not. The flow is driven through the customer decision journey process by the mechanics defined by the Spiegel institutes gears.

Figure 1.4 Research and

Analytics

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To the left and right of the path to purchase lie the outside influences on that path. From the left come the macro-level external influences over the path to purchase, including economic factors, competitors’ actions in the marketplace, and changes in technol-ogy that alter production, supply or demand factors within our market. From the right come influences related specifically to our brand, including the quality and value of our product and accompanying customer service, the public response to our product, and of course, our marketing efforts. Both of these sets of influences serve as inputs and catalysts to the decision that takes place through the path to purchase.

The various flavors of research and analytics reside around and within this process and its influences. From a supply-chain standpoint, the most fundamen-tal (though not simple) method of market research occurs on the arrow marked “Research (1)”, which considers segments of consumers, their purchases, and the influence of external factors on those pur-chases and then develops a model to predict expected future sales such that production numbers and mar-keting budgets can be built from those predictions. This approach to research is improved via “Research (2)” which adds consideration of the brand experi-ence influences on the outcomes in the purchase path, improving the understanding of the variables that influence “purchase or not” and thus improving the accuracy of forecasts and the marketing mix models built against those forecasts.

The branches of research shown in the arrows marked “Research (3)” give us improved understand-ing of our customer segments in demographic, psychographic, attitudinal and behavioral terms, and

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provide customer insights on which our marketing communications and service delivery should be built. To be most effective, this research combines qualita-tive and quantitative methods, and delivers results that drive the accuracy and applicability of the more quantitative research approaches discussed above.

Finally we come to your reason for picking up this book: analytics. As seen in Figure 1.4, analytics is rooted in the understanding of each individual, their influences and their circumstances, and runs parallel to the path to purchase through the status of the pur-chase decision and then around into the formation of insights and tactics (e.g. spend) that influence the next set of brand experience inputs to the path to purchase/CDJ. Importantly, whereas research is typi-cally conducted in a batch process against large sam-ples, this analytics process is conducted in a streaming, real-time timeframe on a case-by-case basis for each individual within the path to purchase/CDJ. This characteristic is what allows the output of analytics to drive real-time marketing decisions such as real-time bidding on media, real-time content customization, and cross-channel marketing automation coordina-tion. In the digital environment, each time a touch-point is engaged by a potential customer, the analytics function should have algorithms in place that adjust the propensity for, or the probability of, a purchase by that individual, and that determines what to do next in marketing to this individual given this new probability for purchase.

This summary of the function and objective of digital analytics provides us with the launching point for the remainder of this book. As mentioned, the subsequent chapters will help you to understand the fundamental

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components and steps required to build effective data-driven experience delivery, beginning with the common current state within marketing analytics, and finishing with the expected near-term future state in programmatic experience delivery, and the influence this may have on businesses and cultures.

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21


Strategy, Technology, Science & Art

The organizational discipline that defines the collection and integration of data, effectively translates that data into useful information, and applies that information to optimizing marketing delivery in a variety of forms is most clearly thought of as “Marketing Science”. As is apparent from this prior definition, data and technology reside at the core of marketing science. While this data can be derived from measurement in digital channels, it can also be accumulated through more traditional market research methods. In fact, to remain relevant in the 21st century, corporate market research functions are finding it necessary to build on their traditional market research data sources and capabilities to better understand digital consumers using digitally collected data. On the continuum of this evolution, what distinguishes marketing science for digital marketing from traditional research functions is that marketing science not only understands digital data and technology, but is able to strategically and tactically guide the organization in the application of insights from this data through both content and media technologies as the core driver in delivering relevance in the digital user experience.

ChapterTWO

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Marketing Science: The collection, aggregation and application of business data and consumer insights to optimize customer experience and business results through marketing technology.

Thus, the Marketing Science position in the delivery of digital marketing is a hybrid of several traditional specialized roles. First and foremost, there is no sense in developing digital marketing strategy without insights from data, and there is no sense trying to develop insights from data without an understanding of the business and marketing strategy these insights will serve. Accordingly, the Marketing Science Analyst is truly the next generation of what has traditionally been considered the Digital Strategist role.

As McLuhan showed us in Chapter 1, the medium is the message. Thus, effective delivery of a message in digital marketing requires not just an understanding of the vast proliferation of digital media technologies (or digital touchpoints), but also an understanding of how information can be applied to optimize the effectiveness of message delivery through these technologies. Put more succinctly, digital marketing tech-nology is information technology. Thus, in guiding the application of data to digital experience, the Marketing Science Analyst must be an applied information technologist and data scientist:

1. Understanding marketing technologies and digital media touchpoints’ code enough to ensure that the right data is being collected from each digital touchpoint;

2. Understanding data structures and sales/marketing IT systems enough to ensure that all of this data is being integrated together for crosschannel analysis;

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3. Being capable of defining and/or developing explanatory/predictive models from data that can be applied programmatically (e.g. algorithmically) to the delivery of digital experiences; and

4. Defining how to apply analytics from streaming data to optimize the delivery and performance of these digital experiences for each visitor across channels.

Thus, the Marketing Science Analyst must be able to move between and translate across marketing strategy, information technology and data science. But their work is not done yet. While, strategy, information technology and data science are critical to facilitating the delivery of messages to the right people in the right touchpoints at the right time, there is one more fundamental factor to every communication through every touchpoint: the right message. Here is where the art of marketing science comes to play. The data, analytics and algorithmic delivery of content will never be optimized if the content itself is not effective. Thus, the data, insights and strategy developed by the Marketing Science Analyst must also be brought to bear in better understanding the people who are engaging with our brand through digital (and other) touchpoints, and in artfully designing content and user experience (UX) in accordance with that deep and detailed understanding.

A simple process for the delivery of digital experiences from the perspective of the Marketing Science Analyst is laid out in Figure 2.1. Beginning in the lower left section, the collection of data is the core of our marketing science/digital analytics capabilities. As has been true in computer science since the first card was punched: what you put in determines what

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you get out. In digital marketing, the collection of data happens through software code. Every analyst should desire to verify the quality of the source data they will use to develop insights. In digital marketing, this means looking into code, and being able to explain your requirements for data collection from a touchpoint experience to the software developers who are building that experience.

With data flowing into the experience delivery process, the Marketing Science Analyst can begin to apply their strategy, science and art. The bottom right section represents the “design and development” stage of a digital experience. Here, the analyst is exercising their data science, strategic and creative capabilities to:

1. Discover and explain what makes an engagement effective;

2. Define strategies around those explanations and determine how the effectiveness of delivery against these strategies will be measured;

3. Build models and algorithms that will allow the optimal delivery of those strategies; and

Figure 2.1 Experience

Delivery Cycle

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4. Work with the content and experience design creative teams to ensure that the experience is built around these effectiveness insights.

With the experience built and put into production, the attention of the Marketing Science Analyst then shifts to the top section. Here, the analyst measures the effectiveness of the delivery and guides the technical and creative optimization of that delivery. This can be done through testing and experimentation and through quantitative programmatic optimization. With performance results being returned and optimization efforts underway, the Marketing Science Analyst returns to the first step in the next round of design, asking what data from the existing delivery can be used to optimize the next round, and defining how to collect needed information that is not currently available.

Since we start and end this process with the collection of data, let’s first consider the sources for this data from across the organization. While brands have become careful to ensure consistent branding across their traditional and digital channels, their digital marketing practices are still structured primarily by channel and function (with a paid media team, a social media team and a web team for example). Such a structure typically fails to create a truly integrated and relevant experience as a consumer moves across digital channels. The capability to capture and use data from any consumer’s digital engagement, and the growing expectation for content personalization that consumers have as a result (beginning with their Amazon and Netflix experiences), means that the disconnection of data across channels will be felt by the user and will impact their

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relationship with the brand. Thus, to secure brands’ relationships with digitally embedded consumers, Marketing Science must first collect and connect the systems (and their corresponding data) that are shown in Figure 2.2.

The gaps between each of the boxes in Figure 2.2 reflect the siloed or segmented nature of digital marketing practice — and accordingly the data generated from that practice — which Marketing Science must help brands overcome.

2.1 Paid, Earned, or Owned Breakdown

In the center of this Digital Data Stack are the communications channels through which digital engagement occurs. At the top are the “owned media” channels, which have been the primary focus of Marketing Science for decades. Digital analytics in marketing began with web tagging, and in many cases this is still the primary analytics competency within marketing or the agencies producing a brand’s owned channel marketing. Chapter 3 will focus on the owned media channel — and data collection, connection and application in that

Figure 2.2 The Marketing

Science Stack

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channel — beginning with the website but also extending to web services, email and mobile apps.

Next come the “Earned Media” channels, which are the focus of Chapter Four. A brand’s engagement with consumers through earned media occurs through search and social media, and Marketing Science for earned media includes SEO analytics, social media content tagging, social engagement analytics and social media listening.

Last are the “Paid Media” channels, which typically command the majority of a digital marketer’s spend across. The major paid media outlets include search marketing and paid display advertising, but social media advertising is increasingly adding options (and complexity) for media planners. Paid media has seen the earliest and most widespread application of analytics to content delivery through “retargeting” which analyzes a user’s past online behaviors to identify the best advertising opportunity, through social content network analysis, which allows social networks to use insights about the interests of a user and their social network connections to increase the relevance of advertisements, and through “programmatic buying” which uses business rules and realtime behavioral feedback to make a splitsecond decision on whether or not to buy an impression based on that particular user’s history and characteristics.

Our owned, earned and paid marketing communications channels are surrounded by additional data sources. On the left side of the document we have first party sources of data that should be integrated with the data drawn from our Marketing Communications channels to increase the contextual relevance of digital

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engagement at any point in time. Customer service records, sales data and survey responses should all support more traditional consumer insights research and usability analysis to increase the relevance of the experience being delivered to the digital user.

On the right side we have third party data that should also be used to support predictive models that best match content with consumers and thus deliver the highest level of relevance within digital engagement. Data Management Platforms (DMPs) and Demand Side Platforms (DSPs) play a central role in retargeting and realtime buying by tying together users’ demographic characteristics with their exposure to and engagement with advertising across the web as well as anonymized information about online and offline spending.

So, with the necessary background now established and with a model of the various pieces of media that need to be connected together as our foundation for moving forward, we are now ready to explore how to collect and connect data to drive relevant digital engagement for your brand.

The Challenge to “Collect & Connect”

The Latin root of the word “data” means “a given or a thing”, and that is really what data is, a collection of “givens”. Data is the source from which information can be created, but it is not quite information in itself. The transition of a set of givens from data to information requires analysis. Thinking in terms of a coin flip, data is the possession of a coin with one side up, and information is the recognition of which side that is.

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So, in thinking of data, we are thinking of what givens we can collect with which to generate information, insights and action. As analysts, every insight we generate and action we recommend is grounded in our data, so every analyst must accept accountability for their data as the most fundamental aspect of their job. In the current state of digital analytics, this is a big job.

As discussed in the preceding section, the sources of marketing data are widely dispersed, and the first challenge of marketing science is to collect and connect these sources. As foreshadowed previously, there are three fundamental challenges in the collection and connection of digital marketing data:

1. The data is scattered across platforms and communication channels;

2. With access constrained by organizational silos; and 3. It is of questionable quality as its generation has

largely been an afterthought.

Consider how a banking customer could engage with their bank through multiple channels in a single day. They may check a balance on their cellphone, then make a quick transfer from their laptop. They may later receive an email for a credit card product which they click through to view before calling from their cell phone for more information. When they ask to then check something about their bank account, they need to be transferred.

In this example, the information about this one user (with an existing customer in the CRM database) has been collected (1) as a mobile visit with one unique visitor ID, (2) as a laptop visitor with a different unique

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visitor ID, (3) as a “click through” in the email database, which may or may not share the CRM system ID, (4) as a second mobile visit referred from email, and (5) as a call to the call center, which may or may not wind up being associated with the email that was sent, and (6) a transfer to a different call center.

If the data above is not correctly integrated, this one person could appear to the organization as five different people, based on the disconnection of the touchpoints within a single experience. The result of such a situation is that the experience of engaging with this bank will often feel duplicative, redundant and/or disjointed versus contextually relevant, quick and easy with continual anticipation of the visitor’s needs. The latter is the ideal state of digital engagement, but it is not possible when data is scattered across the channels in which it is collected.

This issue proliferates when product lines across the bank (e.g. banking, insurance, credit, wealth management) each maintain their own marketing lists and use their own marketing platforms (e.g. landing pages, display ad purchases, email lists) against their own internal objectives (sell, sell, sell). In this case, customers will not only feel the frustration of the lack of contextual relevance and disconnection when trying to engage across multiple platforms, but they will actually begin to have the sense that they are not really a unique and valued customer whom the bank is there to help. Instead they may begin to see themselves as just a target source of revenue whose purpose is to help the bank make more money. When a party in a relationship feels used, that usually does not bode well for the relationship, and this holds true in marketing. When multiple

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business lines within the same organization are all competing for the same customers without coordination — which is dangerously easy to do with digital marketing technologies — they run the risk of alienating those customers through a lack of relevance and consideration of their needs in the context of each customer’s interest in engaging with the bank, and their desired form of interaction with the bank.

So, in this scenario — which is common across most organizations and not just this hypothetical bank — the Marketing Science Analyst has to identify and somehow integrate the sources of behavioral data about each customer; their interactions over time around a common goal and across multiple channels. Creating common recognition of a single person across multiple channels will require the help of the IT team and business lines, as data will need to be passed from system to system, and many of those systems, and the data within them, will be maintained under separate budgets by various lines of business. Should the analyst be able to align the technological and organizational stars to make this happen, they will have the foundation on which to assess digital experiences and develop optimization strategies based on a clearer understanding of the user’s experience across the whole digital ecosystem. But the third foundational challenge suggests that they may not yet have the data to do so.

The unfortunate state of marketing analytics today is that most digital data has been generated as an afterthought in the development of a site or campaign, or even as ‘exhaust’ without any thought at all. If the data collection aspect of the deployment of a digital experience was not as carefully designed and

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considered as the creative and technology aspects, it is likely that data collection was instead tacked on at the last minute and in a very haphazard way designed to get the basic “count and amount” variable and not much else. For an analyst hoping to explain the performance of digital marketing and find a basis for recommending optimization opportunities and predict the outcomes of such optimization, there is typically not enough data to work with given the lack of thought that had previously been put into the generation and collection of behavioral data. Sometimes, the rush to tag a digital experience before it goes live leads to mistakes, yielding data that is wrong. In these cases, the production of reports from this data is worse for the business than simply not having any analysis.

Starting with typically one or more of these issues around data at play, and with responsibility for the data that drives analysis in their hands, the Marketing Science Analyst clearly has their work cut out for them in building effective analytics to guide optimal experience delivery.

2.2 The Changing Nature of Marketing Data

The process of overcoming data collection challenges and building an accurate and comprehensive data repository for digital marketing analytics begins with an understanding of the types of data and the ways in which data can be generated and applied.

A wellknown description of Big Data is built around the factors of “Volume, Velocity and Variety”, with

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Big Data being big because it possesses one or more of these qualities; it comes in large volumes, it comes quickly and/or it comes from many sources.

Historically, the most common data used for marketing is market research, channel performance data, sales data, and prospect and customer records. From a volume standpoint, the data from market research is typically small data drawn from a sample. Channel performance data, sales data and customer data can be larger data sets, but do not come near the size of the behavioral data sets collected from digital engagement.

So marketing has some adjustment to make to processing the volume of data that can be applied to drive and optimize digital engagement delivery, and with the perpetual increase in data arising from constant connectivity and wearable sensors, the volume of data available to marketing and these required adjustments will increase. But ultimately, the question of volume is really just a technical problem of storage and retrieval. The bigger challenges in the new data paradigm for digital marketing come from the velocity and variety of data that can be applied to optimize digital marketing.

Velocity

As long as data and some sort of analysis has been used in marketing (approximately for the past 50 years), it has been collected and delivered periodically, in batches. Traditional market research involves the fielding of surveys or focus groups or ethnographic observation. These methods involve a

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period of collection, then the collection ends and a batch of data is delivered for analysis.

With digital analytics, the collection of data is continuous. Rather than being delivered in periodic batches, the data instead flows to the analyst in an endless stream. This ‘streaming’ data challenges the insight generating mechanisms of traditional market research that were built for processing batches of data delivered periodically. Most of the traditional models are still valid — cluster analysis for segmentation correlation and multivariate regression for prediction — but the speed in which the data requires them to be applied has made the problem of analysis much more complex.

This complexity is solved with the application of computation to process the constant stream of data. Computation not only offers quicker application of traditional models, but also introduces new and perhaps better models for analysis depending on the question. Several of these evolving approaches to marketing analysis through machine learning will be introduced in Chapter 8. Needless to say, adjustment to the rapid acceleration in the velocity of data and the shift in analytic methods required to process that continuous stream (and volume) of data requires more than just minor adjustments in the practice of marketing. These changes are actually precipitating a paradigm shift in the notion of how market research and consumer insights should be applied to drive the delivery of marketing messages under the traditional paradigm, or put more appropriately for the new paradigm; to guide engagement with the brand’s relevant stakeholders to optimally serve the objectives of all involved parties. This paradigm shift

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in what analyticsdriven experience design can and should be able to accomplish is already evolving and transforming the discipline of marketing from its 20th century origins as an outbounddirected message broadcasting mechanism into a more organic feedback system with capability to continually learn from its inputs and adapt appropriately to increasingly broad arrays of context.

Variety

These broad arrays of context constitute the third “V” — variety — in our Big Data feature set. The data accessible to the marketing scientist has become more increasingly variable in two forms: the method of collection and the composition of the data (which is often determined by the first variable).

As mentioned above, the traditional batch collection of data is conducted either directly or by intermediaries as surveys or focus groups. These methods collect data from samples of a population at a fixed point in time. The insights from this sample at this fixed point in time are then extrapolated onto the population and considered applicable to the present moment. While this method can generate very good “directional” insights, as a sample taken at a point in time from a fixed position, it is much like recording a concert performance on a smartphone camera, you wind up with only a partial representation of what actually happened over the course of the concert experience. Furthermore, where you point your camera determines what ultimately ‘represents’ the event; so you have to hope you’re focusing your data collection on what’s actually important.

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The collection methods offered through digital analytics introduce the opportunity to take much larger samples (or even observe entire populations) more frequently (or even continually). But the nature of variety in digital data collection comes not only from the increased possibilities for sampling, but from the type of data collection through that sample.

Survey and focusgroup research deliver data which is primarily selfreported, meaning that participants’ reporting of certain perceptions and behaviors reflect what they believe (at that time) they think and/or do around the question at hand, which due to wellknown cognitive biases, filters and ulterior motives may or may not actually represent their actually exhibited perceptions, motivations and behaviors.

One significant benefit of digital analytics to the building of predictive models is that a large part of the data collected is not selfreported (and thus potentially misleading), but is rather a direct recording of behavior, which gives the Marketing Science Analyst a much better factor for the prediction of subsequent behaviors of the observed individual and or of others who share similar characteristics within a statistical cluster.

Since the emergence of computerized business data in the 1990s, many market research teams have also made use of other sources of behavioral data that fall somewhere between batch and streaming in their nature. Transactional or Point of Sale (PoS) data is recorded and stored digitally, and is accessible for analysis. Historically, and particularly around physical retail, this data has been available to marketing only in batches, delayed by the process of importing from retail locations to a central repository, and

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analyzed only periodically by a marketing research cadence designed around batch data provision. Improved integration between retail nodes and the central data hub have reduced or eliminated lag in the velocity of this behavioral PoS data, which has become streaming for many retailers. The rise of Customer Relationship Management (CRM) in the early 2000’s introduced another potential stream of behavioral data into the marketing scientist’s toolkit, a stream which is becoming more adopted as a source of insight development and experience design, but has not yet been commonly integrated into the customer insights development function, though any firm wishing to compete on analytics must have the integration of data around digital behaviors (website, app, email, video, social and advertising), Point of Sale and CRM into their customer insights modeling dataset already underway.

2.3 The Fundamental Analytics Architecture: The Analytics Pyramid

There are technological challenges to collecting data that is increasing in volume, velocity and variety, but the challenge for the digital marketer is more fundamental; the determination of the data that should be collected in the first place and the ways in which it should be applied to experience delivery. These decisions will in turn determine the data management and experience deliver technologies required to achieve those results.

The first job of the marketing scientist is to determine their data requirements and communicate these through the organization. Without correctly defined data requirements, the marketing scientist

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will be working entirely or in part with incomplete and/or inaccurate data, limiting the capabilities of their analysis. Typically, any marketing scientist seeking to advance their efforts will find that they require more and better data to build more accurate models. In these cases, the marketing scientist’s ability to communicate and ultimately sell the need to spend time and energy on data collection become critical. This ability to mobilize the organization around the collection and integration of more and better data begins with the establishment of a common organization understanding of the applications of data to business objectives.

The Digital Analytics Pyramid has been useful in my consulting and teaching as an artifact around which this common understanding can be established. It has also proven to be a useful foundation on which to develop a more intentional and organized marketing science practice.

The Analytics Pyramid (Figure 2.3) segments the possible application of the insights derived from data

Figure 2.3 The Digital

Analytics Pyramid

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and analysis into four layers: (1) Descriptive, (2) Prescriptive, (3) Predictive and (4) Adaptive.

Descriptive Analytics

The foundational layer represents Descriptive analytics, and divides these into three subcategories.

1. Performance Analytics: This is the type of analysis most commonly envisioned when ‘analytics’ is discussed, since performance insights are what most reporting and dashboard tools have been designed to deliver. In performance analytics we find the “counts and amounts” related to Key Performance Indicators (KPIs) within digital channels; things like page visits, bounce rate, open rate, clickthrough rate, likes, follows, views and downloads.

Performance analytics tell us how we are doing in terms of outputs, but they do not tell us much about who is doing these things or the context in which the thing occurs, and so are less immediately applicable to an understanding of actual business outcomes. This is exacerbated by the fact that performance metrics only measure the positive occurrences of a certain output, so in and of themselves they tell us absolutely nothing about an equally important set of data, all the cases in which the behavior did not occur — which is the understanding we’ll need to improve performance. For that information we turn from the “what” and “how much” of performance analytics to the “who, where, when and why” of context analytics.

2. Context Analytics: Good reports and dashboards present their performance analytics in context,

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because without context we have no basis to act on the insights around performance. If performance analytics tell us some KPI is underperforming our expectations, we have no basis for designing a fix to this issue until we understand whether this is true for everyone or just certain types of people, whether it happens all the time or just at specific times, whether there is a pattern of behavior that appears before and/or after the occurrence (or equally important the nonoccurrence) that might indicate a possible point of intervention to increase the desired behavior. Thus, contextual analytics like segmentation and path analysis give us more clarity around performance.

Unfortunately, not all reports and dashboards are good reports and dashboards. In fact, the vast majority of descriptive analysis provided for marketing in organizations is provided as performance data without much context.

To a large degree this is due to the lack of descriptive data available around most KPIs. Digital analytics for marketing is still a very young discipline, and the collection of data from most channels has been implemented haphazardly, often as an afterthought, and often on a tiny budget. In part, this is due to the old paradigm of outbound mass marketing in which marketing leads did not understand how data could be used to guide their practice. Data was seen as simply a means for proving the success of what they designed through traditional market research, design and management approaches. Some data was better than no data, but all the data really needed to do was prove that marketing was working.

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Now, measuring whether marketing is working is not enough, especially when it does not appear to be working. Now, the reason that something works or doesn’t has become of great interest. Now, marketing leads are suddenly asking about the data they have accessible to explain the performance they are getting from across all of their marketing channels. And they’re finding that data doesn’t exist, so they’re turning to the readers of this book to bring it into being. Establishing contextual insights to explain performance is the first place to start.

3. Research: The third subsegment in descriptive analytics is the extension of marketing research into the field of digital data and analytics. Performance analytics rightly hold the prominent first position in digital analytics; if we are doing nothing else we should at least be measuring performance. The performance we observe is contextualized and better understood through the introduction of context like segmentation and pathing, but performance does not become explained (particularly in a statistically valid sense) until research methods are introduced.

The most basic type of research that should be conducted around digitally collected data is the development and maintenance of descriptive statistics around key performance indicators, particularly measures of centrality (mean, median and mode) and measures of dispersion (variance and standard deviation). These basic statistical measures allow us to define statistical norms around our performance variables, and are best when defined for multiple contexts, particularly

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segments. Segmentation itself around digitally collected variables falls into this research category, as does basic correlation to test the significance of various steps throughout the path analysis.

In short, the research category of descriptive analytics is the most advanced of the three in that it provides statistical rigor around our performance and context analysis. It is also the gateway to the next level of the analytics pyramid.

Prescriptive Analytics

The second layer of the analytics pyramid represents Prescriptive analytics. In this level we advance from observing and describing outcomes to taking a more experimental approach to data collection and application.

As we advance through the three categories of descriptive analytics we gain increasing insight into what works and what doesn’t work. In the research category we establish statistical rigor around those insights. However, even as we establish the parameters of performance against various contexts, we will likely find that our new insights yield as many questions as they do answers. At a minimum, we will want to know whether good performance can be made better through our understanding of the context around that performance, and whether those contextual drivers of good performance in one area can be applied to improve performance in another area with shared context.

Our descriptive research will also allow us to identify blindspots. This will be particularly apparent when

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we find ourselves developing an engagement strategy that involves trying something for which there is no prior example, and thus no prior measurement. For example, a new segment may develop, or a new channel for communication may emerge, or a new context for engagement may evolve. These new conditions will require the development of new strategies. In some cases, research should be conducted to understand more about the new situation, and this research may reveal similarities that allow us to use past performance data to guide the new strategy. However, given that we will be trying something that has not been tried before, our descriptive analytics are directional at best.

In today’s data rich environment, management often expects marketing scientists to provide an expected Return on Investment (ROI) prediction for a project as an input to the decision making process. To deliver this ROI forecast, marketing scientists must have two datapoints; investment and return. Developing an investment forecast in advance of delivery is simply the definition of the budget, a relatively straightforward exercise in any organization. The more difficult variable for this prediction is the “return” forecast. Management will ask the marketing scientist to look at the data for the answer, but of course, when the return is being generated by activity that has never been tried or measured, there will actually be no directly relevant data on which to base an estimate.

In such cases, the marketing scientist may make predictions based on data that seems similar to the case at hand and hope the basis for those predictions was sound. But as the name of this stage suggests, the better scenario for the truly datadriven marketing organization is to develop a set of prescriptions built

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on informed expert opinion — an array of options each with a range of possible outcomes. These probabilistic prescriptions for drivers of performance should be defined as hypotheses which may then be tested to develop evidence about the initial theories around drivers of performance for our emergent and previously unmeasured circumstances.

The datadriven organization is not willing to make a final decision from guesses or the gutinstinct of top decision makers. Unfortunately, many organizations do still operate this way, particularly when it comes to marketing decisions, though readers of this book can help to change that. Also unfortunately, organizations will often try to make the data they do have fit the solution they’ve already decided (by instinct) they want to pursue, regardless of the validity of that fit.

The benefit of testing and optimization and the prescriptive level of digital analytics is that it relieves management of the requirement to guess about the right approach, and thus run the risk of guessing wrong. The short average tenure of CMOs is the direct result of the consequence of being the chief decision maker in a discipline ruled by instinct, guesses and more recently, an effort to decrease guesswork by applying data about apples to problems about oranges.

CMOs and their senior managers need to admit when the data they have is not applicable to the problem they are trying to solve, then they need to generate that data through testing. The hypotheses they test may be developed from the data they have through descriptive analytics, but the data on which they ultimately base their decision will emerge from

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a carefully conducted approach to developing prescriptive analytics. The methods for this level of the analytics pyramid through testing and optimization will be discussed in Chapter 6.

Predictive Analytics

The insights we develop through our descriptive and prescriptive analytics will result in the emergence of “rules” around certain outcomes. For example, through observation and experimentation it may emerge that people in Segment A tend to move with higher success from the consideration to the purchase stage of their decision process after viewing a type of video, and that a reminder email to those who did not convert during the visit in which they watched the video can prompt a large number of those to return and convert. We may find that for those who have not given an email, a Facebook post showing the product they viewed is the right method for pro mpting a higher than average return for conversion. We may have tested various periods of time to wait before sending the email or Facebook post and established subsegmentation for (1) those for whom a reminder in 1–2 days is most effective, (2) those for whom a second reminder is effective, and (3) those who were apparently not as interested in converting as their view of the video suggested under our initial model.

The understanding of these rules in all of their specific contexts provides the basis for the development of predictive models. These rules may be applied as the training data set for planning and forecasting, offering relevant data to ROI forecasts and other predictive performance models. They may also be

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codified into the “programmatic” delivery of the marketing experience through emerging tools such as Demand Side Platforms (DSPs), Realtime Buying (RTB) of media, and Marketing Automation software. In each of these platforms, the general rule for the best approach to drive any user through to a conversion is established from the data and coded to be followed whenever a user matching the right criteria appears in the right circumstances.

Following our example above, if the system recognizes that a visitor to the landing page belongs to Segment A, it will programmatically fill a prominent spot on the page with the right kind of video. Our data collection should record if the video was watched, and whether the calltoaction (CTA) to convert was then followed. If the visitor did not convert in that session, the Marketing Automation system will begin the countdown to the followup contact, will identify whether the followup will be through email or Facebook, will send then appropriate followup at the appropriate time, will record whether the desired outcome occurred, and will proceed to the appropriate next step.

Everything that occurred above was based on a series of predictions about the best next step to drive to an outcome, and the basis for those predictions is the data collected through our descriptive and prescriptive processes. Through the technologies described, marketing scientist may now predefine a staggeringly large set of stepbystep rules to be followed under an equally large set of circumstances for any number of types of digital users.

The limitation on the application of predictive analytics to drive optimal marketing delivery is not one

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of models or technology — these exist. The limitations are with data. If marketers do not have sufficient contextual insight, research into segments and paths, and tested hypotheses about previously untried approaches, the predictions about what is best to do next in any given case will be developed from an incomplete understanding of the problem and all possible solutions, and as such, may predict a “best” path that misses better alternatives. Good predictive modeling requires contextualization of performance around the target objective, and good research into segmentation and the paths of those segments from point to point through their engagement with the brand.

Feeding historical data into a tool that will act autonomously against predictions it makes from that data can be a benefit to the business when the data is good and the predictions are accurate, or it can be an autopilot system that drives marketing results straight into the side of a mountain through a series of bad decisions built on the rules it established from the data it was given. In other words, marketing scientists should seek to build predictive, programmatic marketing around as many decisions as the data warrants, but the limits of the data should be well understood, and efforts to extend the limits of the data should be recognized as an ongoing pursuit. This pursuit involves a constant cycling of the results from predictive delivery back into the frontend of the descriptive process, to proceed through contextualization, research and testing to a new set of improved predictions. Much of this cycle will be conducted by the marketing science team. But increasingly, some of this cycle can be conducted without human intervention, through machine learning techniques that define our final level in the analytics pyramid: adaptive analytics.

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Adaptive Analytics

Adaptive analytics do exactly what the name implies — they transform descriptive and predictive data to information and allow the experience being delivered through digital channels to adapt to the best fit for any specific digital visitor in context based on that information.

Adaptive analytics are an extension of predictive analytics, with the chief distinction being the application of machine learning algorithms to drive the adaptive delivery of digital experiences. Machine learning algorithms start with the predictive models developed in the Predictive analytics stage, and with each new data point they automatically and continuously refine and diversify those models to deliver better predictions around ever increasingly specific contexts. With machine learning, the “rules” that guide predictions are not fixed into place until a new model is developed by an analyst. Instead, a computer is given the initial model as a starting point and is then set free to adjust that model to optimize some key objective it has been assigned to maximize.

When this more fluid and adaptive form of modeling is coupled with integrated data, common customer segmentation, strong content management capabilities, programmatic delivery capabilities in marketing touchpoints and sound marketing strategy, the stage is set for highly effective “realtime”, “personalized” and “1to1” marketing. However, the climb up the analytics pyramid is not an easy journey; it must be approached deliberately and systematically.

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49


The Applied Digital Analytics Playbook (ADAP) Part One

The Applied Digital Analytics Playbook (ADAP) is a template that guides a deliberate and systematic jour-ney up the analytics pyramid, and can serve as the core piece of documentation at the heart of every market-ing science initiative. The ADAP allows the marketing science analyst to clearly identify the problems that data can solve, to identify the existing and needed sources of data to solve those problems, and to delib-erately design the collection, organization and appli-cation of the required data to experience delivery and to the evaluation of the results of that delivery.

The ADAP is built around four sections: (1) Problem Definition, (2) Solution Definition, (3) Data Design and (4) Analytics Plan. These four sections together are designed to support communication and collabo-ration across multiple constituencies as both a strate-gic business communication and a marketing science engineering plan. This chapter will take a detailed look at the first two sections of the ADAP. Details around how to populate the last two sections (Part Two) will emerge as the subject of the remainder of the book, and will be addressed explicitly in Chapter 8. An ADAP template is available at this book’s counterpart website (www.architectingexperience.com).

ChapterTHREE

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3.1 ADAP Section One: Problem Definition

Business Problem/Business Case

Sample Business Problem

Competitive Travel Site has the largest market share as of the end of 2013, owning 42% of the Online Travel Agency (OTA) market. Our Company is the second largest player in the market with a 21% share. Competitor is growing at a much faster rate than Our Company, grow-ing by 18% in 2013 as compared to 2012, versus our 9% growth in the same period. Also, Competitor has a 3 year CAGR of 13% whereas Our Company has a 3 year CAGR of only 4%. To maintain or grow reve-nues, our Company needs to focus on providing a better offering than our competitor through understanding of our customers; improving sales to new users, getting them enrolled in our loyalty program, and increasing frequency and size of purchase.

The first section of the ADAP is focused on the defi-nition of the business problem, on making the case for spending the business’ time and money to solve the problem, and on documenting the measurable business objectives and key performance indicators associated with successfully fulfilling that business case.

The fundamental problem for any business is the ques-tion of how to increase profits. This problem typically spawns children such as ‘how to increase sales’ and grandchildren such as ‘how to increase repeat sales’, ‘how to decrease switching’, ‘how to win back past cus-tomers’ and ‘how to acquire new customers’. The busi-ness problem statement in the ADAP should articulate a problem that can be solved by the application of data to digital marketing experience delivery, and the busi-ness value that will come from solving this problem.

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Business Objectives & Key Performance Indicators

Once we have established clear documentation of the business problem and the benefit that will come from solving the problem, we can then turn our focus to the definition of solutions. Accordingly, the next section of the ADAP is devoted to the documentation of Objectives and Key Performance Indicators (KPIs) for the business in general and marketing in specific.

Objectives & Key Performance Indicators (KPIs)

Each of the problems defined in each of our problem areas should be given a matching objective. These objectives simply describe what we intend to accom-plish to resolve the associated problem. Objectives should be stated in terms that will allow them to be measured for performance. The measurement of performance against these objectives is conducted through Key Performance Indicators, or KPIs.

Considering the hypothetical problems defined in the previous section, our associated objectives and KPIs might look like the following:

Business Objective 1: Increase new customer acquisition.

KPIs: Total number of new visitors. New app down-loads. New visitor purchases and basket size (see Objective 2). Conversion rate of new customers to repeat (see Objective 3).

Business Objective 2: Increase basket size and value per sale.

KPIs: Number of items added to basket. Value of items added to basket.

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Business Objective 3: Increase repeat sales.

KPIs: Total number of returning visits. Returning visitor purchase recency, frequency and volume. Number of items purchased by loyalty members online. Number of items purchased with loyalty points, online and offline.

Marketing Objectives & Key Performance Indicators

Having defined the business problem, we can now define the marketing problem, which describes the engagement points that can be optimized in the inter-mediation between the business and the customer. The marketing problem reflects the challenges that arise in trying to solve the business problem by addressing customer problems.

In my experience developing marketing objectives with many different students and professionals, I have observed that marketing objectives are often framed around what the business wants to make peo-ple think or do. The way in which these problems are framed greatly influences the probability for the success in solving these problems.

Marketers should not believe they have the capabil-ity to ‘make’ anyone think or behave in certain ways, at least not for long. In human exchanges, ‘making’ a person behave or think in a desired way is typically achieved through one of two means: (1) intimidation or (2) manipulation. While few marketers that I’ve ever met would utilize intimidation as a tactic, more than a few have no issue considering manipulation to be a legitimate marketing tactic. However, these marketers may give themselves and their intellect a bit

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too much credit, and give their customers too little credit. The development of effective and relevant experiences in an environment of ever expanding choice cannot be sustained through either of these methods. While manipulation of feelings and self-perceptions has been a part of the marketing play-book through the 20th century and up to the present, the availability of multiple options and clear alternatives for most goods for most consumers in most developed markets along with an increasingly sophisticated understanding of media by consumers means there is less willingness amongst consumers to allow themselves to be easily manipulated by a brand’s message, particularly if their needs are not met in the delivery or consumption of the good. Messages, even manipulative messages, may still

Sample Marketing Objectives

Marketing Objective 1: Increase awareness of and enrollment in our Loyalty program.

KPIs: Loyalty program sign-ups conversion rate. Below or at target on cost per acquisition.

Marketing Objective 2: Guide new and existing enrollments toward increased sales, satisfaction and referrals.

KPIs: Pre-booking behaviors. Booking rates by new & existing loyalty members. Value of bookings. Loyalty customer CSAT survey. Loyalty customer call center/online support issue/complaint rate. Referred loyalty program sign-ups.

Marketing Objective 3: Work with IT to define and implement data needed to achieve marketing objectives one and two.

KPIs: increased sign-up, engagement and sales conversion perfor-mance and decreased complains from users receiving targeted and personalized content.

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work, but only if the experience of consumption of the product fulfills customer problems better than any alternative. Thus, delivering a relevant experi-ence and meeting customer needs remains the core objective of the marketer, and the marketing prob-lem should be tied to this objective.

You may have noticed that our objectives are not defined around specific digital media touch-points (e.g. web or mobile app), but are instead defined around the problems facing the business and the cus-tomer. Each KPI will typically be related to a specific touch-point, but the measurement of performance against an objective will almost always involve evalua-tion of multiple touch-points through multiple KPIs. Marketers often think of performance measurement by channel, with web reporting, email reporting, paid media reporting and social media reporting all broken out separately. This division by channel often aligns with the internal organizational structure developed around digital marketing, but it is completely unnatu-ral when considered in terms of how customers engage with our company through digital. Customers experi-ence our brand through a combination of overlapping touch-points, and they engage with those touch-points in an effort to resolve a customer problem or need. Thus, as shown in the sample, our KPIs should be built around needs, and our reporting should be developed from a multi-channel measurement around the points of engagement designed to address that need.

By explicitly documenting the problems we are try-ing to address from multiple perspectives, and by defining what success would look like and how it would be measured, we have now completed the first and perhaps most important step in preparing our

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organization to deliver good digital analytics. The next section of the ADAP begins to define how we plan to successfully meet those objectives, and how data and analytics is involved in that success.

3.2 ADAP Section Two: Solution Definition

The second section of the ADAP turns our focus from describing the problems we are going to solve (“what”) and the reasons we are going to solve them (“why”) to now define more specifically for “whom” we will be solving these problems, and “when” and “where” they will be solved. This is the section of the ADAP that is most closely aligned with the digital marketing strategy, user experience and design disci-plines. The alignment to these disciplines illustrates an important point with regard to the marketing sci-ence analyst’s role in the digital marketing design and development process.

The Integrated Marketing Communications (IMC) way of thinking places understanding the customers in the center of the marketing process, and that understanding of the customer is ultimately data about each customer. So it is really data that resides at the center of IMC thinking, and as shown in Figure 3.1, that data is coming from all directions, and has the capability to be applied throughout the entire marketing lifecycle.

At the top of the cycle depicted in Figure 3.1, the collection of data from behavior and context is driven into our central data repository. In the next step clockwise, this data is used to help determine the priorities for research and forecasting, and results from that research is added to the data repository.

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In the user experience delivery lifecycle, proceeding clockwise, strategy and planning draw from research and data to define the approach to be taken, from media mix planning to user experience strategy across those channels. The defined strategy is pre-sented to the next step of creative and user experi-ence design, where data about specific characteristics of customers is critically important to build relevant experiences that provide highly effective results. In the next step, data is applied to target and personalize the delivery of content and experience, and finally, the performance of that experience is measured, with that performance data defining the next stage areas for optimization, which will in turn determine new data collection and research needs to begin the cycle again.

The second section of the ADAP is where we explicitly design the solution to our defined problems by

Figure 3.1 The Data-

centered Cycle

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describing the digital experience we plan to deliver. As shown previously, the definition of user experi-ence must be guided by data. Therefore, the second section of the ADAP is designed to guide and docu-ment our thinking about the integration between data and user experience design and delivery.

While data and its application through segmenta-tion, prediction, targeting and personalization have become a part of more developed strategic planning and even made their way into the creative processes of user experience design by more advanced practi-tioners, for the most part, data in the form of statisti-cally sound segmentation and targeting models from digital behavioral data is still finding its way into the creative portion of experience design. Though the pace of the integration of digital behavioral data into the creative design process will continue to quicken as more and more capabilities for data in the design process continue to emerge, this second section of the ADAP recognizes that for the creative side of the marketing communications endeavor, the primary understanding of the end user/customer in our digi-tal experiences has been and will remain the cus-tomer persona — so section two of the ADAP begins with the development of a centralized and common articulation of those personas.

Personas Aligned to Customer Objectives

The ADAP’s section on personas is intended to clearly define the customers we are seeking to engage through our digital experience as we understand them through primary research and secondary data sources. Eventually, as data comes into the picture, these personas will be evaluated and refined in the

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context of various segmentation schemas. Whatever insight we have driving the definition of our perso-nas, their purpose remains to provide identities for customers through storytelling, using a narrative characterization and backstory for different seg-ments of customers. Personas have long been a cen-tral component of creative planning and more recently user experience strategy, because they allow experience design teams to feel like they ‘know’ the people for whom they are building an experience.

Personas are initially built from customer insights research, so they will only be as good, and helpful, as the research that has been conducted to understand customers. If the customer insights research and eventual digital data collection is designed primarily around demographics and transactions, then our personas will carry less power to create relevance around customer interests and objectives. Personas built on such limited data tend to paint pictures in broad strokes, such as the now classic “soccer mom” (e.g. A middle-class female aged 30–45 focused on her children. Makes online purchases from our category weekly.) Such broad-stroke description does not give user experience design teams much to work with when it comes to knowing and understanding the customer to ensure their experience is as relevant as possible in their specific context of engagement.

On the other hand, if our customer insight research and digital data collection is designed to provide insight into our customers interests, attitudes, behav-iors and responses, and we can place all of that in the context of different demographics and transactional groupings, then we have the right ingredients for comprehensive and fully-fleshed personas that will

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actually let us ‘know’ the people for whom we are building experiences through our marketing touch-points. Personas built from this more complete and complex understanding of customers will allow user experience teams to better understand the personal-ity and motivations of the people for whom they are designing experiences, which in turn will make for better experiences, and better outcomes.

In addition to the inclusion of meaningful attitudi-nal, motivational and behavioral insights built around meaningfully distinguished demographics and transactional segmentation, another important aspect of a strong personas is the integration of cus-tomer problems and objectives into the story.

Stacy, a wife of six years, mother of one young child and graphic design professional, lives in a trendy urban neigh-borhood in a condo she and her husband bought four years ago just in advance of her son’s birth. On work mornings, they all try to have breakfast together before she commutes with her son, dropping him off at school before going to work. She enjoys balancing her role at work with her role at home. Stacy’s job requires her to travel once or twice a month. When she has to travel, she is always looking for ways to minimize the time away from her family while still accomplishing her business needs.

Stacy is a frequent user of many loyalty programs, although she does not travel enough to achieve status on any given airline. The thing that really makes her travel okay is the opportunity to apply any reward miles she can use from flights or stays to trips with her family.

Though she occasionally uses our service to book, she also uses competitor sites as well as she hunts for the best timing on her travel. She is not yet enrolled in our loyalty program because she is not aware of it. She would be very interested in

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a loyalty program that makes personalized recommendations around her main interests in minimizing travel time and maximizing points. She does love being able to share great deals and interesting purchases through social media, where she also posts frequently about family, entertainment, health and wellness.

This persona will give the experience design team a clear understanding of Stacy, her motivations and interests, and her problems with the brand’s experi-ence. As a next step, the experience design team will now need to address the specific marketing prob-lems and marketing objectives that will connect the business objectives (create awareness and enroll-ment, create loyalty for referral, increase share of wallet, etc.) with Stacy’s interests, motivations and objectives.

Customer Problem

When business problems are considered only from the perspective of the business and its needs, the solutions tend to be one-dimensional and lacking an important perspective — an understanding of the customer problems and objectives that correspond with the business problem and objectives. If the business has low sales, if they’re losing customers, if they’re not gaining enough new customers; these are all influenced not just by what the business does, but equally importantly by how the customers respond to the business and the experiences it creates. In short, to solve its own business problems, a business must first solve customer problems and focus on meeting customer objectives. To accomplish this, it must understand those customer problems and customer objectives.

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Thus, in addition to documenting the business prob-lem and objectives, the ADAP also documents the customer problems and objectives related to the business problem.

Although the understanding of customers and their needs is central to the Integrated Marketing Commu-nication approach, a business’ primary focus on profits can make customer-centricity a difficult per-spective to maintain. Quite often, when I first ask clients or students to define customer problems, they instead describe the problems the business has in marketing to their customers versus the problems that customers have that the business can solve. The distinction between a business’ problem with its cus-tomers and the customers’ problems for the business is critical in that it separates strategies built around what the business wants from its customers (‘give us money’) versus strategies built around what the cus-tomer wants the business to solve (‘give us solutions that warrant us spending our money’). A typical initial response to the request to define a customer prob-lem would look something like this; ‘enrolled cus-tomers are not opening the loyalty program emails we are sending or responding to in-store loyalty pro-gram offers.’ Although this mentions the customer, that alone does not make it a customer problem; what it states is not the concern of the customer, but rather the concern of the business in engaging the cus-tomer. As such, the prior statement is a reasonable marketing (business) problem, but it is not a cus-tomer problem. A true customer problem statement would define the issues and concerns in the mind of the consumer. The business may or may not be able to solve these problems, but they will have no chance of building effective experiences for customers if they are not even aware of their issues and concerns.

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The Consumer Decision Journey Aligned to Touch-points

Sample Customer Problem

Customers who occasionally book with us haven’t identified sufficient incentive to enroll in our loyalty program, while many customers enrolled in the loyalty program do not have sufficient incentive to actively use/manage their accounts, and many may be frustrated by the limitations of the loyalty program’s digital experience. Specific issues include:

· Website experience is subpar. Very busy site, crowded with many irrelevant offers and not customized at all, even when logged in with loyalty number,

· Mobile App offers better personalized experience during travel, but it is not valued as expressed through low use vs. downloads.

Customer Objective 1: Be provided with a clear presentation of the information and options that matter to me.

KPIs: Decreased site/app search activity. Increased repeat utilization of site and app. Increased engagement with personalized/targeted content. Response to random satisfaction survey.

Customer Objective 2: Easily earn access and use points.

KPIs: Use of mobile app during travel to earn points with affiliate vendors. Use of website for account review/point redemption.

The problems and objectives for any given consumer will evolve throughout the experience leading up to purchase, so the mapping and documentation of these problems and objectives should be conducted against a process such as the Customer Decision Journey. As shown in the CDJ diagram (first appeared as Figure 1.2, here as Figure 3.2), we should collect data about each decision point in the customer

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decision journey for everyone who has passed through it to help understand what is effective and what can be improved for future visitors. I have not encoun-tered a company that does not have records of its sales, but I have encountered many who are missing key information on even the most rudimentary demo-graphic insights on who it is doing the buying. Even less common is the understanding of who is dropping out of the process before buying, and where and why they are dropping out. Data collection will help us understand all of these inputs to future strategic planning.

As mentioned previously, perhaps more important than detailed knowledge of the customer base around our successful sales (after all, we now have their money) is knowledge of the customer base around our lost sales. At this customer decision point in the journey, we need to understand that portion of the market that is choosing an option other than ours, we need to develop strong hypotheses about why that might be, and we need to test those hypotheses with

Figure 3.2 The Consumer

Decision Journey

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alternative forms of engagement targeted to those types of consumers. While trying to understand how to grow the share of market has long been a focus on Market Research teams, and trying to reach them the focus of marketing strategists, with digital data we now have a trail of context around those poten-tial customers who engaged with us but never pur-chased. Readers of this book should ensure that their organizations are integrating such data into market research activities, and should ensure that digital strategies include the development of hypoth-eses and alternatives aimed at improving conversion performance amongst those who had previously chosen another option.

The alignment of the experience journey to touch-points if often best conceptualized and expressed through an experience map, which aligns the design of the digital experience in terms of graphic presen-tation, copy and user experience with the touch-points assigned to each stage of the user experience for each unique set of customer segments or personas and their objectives in each stage of the journey. As we documented the decision journey for each segment as detailed above, we will have also defined the touch-points (e.g. digital ad, sponsored post, email, website, app, etc.) that will potentially be engaged throughout each step of the journey. With the experience map, we align each creative and user experience asset that will be delivered through the experience with a specific (or set of specific) cus-tomer segment/persona + stage + channel combina-tion. This methodological alignment of every component of the experience with a specific segment at a specific point in the journey helps marketing management ensure three benefits.

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Benefit One: That the initial investment in the pro-duction of creative assets is based around clearly defined and measura-ble objectives, with a clear hypothesis about why and how each creative/media mix approach will meet those objectives better than any alternative.

Creative design for marketing has always been the ‘sexy’ side of marketing, and the first thing people think about when it comes to advertising. In a new business pitch by an agency, it is typically the creative that wins the business, and throughout the industry, it is creative execution that wins agencies and brands awards and recognition. The Super Bowl has long been the premier example of brands spending huge amounts of money to try and one-up each other around outrageously ‘creative’ and entertaining advertisements with the primary objective of receiv-ing publicity and recognition for having produced a top Super Bowl ad, and without much regard for whether the ad delivered a better ROI than other marketing alternatives.

Somehow, the decision process around the approval for creative delivery — which composes one of the largest costs for marketing — remains one of the most subjective and arbitrary aspects of marketing. The CMO’s approval of a creative option versus alternatives is seldom a data-driven decision. Instead, this decision typically involves a dependence on the expert subjective opinion of in-house and agency strategists and creatives, along with the occasional dollop of personal preference. Often, the expert opinions upon which these decisions are made are motivated more by an interest in recognition and

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award winning (and of course agency’s billings) than they are on the relative potential for each option to deliver the greatest results for the lowest associated cost. In other words, the most sexy and ‘award wor-thy’ marketing tactics may not be the most effective for delivering relevance and optimal user experi-ence throughout the Customer Decision Journey. More importantly, most CMOs still have no way of proving this assertion as either true or false. But wouldn’t the CMO who had that type of data for decision making have a distinct advantage over com-petitors — not in the realm of vanity awards, but in the arena that matters to the business — in deliver-ing relevance to customers and reaping the results.

By requiring that each component of each creative recommendation be mapped to customer and mar-keting objectives for specific segments at specific stages of the CDJ, marketing management can ensure that their campaign and channel mix is addressing the right markets across all of the stages of the CDJ, and not just seeking broad public aware-ness of the ad (regardless of delivering resulting outcomes) or focusing generally on acquisition at the ‘top of funnel’ that is so typically the primary focus of marketing. Marketing management can also have a clear explanation of the basis for creative options with regards to their potential to deliver measurable results against defined objectives. While winning awards and publicity for creative may still be a valid marketing objective for some marketing efforts, marketing management can at least make explicit the portion of the budget that should be spent to achieve this goal, and can understand what type of results they should expect from the creative work and subsequent engagement by customers

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through the CDJ that will be generated from the remainder of their budget.

Benefit Two: That thanks to this explicit definition of objectives, we can effectively assign Key Performance Indicators (KPIs) to measure the performance of each crea-tive/content aspect of the digital experi-ence in delivering results against those creative design hypotheses and their corresponding performance objectives.

By enforcing the methodological alignment of every component of the digital marketing experience with a specific segment at a specific point in the journey, marketing management requires that the basis for creative and UX design decisions be based on an evaluation of outcomes and the formation of hypoth-eses about what will best deliver those outcomes (Benefit One). By enforcing the definition of meas-urable Key Performance Indicators against each of these objectives, marketing management is able to articulate what kind of results the company should be seeing from each channel, for each type of customer, at each stage of the customer decision journey. As we will discuss in details in subsequent chapters, this provides the foundation for cross-channel customer-centered performance measurement.

The generation of publicity and awareness through high-profile advertising is a fine marketing objective for any company that can afford it. The generation of leads or completion of purchases are also fine marketing objectives. With any marketing objective (and business objective, and customer objective), what we want most from our objectives is that they

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are explicitly defined and documented, and that they have an associated set of key performance indi-cators to guide their measurement. This book does not argue that every marketing activity should be measurable in terms of revenue and ROI, but it does unquestionably argue that every marketing activity should be measurable since ultimately, there can be no truly effective management without measure-ment. By requiring that creative decisions are based in clearly articulated hypotheses about what makes them effective in specific contexts, and by defining the KPIs that determine effectiveness, marketing managers are well on their way to being able to deliver not just optimal channel mixes, but also opti-mal content through those channels.

Benefit Three: Thanks to this measurement, we are able to continue and optimize crea-tive and UX designs that are work-ing, and stop expending budget and effort on those that aren’t working.

With all of the data available to marketers, there is absolutely no reason that hunches, guesses or opin-ions should ever be the primary basis for a market-ing decision. Not only is there data available to provide data-driven guidance or direction at the start of any decision making process, there is even more data available around the performance result-ing from prior decisions, and around the options available to either extend positive results or fix nega-tive results. The benefits of objectives-oriented crea-tive production ensure that the creative, content and overall user experience has been designed to deliver measurable results against clearly defined objectives. Because we know what to measure, and have

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expectations around the results, through real-time measurement we are able to identify issues with the experience as they arise, and we are able to quickly develop and apply new hypotheses about what will improve performance through methods which will be discussed in subsequent chapters.

ADAP Part One Summary

The first half of the Applied Digital Analytics Plan is focused on carefully and comprehensively articulat-ing the reason we are bothering to collect and ana-lyze marketing data. Without this important first step, we will not have a clear and common organiza-tional understanding of what we should be measur-ing and analyzing. Without this first step, we increase the likelihood that our approach to data collection will be haphazard, siloed and inconsistent across channels. Without this first step, we lack the vision of how data can be converted into insights and applied to contextualization, anticipation, targeting and per-sonalization of omni-channel experience for multi-ple segments/personas. This first half of the ADAP is where we are able to make a compelling business case for improved data collection and analysis, grounding that case in a discussion of the business objectives we can meet through our improved approach to marketing science and analytics.

Before discussing the second half of the ADAP, which focuses on design of our data and its reposito-ries and the specific ways in which we apply that data to experience delivery, the following several chapters will discuss the ideal application of marketing tech-nology for data collection, analysis and experience

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delivery for businesses of all sizes. As we leave this chapter, it is important to recognize that an organi-zation’s capability to reach the ideal applications of data and technology that you will read about will very much depend on the extent to which they have gone through the strategic thinking and collective articulation of customers and their objectives, digital experience vision and business and marketing goals as outlined in the first half of the ADAP.

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71


The Changing World of Owned Media

As discussed in Chapter 2, owned media are those channels in which a brand has complete control over access and content. Email was the first digital owned media channel to gain interest for marketing uses, and the corporate website — which quickly followed email in adoption — is still typically the most promi-nent owned media channel for brands. Web-based landing pages, online tools, mobile-optimized web sites and mobile apps are all owned media channels that can play an important role in a brand’s digital marketing strategy.

In fact, the increasing shift of digital media consump-tion toward mobile access and the prominence of social media is creating a challenge to the corporate website’s traditional role as the centerpoint of digital strategy. Designing for mobile access means rethink-ing the structure and presentation of content. And social media means a decentralization of the brand message in the digital ecosystem as consumer gener-ated content about the brand (e.g. ratings, reviews, tweets, check-ins, and posts) becomes more promi-nent and more importantly, more relevant (authentic) in the eyes of digital consumers.

ChapterFOUR

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This returns us to the central theme of digital analyt-ics for digital marketing; that relevance drives results. While owned media channels offer brands the strongest opportunity for driving business results, they also run the risk of being so oriented around obtaining business results that they overlook user objectives and user experience. Email is an excellent example of this problem; brands believe they have direct access to a customer through this channel, and use it in an effort to guide customers into fulfill-ing the businesses objectives. However, most digital consumers have too much email to deal with flowing through their inbox, and any message that doesn’t clearly stand out as relevant to fulfilling an interest is not only something to ignore, but is potentially an annoyance that erodes rather than builds the rela-tionship with the brand.

Corporate websites are not nearly as intrusive as email, and so avoid the risk of working at odds with their intended objectives. Websites are typically only accessed through intentional effort on the part of the user, which means the people who are there actually want to be there. In this context, the issues facing corporate websites are two-fold:

1. Not as many people are intentionally seeking out brand websites as review sites and social media content now also influences consideration.

2. Of those who do come to the website, it may not provide what they were looking for.

There’s not much that brands can do about issue one other than recognize this fact and adjust their digital marketing strategy appropriately (using tech-niques we will discuss in the beginnings of Chapter 6).

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This is simply a trend in digital media consumption arising from the increasing preference for digital content consumption through social channels and on mobile devices. And recognizing and adjusting to this fact is not a trivial effort; the implications of such an adjustment extend to adjusting budgets and strat-egies around the website, paid search, display adver-tising, email, social and mobile.

The second issue around delivering relevance through the website to those who have sought it out is something fully within the brand’s control, and web analytics will be the tool that delivers relevance.

The history of web measurement over the last two decades, which takes us from log file analysis on the earliest websites to today’s sophisticated analytics tools, shows that even though the development of web analytics technology has come a long way, it is still not yet a perfect science, and will likely undergo more changes as the role of the website and the nature of engagement with websites continues to evolve.

Understanding web measurement requires an under-standing of web architecture. Figure 4.1 is a general representation.

When the browser on our computer, tablet or phone gets directed to a website, the browser sends a request to the web server hosting that website. The web server receives and processes the request, and returns the code and content that defines that web-site as a result of processing the request.

4.1 Web Architecture & Web Data Collection

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Modern browsers like Google Chrome make it pos-sible to observe the responses from a web server being sent to a browser as a result of an HTTP request.

Figure 4.2 shows a partial list of the content sent as a result of navigating from the home page to a sub-page of a website. The content types sent from the server and received by the browser includes HTML defining the structure and content of the page, CSS (cascading style sheets) defining the look of the page, images, and JavaScript, a programming lan-guage which builds richer interactions into the page by allowing certain aspects of information process-ing to be conducted and stored in the browser (client) as opposed to needing to be processed through an HTTP request/response exchange.

The JavaScript content mentioned above deserves a moment of special attention because of the role it played in transforming web analytics. Prior to the introduction of JavaScript for the web, web analytics were built upon information collected on the back-end of each web transaction, in files called “weblogs”. Each time an HTTP response was sent, information

Figure 4.1 Web

Architecture

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The C

hanging World of O

wned M

edia 75

b2242 Architecting E

xperience: A M

arketing Science And D

igital Analytics H

andbook9”x6”

Figure 4.2 Chrome Network Tab

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about the request received and corresponding response provided were captured in these weblog files, and anyone wanting to analyze what type of transactions were happening on the site could pull up these weblogs to categorize and analyze the his-tory of HTTP requests and responses. In 1993, the first true web analytics software was launched to allow easier out of the box analysis and reporting on weblog data. That company, Webtrends, continues today as one of the web analytics software leaders, although they have lost ground to the undisputed champions of web analytics whose platforms are based on client-side JavaScript; Adobe’s Omniture and Google Analytics.

It was the richness in analytics that could be gained from collecting data on the client side that drove the shift from server-side data to JavaScript based client-side applications. Before JavaScript, browsers were relatively dumb clients; they could display what was sent to them in terms of HTML and images, but they didn’t do much else, so there was not much data to collect, nor was there any way to collect it.

The latter problem of the capability for data collec-tion was solved in 1995 when Netscape introduced their newly developed JavaScript language into their browser, and Microsoft adopted the language into Internet Explorer in 1996. This adoption by the two largest browsers suddenly made the client-side of the HTTP transaction a potentially much more interest-ing place for web developers, who could now make things happen for users inside their browsers, as opposed to on the web server. This was especially liberating in the low-bandwidth context of the early internet, where conducting programmatic exchanges

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with the server could potentially take minutes. By loading the program into the browser, sites could do things blazingly fast (in a relative sense), because the processing was being done on the client side.

The first application of client-side analytics was the soon ubiquitous hit-counter which proliferated across websites in the late 1990s. The hit counter used JavaScript to register a site visit and increment the total number of visitors to that page in a display on the website. While hit counters offered insight into relative traffic across different sites, they did nothing but provide a count, and so were not useful analytics in and of themselves. What was useful was the idea of capturing data about a visitor’s activity on the website using JavaScript, and it was this idea that in 1996 spawned a company named Omniture, which today, as a part of Adobe, stands as one of the leaders in the analytics space.

JavaScript was critical in establishing the JavaScript “web tag” which, through tools like Omniture, became the preferred approach for web analytics, but there was one other critical piece of the client-side infra-structure which made this possible; the now famous (or infamous) “cookie” protocol, which was also developed by Netscape, and actually preceded JavaScript as an aspect of the browser, having been integrated into the Netscape browser in 1994.

Cookies are simply small data packets which are sent over in an HTTP response and get stored on a user’s computer through storage associated with the browser application. This data packet can be identi-fied as being present or not on subsequent visits to the website that placed it (indicating prior visits),

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and can be queried by the server for other informa-tion it might contain. This other information that a cookie might contain is where these two browser features come together, because it is JavaScript that allows information about what someone is doing in their browser to be sent to a cookie on their machine. In essence, cookies created a space for data to be stored on a user’s machine for later retrieval by the web server, and JavaScript allowed programs to be run on the user’s machine that could send data into that storage space, all without the user ever knowing that part of their computer’s memory was being allocated to collecting informa-tion about them.

It was this stealth commandeering of web users’ com-puters functionality that caused an initial alarm about cookies to rise once people became aware of them around 1996, and that keeps them controver-sial to this day. For the purposes of this discussion, it is enough to note that the controversy over cookies has increased enough that alternative data collection methods are becoming important for organizations to consider.

It’s not just the cookie controversy however that should be causing companies to rethink their web analytics approach. There have always been data-fidelity issues inherent in client-side tracking, the most predominant being the inability for cookies and tags to recognize that several different people are sharing a browser. Web cookies are also only as good as their implementation — putting a cookie in the wrong place or structuring it in the wrong way can cause under-collection or over-collection of data.

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But perhaps the most significant limitation of client-side web analysis is the inability to trace a unique user across the multiple devices (work and home computers, tablet and phone) they are likely using to access the website and/or apps. Without help from a unique user ID that unites visits from multiple devices under a single visitor record, this one per-son’s series of visits will appear in the web analytics as several different visits from apparently different peo-ple. As we will see, this means that the true ‘journey’ of these customers from their first interaction through a desired conversion may not be captured correctly, as different steps in the journey are attrib-uted to different anonymous users.

4.2 Client-side Tagging

Of course, none of these issues mean client-side tag-ging is not a valid solution; it is and will remain one of the best methods available for collecting data around web traffic and behaviors. The issues and limitations mentioned above must be recognized and addressed by the web analyst as part of their web analytics methodology, but first, good data collection through client-side tagging should be established.

Software Solutions Overview

The major players by popularity and name recogni-tion in client-side web analytics are Adobe’s Site Catalyst (formerly Omniture, now part of the Adobe Marketing Cloud) and Google Analytics. Other strong and commonly used solutions include IBM’s Digital Analytics (formerly Coremetrics), and Webtrends.

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Adobe’s Marketing Cloud Web Analytics is also com-monly referred to as Omniture and/or Site Catalyst (which are respective the legacy company and appli-cation that were integrated into Adobe). Adobe’s web analytics offering continues to improve, and is a very powerful and versatile application. However, the primary take-away heard around Adobe’s offer-ing is the steep learning curve. For example, the out of the box implementation has been improved in recent years, but software coding skills are still required to make the most of an implementation. Also, more advanced data management will require the purchase of support hours from Adobe client services to establish VISTA rules (Visitor Identification, Segmentation and Transformation Architecture). The learning curve applies to report-ing as well. The out of the box dashboard gives standard descriptive metrics, and more advanced analysis of the data is available through the Ad Hoc Analysis and Data Workbench modules. While these tools also have a somewhat steep learning curve, once learned, they offer some of the best capabilities for cross-channel reporting and historical data analy-sis available in any measurement suite. Adobe’s fully integrated segment manager and testing platform, and their increasingly integrated social platform, round out this solution.

At the opposite end of the learning curve sits Google Analytics (GA). GA offers a simple tagging syntax and relatively intuitive dashboards and reporting that can focus on acquisition, behavior and goal completion (including ecommerce goals). With less technical know-how and/or client services support than is required by Adobe, GA also offers segmentation and attribution analytics. (In the free version these are based on statistically valid samples of the data.)

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Metrics from Google’s vast array of digital advertising offerings are readily available, and Google’s inte-grated “Experiments” module allows testing and tar-geting from this solution as well. GA also offers an Application Programmers Interface (API) allowing data to be retrieved from Google’s servers for use in external applications. While utilization of this API requires a very capable software engineer, it allows for historical data analysis and cross-channel analysis for those GA users who have such needs.

IBM’s Digital Analytics (formerly Coremetrics) web ana-lytics solution is receiving more attention in the web analytics selection processes, thanks in no small part to its direct integration with the popular IBM Unica, TeaLeaf and Silverpop applications, all of which have dropped those former names and become inte-grated aspects of IBM’s digital marketing suite. The analytics application is a solid solution in its own right, with a straightforward tagging syntax, built-in segmentation and very good pathing and heatmap analysis in the dashboards. While IBM for web analyt-ics is less well known than the two leaders mentioned above, their array of complementary marketing tech-nologies and established presence in most IT depart-ments makes them perhaps the most fully integrated solution with a suite of technologies that can drive comprehensive owned-channel marketing automa-tion. While this focus on owned-channel (web, mobile, email, CRM) integration and automation continues to evolve, IBM has traditionally given less focus to integration capabilities with channels such as social and paid media, although this may change with additional acquisitions.

Webtrends, the first technology to come to market with a dedicated web analytics platform, continues to

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perform as a leader in this space. Webtrends offers an open data architecture to clients, making web analyt-ics data accessible across the organization, and has built-in social and mobile measurement capabilities to align with web metrics. Webtrends’ technology spe-cializes in instantaneous analysis of in-session data, and correspondingly offers a strong array of conver-sion optimization capabilities built around segmen-tation, testing, and data-driven targeting. In addition, Webtrends understands the digital marketing lifecy-cle, and typically delivers its implementations in conjunction with services that align owned-channel data with email and paid-media retargeting. Webtrends’ services group also offers specialized approaches for retail, travel and finance, all of which makes them a favorite of ecommerce websites.

For the purposes of understanding how client-side tagging works “under the hood”, we will use GA as our platform since it is the only platform that will allow you to actually try everything explained here for free.

All of the client-side solutions mentioned share a similar architecture (Figure 4.3) for data collection:

Implementing Page Analytics

Figure 4.3 Web Analytics

JavaScript

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Lines 1 and 9 above are the opening and closing indicators to the webpage that JavaScript is begin-ning and ending. Lines 2 through 5 send informa-tion to the file on a google server listed at the end of line 5. Line 7 contains the GA ID and URL for your site, and line 8 tells GA to increment the “pageview” metric each time it receives this data (so on each view of a page containing this code).

One or more snippets of JavaScript reside on each web page. Another JavaScript file residing on a web server channels information collected from the page to data storage, which is typically hosted by the web analytics application.

The standard JavaScript “tag” for GA is shown below. This “tag” would be pasted in the <head> section on every page on the site. Typically this is accomplished by placing this once into a “header” module that is served to each page as it is rendered through a Content Management System (CMS).

1. <script>2. (function(i,s,o,g,r,a,m){i[‘GoogleAnalyticsObject’]=r

;i[r]=i[r]||function(){3. (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new

Date();a=s.createElement(o),4. m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.

parentNode.insertBefore(a,m)5. })(window,document,‘script’,’//www.google-analytics.

com/analytics.js’,‘ga’);6.7. ga(‘create’, ‘UA-10101010-0’, ‘imcanalytics.com’);8. ga(‘send’, ‘pageview’);9. </script>

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This introduction to web analytics will now touch briefly on two other types of “tags” that are com-monly used in web analytics, the “campaign” tag and the “event” tag.

The pageview tag discussed above will first be fired for each visitor as they enter our site. All web analytics platforms have the built-in capability to tell us some-thing about where that visitor is coming from. Did they click in from a search engine? Were they referred by another site? Did they enter from social media? From a digital ad? From email? Web analytics plat-forms will answer all of these with general regard to the “channel” from which the visitor is coming, and where possible, will provide the URL of the referring site. Standard metrics will also tell us the type of browser and device, the geography and network of the visitor, and other interesting distinguishing variables.

What we won’t know about many of the referring channels is whether our marketing efforts had something to do with the referral; that is unless we implement a campaign tracking tag. Knowing that someone came from email or a paid display ad is a start, but to have the data that will allow us to better understand our performance and continually opti-mize our strategy and delivery, we will need addi-tional context around that click. Was there specific content in the email that drove the click? Which digital advertisement brought in the pageview from paid media? These are the questions answered by a campaign tag.

A GA campaign tag is simply a string of code that is appended to the end of the URL that drives

Implementing More Detailed Data Collection

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people from the external source to the targeted web page.

In the code you see the URL to our site, then follow-ing the / a question mark preceding several utm codes. Each of these utm codes corresponds to a variable in GA reporting, which become populated with the data following each = sign (and preceding the next ampersand) when a visitor arrives at the site through this link.

We will discuss this tag in more detail in Section 4.4. For now, we can see that the tag above will tell me that the user who has arrived through this link was sourced from a loyalty program mailing via the email medium with a January update for our tech savvy customers. Every referring source that you’ve estab-lished as part of your marketing efforts can carry a unique code that allows you to 1) respond in real-time to the source of your visit and the context that provides, and 2) analyze your marketing efforts within and across channels through campaign seg-mented performance reporting.

While the code above told us about email, a different campaign code attached to a paid display campaign built around retargeting would send different varia-bles. For example, the next one tells us this visitor was sourced from an ad network. In the real world, we would ensure that we have programmatically received information on which ad network. We would see this was delivered through the Cost-per-Click (CPC) ad

http://www.yoururl.com/?utm_source=loyalty_letter_a&utm_medium=email&utm_content=1_2015_update&utm_campaign=tech_savvy

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medium and was delivered through our retargeting campaign.

Campaign tags are thus an important piece of data collection for giving us more granular data on how visitors are arriving on our site. Each analytics plat-form will have its own methods for formatting these tags which will be outlined in their document.

While campaign tags tell us something about what was happening before the visitor arrived, the next thing we’d like to understand is what is happening after the visitor arrives. For this we turn to event tags.

Event tags are JavaScript tags for data collection that are placed around a page in any place where a visitor might do something that we want to track. This JavaScript can be placed multiple times in-line with the event and fired on occurrence of the event, which is what we will see, or it can be placed one time in a “listening” mode which will dynamically collect and transmit relevant information when cer-tain pre-defined things happen on a page. This latter approach will require more help from a developer, but is the standard approach for complex pages and through suites like Adobe Web Analytics.

A simple GA (Universal) event tag is shown below:

www.imcanalytics.com/?utm_source=ad%20network& utm_medium=cpc&utm_campaign=retargeting

1. <a href=“../book” 2. onClick=“ga(send,‘event’,‘Engagement’,‘Link_

Click’,‘strategy’);”>3. See The Book</a>

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In line one of the code above we are simply giving the browser an HTML instruction (<a>) to create a hyperlink to the ‘book’ page of our website.

The second line of code above is where our JavaScript snippet kicks-in. It says that when a user clicks the link we’ve just created, we want to access the master GA Javascript from this page and tell it to track this event. We will pass along some data; the type of event was engagement (with content), the action taken was a link click, and the action happened in the ‘strat-egy’ content section of the page we were on.

The information passed into our web analytics from an event tag will allow us to group and sort reporting on our visits by each of the variables passed. We can compare different types of actions taken on our site. We can see which types of content drive certain actions. We can follow a trail of actions to see if it leads to our objectives for the site (such as placing an order). Each of these dimensions of analysis can be further evaluated through the data from our cam-paign tracking to tell us how different sources of acquisition to the site result in different events and outcomes. Finally we will want to add even more con-text through segmentation — the understanding of who these people are who are taking each of these actions.

4.3 Tagging Design & Deployment

As mentioned in Chapter 2, establishing good data collection takes time, planning and effort. Without these, data collection will be added as an after-thought, if at all. In most cases, poorly designed data collection is actually worse than having no data because it drives decisions from a false or faulty

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Because what we tag and measure on the website (as in any channel) should be based upon our objectives and KPIs, and will be implemented against a designed experience, the definition of a web tagging strategy begins with the completion of the first half of the ADAP.

The actual implementation of web analytics tagging is done by software developers, so the documenta-tion around our tagging will require clear communi-cation of our requirements. The ADAP will have defined the objectives and KPIs as well as the experi-ence map around those objectives, and by the time we are ready to document our tagging approach, the design team will have converted these into wire-frames of the user interface in the web or even com-prehensive designs (“comps”) that include the creative treatment that will compose the final design.

These wireframes or ‘comps’ of the planned experience provide the platform for our tagging documentation. Figure 4.4 shows an example of a wireframe.

The first step in our tagging documentation is to assign a unique ID, usually just a number, to each

sense of understanding. Ensuring that decisions are being made from good data requires investment, but it is an investment that will pay off immensely through your ability to make informed planning decisions, understand the value of outcomes, and optimize engagement.

Tagging Approach: Annotations & Tag Matrix

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element of the page that will receive a tag for data collection, beginning with the page itself, which will receive a primary “page view” tag. Once we have assigned numbers to each element we will tag, we will ensure the design process has maintained and delivered documentation around the objective(s) and KPI(s) associated with each element of the page, and we will need to add these when they have not been included in the experience designs, or are not clear. This documentation of KPIs will be used when we design our reporting around experience objectives.

With the elements of the page that will be tagged now identified, numbered and defined in terms of Objective and Key Performance Indicator (KPI), we begin assigning the actual tags to each element in a document called a “tagging matrix”. The exact syn-tax for tagging will depend on the web analytics platform we are using. However, regardless of the platform we are using, the information we convey to

Figure 4.4 Wireframe

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our technology team for tagging will remain the same. In this matrix, we need to convey the informa-tion we want sent from this page to our reporting system when the tag is triggered. In terms of the ADAP, this type of information flowing back from the experience to our database is called output, as it is the output generated from engagement with the experience.

A portion of a sample tagging matrix for GA (Universal) might look like Figure 4.5.

In Figure 4.5, each of the ID’s corresponds to a por-tion of the page on a wireframe or comp, so the developer can confirm that they are placing this tag, which is really just a snippet of code, in the right area. The second column in the figure above provides the tag itself. As mentioned, there are sev-eral ways that tags can be implemented. These GA event tags are configured for inclusion with a sim-ple “onClick” or other event command in the page’s HTML at the appropriate place. Check the documentation for your specific measurement platform in consultation with your tech team to

Figure 4.5 Tag Matrix

Excerpt

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determine the best way to implement tags for your experience.

Looking carefully at the table above, you will notice that portions of the tags are defined with sharp brackets “<>” around them. This is a method for notating portions of the tag that will be generated dynamically when then tag is fired. So looking at line 2 in Figure 4.5, we see that when this tag is fired through a user’s action on the webpage, the web analytics database will receive a record of the cate-gory in which we want to group this action (“deepen-ing” as the objective met), the action taken in terms of this deepening (a click on the “Things-to-Do” call to action), and a dynamic piece of information tell-ing us which particular suggested thing to do was clicked.

The tags we add to our webpage will likely not be limited to just our web analytics tags. For example, Paid Media solutions (media agencies, ad networks, exchanges and Demand Side Platforms) often utilize tags to help identify how specific paid media impres-sions are performing against page objectives. Marketing Automation solutions also utilize tags to support the tracking of users across platforms for personalized experience delivery.

The tagging matrix provides us with the right doc-ument to capture these additional tags in addition to our web analytics tags. We will document these tags in the same way we document web tags; by

Adding Other Tags

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Tag management systems provide us with a way to organize and manage all of the different tags we may be applying to an experience from one single place. Tag management involves the implementation of the tag management system’s tag on each page of the site. Once this tag is implemented, the tag man-agement system allows multiple unique tags from multiple systems to be loaded through this single page management system tag.

At its simplest, tag management is simply a call on each page to a separate JavaScript file containing instructions for all of the tags. For example, instead of placing the GA page tag on each page (likely through a reusable header template), we place it in a tag-management.js file which is called through that same header template, and thus dynamically populates each page with the GA tag. As shown in Figure 4.6, this simple manifesta-tion will reduce the clutter of individual tags on a page. More importantly, it allows us to add and modify tags on any page from a single location, without having to change anything about the rest of our site.

Examples of tag management systems include Google Tag Manager, Signal (formerly Bright Tag), Ensighten

associating them with an ID (either the entire page or an element/action on the page), and docu-menting the specific syntax that will be applied to the page by the developer responsible for apply-ing tags.

Tag Management

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and Tealium, among others. Adobe and IBM have tag management built into their solutions as well. Criteria for determining the right tag management tool for your needs include the number and type of tags you will be managing, the number of users who will be managing those tags and the way you want to manage permissions, the cross-channel capabilities you are seeking, an IT assessment of the ease of inte-gration with your existing systems and the level of support you need from the technology vendor.

Implementation and Validation

As mentioned several times above, it is likely that your tags will be implemented, either directly or via a tag manager, by someone with the technical skill to add them to the experience. Once the tags are implemented, it should be the responsibility of the analyst to validate that the tags are all ‘fir-ing’ correctly and are providing the desired

Figure 4.6 Tag

Con solidation/Tag Manager

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information about all of the experience actions defined to be measured. This means that tags should be evaluated in a quality assurance (QA) environment prior to being released to produc-tion. The need to have all tags evaluated through a QA environment with time to validate and fix as needed in turn requires that tags be implemented as planned and managed part of the development process. Unfortunately, tags are often instead added as an afterthought once the development is com-plete, and sometimes even after it has been moved into a live environment. This diminishes the plan-ning that goes into tags — delivering the bare mini-mum of page views in most cases, and leaving management pining for better analytics once the need for data about the experience is recognized. Rushing to get tags applied at the end of the deliv-ery process may also result in incorrectly tagged experience that fail to deliver data as planned, or that deliver incorrect data that actually misleads management in the decision making process. One of the worst examples of this I’ve witnessed was a page tag that fired each time a rotating banner on the page changed. This error in tagging massively influenced the number of pageviews being reported, and falsely decreased the “bounce rates” or exits from a single page view. The marketing manage-ment team working with data from this site was thus making decisions based on an entirely false view of their web traffic resulting from poorly imple-mented tagging. Following the ADAP process, developing a tagging matrix and allowing time to test tagging before the experience is deployed is the way to avoid such costly and significant mistakes in web tag implementation.

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There are two types of mobile owned media proper-ties — mobile websites and mobile applications, or apps. The mobile website can a site that has been designed specifically for the mobile experience, usu-ally as a subdomain of the main site. Increasingly, websites are being designed to be “responsive” to whatever device is being used by the visitor, which means that the same site that is served through a laptop browser is also served through a mobile phone, using pre-defined rules to configure itself correctly for the given screen-size.

The mobile application is different from a website. An app is a piece of software residing on the user’s device that does not need a connection to the inter-net to operate. So, while mobile website tagging can typically follow the exact same approach as the standard website tagging, app data collection requires some variation to account for the periods when the app might be in use, but is not connected and so is not able to send data about that use to an analytics system.

The major web analytics options listed previously all have specialized approaches for mobile app manage-ment, so the free GA solution is an option here too, as is the extension of any other web measurement solution. There are also mobile app specific options such as Mixpanel, Flurry and Localitics. All app measurement tools offer the ability to cache meas-urement data when users are active but offline. The app-specific measurement options mentioned above do not offer anything that is not available in the web analytics platforms, and are differentiated largely by

4.4 Mobile Marketing

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Email is the last major touch-point for our consid-eration within the owned digital media category. Email marketing was the earliest digital form of marketing on the web, since the capability to email existed before browsers, and early targeting tended to involve selection based simply on the fact that the recipient had an email account. As email accounts became common, email marketing prolif-erated so quickly and so broadly that email inboxes had to be fit with “spam” filters and junk mailboxes to deal with the influx of marketing efforts through email.

their dashboards, so they are typically the option for enterprises managing just an app. For enterprises managing mobile apps as an aspect of a larger inte-grated owned media mix, it makes sense to try and extend tagging through a single system across as much of that mix as possible. And because the only reporting we will be concerned with from an inte-grated standpoint is user-centric cross channel reporting, our interest in any system should be how easily we can avoid its channel-specific dashboards and access its data for inclusion in our centralized cross-channel reporting and analytics.

4.5 Email Marketing

Email Strategy

Of course, unsolicited junk mail through an inbox does not meet the relevance criteria discussed in this book, and is usually as appreciated as an unsolicited sales call in the middle of dinner. But the practice of

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email marketing is cheap and easy, lists that promise to be well-targeted are available for purchase, and due to inherent cognitive bias, few marketers think that their email will be considered junk. So despite the fact that an unwanted email is more likely to alienate the receiver than it is to persuade the receiver to take the desired action (if it is acknowl-edged at all), mass emailing is still a foundation of many marketing efforts.

So, email marketing can quickly become a waste of time and budget if the strategy behind it is not built around delivering relevance in context. But with the right strategy, it can also be a very effective part of an integrated marketing program. Since 2012, many platforms that had been dedicated to email market-ing have become integrated into larger Marketing Automation suites (e.g. Exact Target, Silverpop) with the recognition that an email with the right content sent at the right time in coordination with engage-ment through other channels can be an effective method to encourage desired conversion behaviors.

The textbook example of effective email marketing comes from the 2012 Obama presidential campaign. What made this email marketing program so effec-tive was the combination of recipient segmentation targeting and content testing, both of which were guided by ongoing data collection and analysis. These same three practices (segmentation, targeting and testing) are the key to optimization in all digital channels, but are perhaps most easily accomplished in email, meaning there is really no excuse to con-duct email marketing without applying these practices.

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Effective email marketing such as the type con-ducted by the Obama campaign focuses first on maintaining a good list of targets, meaning we are only going to email people for whom (1) email is an effective channel for engagement, and (2) an email from us will have relevance to them (if presented correctly). Because the Obama campaign’s email operation was focused around a single goal through a single email team, it was able to avoid the issue of multiple mailing across product-lines and/or over-saturation common to some large brands and to overly eager companies of all sizes.

These initial targets were then segmented by interest and behavior. Some recipients responded strongly to requests for volunteering, but were not campaign contributors, while others gave moment but did not volunteer. Many potential recipients had also at some point shared feedback on their interest in issues relevant to the campaign. This information on prior behavior and interest was used to determine groups who would receive certain emails, as well as the content of the emails themselves.

With clearly defined groups based on behavior and interest selected to receive differentiated messages, the team then placed significant effort into the design of that content. The Obama campaign team had a large team brainstorming X subject line and Call-to-Action (CTA) combinations each day. They would select the best options to test, and begin test-ing with small samples, eventually optimizing their email content based on the results of those tests.

Most businesses, even large businesses, do not have the resources to spend on this type of creative pro-cess around email. Most businesses also do not have

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as much to gain from effective email campaigns as a political campaign does. Organizations should define the effort they put into any channel by the potential returns they can gain through that chan-nel. Many organizations are still setting their sights too high around the expectations for email market-ing on its own. Email strategies should set realistic expectations around the results they can expect from email — recognizing that email is usually only welcome in specific contexts. With this in mind, they should try to define how those results are improved in concert with marketing engagement through other channels, and should include email marketing as a part of their orchestrated, multi-channel mar-keting plans designed for customer decision jour-neys, potentially applying marketing automation software to facilitate this orchestration.

Email Data Collection

The effectiveness of email can be measured by sev-eral KPIs. Effectiveness begins with the recipient opening the email. If an email is not opened, there is no way it can drive positive results. All email sys-tems measure email open rates as a core KPI. Some may also measure delivery (versus route to spam) and delete without open rates. These are all useful for pruning lists or evaluating response by different types of recipients to different subject lines.

Email management systems also measure clicks on links or other calls-to-action inside the email as a core KPI, and will provide dashboards and reports with counts of delivery, open and interaction instances. These metrics are useful for evaluating the email channel specific performance, but a more

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complete understanding of how email channel per-formance contributes to customer and business objectives is only available when metrics are col-lected across channels, allowing them to be linked within and across steps in the customer decision journey.

Marketing Automation tools like Rocket Fuel should be implemented with tags that link traffic coming from a click in an email to activity on the website. Such tracking can also be done for free using cam-paign tags in GA.

Remember the campaign tag; the additional infor-mation that is appended to the end of a URL, which are read when the URL is used to open a page? A GA campaign tags is structured as follows:

The syntax of this string is the standard HTML GET request syntax, with the beginning of the request demarcated with a ? after the website URL, and with each subsequent query string (name/value pair) beginning with a &.

The tag above is typical of a link that might be embedded in an email. This one would be embed-ded in a loyalty program email, as we see from read-ing the portion of the URL above immediately following the URL and ? symbol defining the utm_source to be equal to loyalty_letter_a. The

http://www.yoururl.com/?utm_source=loyalty_letter_a&utm_medium=email&utm_content=1_2015_update&utm_campaign=tech_savvy

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utm_medium variable tells us this click came from email. The utm_content variable tells us that the content of this email was the 1_2015_update, and we see that this was sent to our tech-savvy email segment, possibly as part of an A/B test on con-tent, since we see an a appended to the end of the source tag.

With all of this information included in the URL we embed into links and buttons in our email, we will now have a great deal of context about the different visits we get from our email campaign, including which versions of different email variations are driving more of specific segments into our site. This informa-tion allows us to understand the effectiveness of our efforts around channels and segments. In this way, it is very much like the ‘event’ feedback we will have from our web analytics. We can identify what type of content is getting engagement, and we can associate certain segments of users with our measured behaviors.

The next question then should be, ‘how can we use this information about what has driven engagement amongst a type of person to deliver more relevant content at each next interaction?’ With email, we have identified each person’s segment in order to send them the right content, so when they carry that segment value in to the site through their click URL, we will be able to associate all of the subsequent activity in that session with a visitor of that segment type. But how do we maintain that understanding if they visit again through another referring source which is not tagged and does not give us the same information, for example, by entering the URL directly. Alternatively, how do we avoid seeing this

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visitor as two different people if they revisit our site through the same link, but this time from their tab-let instead of their laptop, since the web analytics software will consider the different browser to be a different person? The information we can send through our link is limited, partly based on syntax and also significantly because for the most funda-mental privacy reasons, we don’t want to pass our customer’s personal information through URLs. We need a method to enhance information about the person coming to our site through a link. A very common and useful way in which this is achieved is through the use of ‘cookies’.

4.6 Introducing Cookies

Web cookies are perhaps the best known, least understood and most irrationally feared elements of digital analytics. They are also one of the most useful tools in the project of establishing relevance in digi-tal engagement, and — as anyone old enough to remember the flashing neon banners for stuff you’d never buy that were omnipresent in Web 1.0 can attest — for most people, the experience of using the Internet would be drastically less interesting and significantly more obnoxious without them.

In technical terms, a cookie is a little package of information that is placed into each visitor’s browser using instructions provided by the page. Cookies come in two common flavors: “first party” and “third party”.

First party cookies are pieces of data collected and accessed directly by the website you are visiting. The data collected through this cookie is specifically

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designed to improve the experience on that site, and only that site, and is typically related to that site. First party cookies will store information like your Member ID and other details that allow the site to ‘remember’ you and apply that information about you and your past activity when you return. Without first party cook-ies, the experience of using your favorite sites would be a little more tedious (log on every time), a little more repetitious (provide the same inputs every time, and across every device), and not nearly as personalized.

Third party cookies are the most common and most contentious type of cookie. These cookies are pieces of data collected and accessed by digital advertising networks and exchanges like DoubleClick and Yahoo!, and Data Management Platforms (DMPs) and data resellers like BlueKai and Datalogix. Third party cookies are used to track the type of sites you have visited, which is typically the source of privacy concerns related to third party cookies. These cook-ies essentially ‘watch’ your browsing behavior, and use what they learn for both general and specific targeting. This tracking is ‘anonymous’ in that no one with access to the data is able to see personally identifying information (PII) about you, such as your full name, social security number, credit card number, nor are they typically even 100% certain about your gender or age. Third party data compa-nies use what they observe in terms of browser activ-ity to essentially guess your age, gender, income and interests. They use these guesses, along with your location and the record of your activity, to help advertisers and other content providers either target you with new content they think will be relevant to your context, or re-target you with a ‘next-step’ piece of content related to a prior content exposure or

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engagement. This cookie-based targeting and retargeting will be discussed in detail in Chapter 6.

When data is collected through first party or third party cookies, the information is stored in the visitor’s browser. To retrieve this information from storage on the visitor’s computer, you have to know (1) the spe-cific name of the cookie, and (2) you have to have some way to interpret the content of the cookie. Your browser will allow you to see the cookies related to any site — in Chrome for example, navigate to www.amazon.com then use your right-click (PC) menu to select “View page info”. At the top of the resulting menu you will see cookies belonging to amazon.com (first-party cookies) and cookies belonging to ‘others’ (third-party cookies). This site will likely have over 20 cookies of each type. Select “Show cookies and site data” and you will see a list of cookies by owner. Look inside any of the folders to see the names of the cook-ies. The select any of the cookies to see the content. You will see that most content appears to be just a string of random characters. These are not random, but are encrypted so that not just anyone can make sense of the data contained in the cookie. The string may contain a unique ID representing you and/or other details about behaviors and characteristics, but without the decryption key, this will not make sense or be useable by anyone except the owner of the cookie. Additionally, the data in the cookie may sim-ply be just a unique ID meant to link your session at the time of a visit to data residing on a secure back end system. Without access to that back-end data, the unique ID provides no information.

Before we close the lid on the cookie jar for now, you might be asking yourself where the “second party” cookies have all gone. Consider what is meant by all

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of these parties in the transaction or exchange that is occurring each time a user visits a website. The first-party in the transaction is the site being visited. The third-parties are all entities that are not controlled by the site, but are ‘contracted’ by the site to indepen-dently collect and/or provide information. In a tradi-tional commercial definition, the second-party in a transaction would be the buyer, or the visitor. Of course, media likes to create its own definitions. In media, second-party data has come to mean a type of first-party data that is generated through a source beyond the site publisher, but nevertheless controlled or guided in some way by the publisher. While it is not the publisher’s domain on the cookie, the informa-tion collected has been shaped/customized by the publisher, versus third-party data which is defined by the third-party entity then provided to buyers as-is. Second party cookies and other second-party data only began to emerge after 2010 with the introduc-tion and evolution of intermediating technology like Marketing Automation tools and Demand-side Platforms (DSPs) residing between web publishers (first-parties) and web advertising networks and DMPs (third-parties).

4.7 Applying Owned Channel Metrics

With our tags and cookies in place, the question eve-ryone wants answered is ‘how can we use the owned channel data we’ve begun to collect to improve our business results?’ Thinking back to the analytics pyra-mid, you’ll recall several layers of analytics. In the first layer, the “Descriptive” layer, we conduct analysis for performance measurement, contextualization of per-formance and research support. These metrics may

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be evaluated in channel-specific terms, as they will be in each of the following chapters, and in ‘cross-channel’ or ‘omni-channel’ terms, as will be dis-cussed in the beginnings of Chapter 6.

The next levels of analytics were prescriptive and predictive. These layers of analytics should always be built across all relevant channels around the cus-tomer segments we can define from data. Our think-ing around how to conduct these levels of analytics and apply the results to digital strategy and tactics will also be deferred through the following chapters until we reach the topic of cross-channel Marketing Automation in Chapter 6.

Descriptive Owned Analytics

All of the web, mobile and email analytics platforms mentioned earlier in this chapter tend to provide comprehensive analytics reporting and dashboards for an understanding of channel performance, and most also offer the capability to develop good con-text around that performance and draw insights for research from the data, including the integration of testing capabilities that will be discussed in Chapter 6.

Email metrics tend to be very functionally focused around the key metrics of delete rate, open rate, un-subscription rate, and click-through rate. Beyond these, email is measured as a referral to other chan-nels within those channel’s dashboards vs. within an email report. So while we can see the open rate and click-through rate in our email metrics, our insight into what happened after that click-through will come from metrics in the channel that received the

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referral — which is typically a web landing page — as long as we tagged the click to pass along the right information into the receiving channel. Thus, the descriptive analytics in an email platform are typi-cally used to evaluate the response of different seg-ments of recipients to all of the content that has been sent to them, including to evaluate the results of content tests within a segment (e.g. testing the response to two different subject lines, or calls to action). In conjunction with the marketing automa-tion platforms that will be discussed in Chapter 6, email marketing platforms are beginning to use this descriptive response feedback to build prescriptive recommendations and predictive targeting algo-rithms into their platforms.

Most web and mobile dashboards provide some pre-defined views organized around types of visitors, sources of traffic, and the classic stages of the consumer funnel through which they pass in their consumer journey. GA has organized these as clearly as any of the standard platforms.

Conversion metrics are the backbone of our descrip-tive analytics, telling us whether or not our earned channels are converting our visitors to our key busi-ness and marketing objectives. Typically, the KPIs associated with our business objectives will be defined here as goals, and when possible, goals should be assigned financial value. Aside from the rate of user/visitor conversion to our KPIs within any given channel, conversion measurement may also include measurement of the contribution that other channels make in driving that conversion through attribution measurement that recognizes that our main or ‘macro’ conversions may be assisted

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by other channels for key ‘micro’ conversions through awareness and consideration stages leading up to the macro conversion.

Acquisition metrics deepen our understanding of the sources of traffic to our website or app. These metrics may feed performance measurement against acquisi-tion objectives set for traffic-driving channels like organic and paid search, email, social and display advertising, but it should be remembered that acqui-sition (e.g. click-through) is not an end in itself, it is simply an initial means to the end we desire for each visitor, conversion into our defined macro conver-sion. As measures of performance, acquisitions met-rics are the basis for setting and measuring channel performance optimization against conversion goals. Acquisition metrics also provide useful context to aid this optimization such as comparative campaign and keyword performance.

Audience metrics exist to provide context around the types of visitors, augmenting performance met-rics and supporting research in descriptive analytics, and are critical to the segmentation and targeting efforts that are central to prescriptive and predictive analytics. Audience metrics typically begin with straightforward demographic metrics of age, gen-der and geography, and technographic variables around browsers and devices used to access the site. All of Google Analytics’ audience metrics are built from what they know about site visitors based on the Google advertising cookies each of those visitors carries in their browser, and many other platforms also incorporate or can at least integrate with data from advertising cookies from multiple sources. When this link exists, audience data also includes

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summaries of the interests and market segments of site visitors, which can be used to develop segmenta-tion, guide content creation, and target the delivery of that content.

Behavior metrics will typically provide measures of marketing KPIs, and provide critical context around user’s interactions with the site, revealing patterns over time around behaviors leading up to conver-sion or non-conversion. These metrics largely fall into the “context” and “research” sections of the analytics pyramid, and are critical in allowing us to develop insights into what works, what doesn’t and form hypotheses around what might work better.

Behaviors that we measure on the site include pages viewed as the most basic measure, and the visitor’s interaction with pages as measured through the events we might have established, site search and the paths users take through the site.

Entry and exit pages are the most basic metrics of interest around pathing. Entry pages are typically driven by our acquisition efforts, while exit pages are based on alignment of the experience to the user’s needs. “Bounce Rate” is a commonly evalu-ated metric related to exit pages, measuring the cases where the exit page is the same as the entry page, and thus measuring the effectiveness of land-ing page content in encouraging deeper interaction with the site amongst the audience coming to that page.

The importance of considering context around metrics has been mentioned several times, and it is worth noting an example when evaluating bounce

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rate. When bounce rate is high, the immediate impulse with this metric is to conclude that the page has a problem, and that content on the page needs to be optimized for the audience. However, it should also be considered that if some or all of the audience being delivered to the page is not ‘qualified’ as potential converters, a high bounce rate may not be a reflection on content effectiveness, but could instead be a reflection on ineffective targeting and acquisition. As always, when evaluating underper-forming metrics, all realistically possible patterns of cause and effect to examine should be identified and then evaluated.

Continuing this example of establishing context around the reason for a high bounce rate, our first area for analysis might be to look for meaningful correlation between bounce rate and both traffic source and audience type (via segments). In most web analytics systems, from the free to the top tier paid solution, conducting such correlation will involve accessing the source data for some defined period and analyzing it in a tool separate from the web dashboard. In GA for example, this would require using the API to pull data on segments, sources and bounce rates into a CSV for analysis in the statistical tool of your choice. In the Adobe Marketing Suite for example, this can be done in the system as long as a user has access to higher tiers of the product capabilities.

For the purpose of understanding what we might analyze via correlation analysis (then test for causality with A/B testing before rolling out a solution), we might start with the “Behavior Flow” view in GA

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for a visual representation of the patterns we wish to analyze. The “Behavior Flow” view shown in Figure 4.7, is filtered on a very basic segment of “Returning Users” and grouped by “Traffic Type” as seen in the first column.

A similar view of paths through the site segmented and grouped by any combination of dimensions can be developed in all major web analytics tools, and the segments and groupings you are able to apply to the data are typically limited only by the limits of your measurement set-up and data collection approach.

This initial view gives us a good sense of which chan-nels are bringing this segment to our site, where this segment is landing through those channels, and what happens once they land (e.g. do they bounce

Figure 4.7 Google

Analytics Path Analysis

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or not), but it only works for a segment-by-segment analysis. From this we can have a sense of which pages are causing a particular segment to bounce, where those who do not bounce are going for subse-quent views, and where they are ultimately exiting after passing through the site. We might actually even use factor analysis to look for differences in characteristics between the portion of this segment that bounces and the portion that passes through to subsequent pages and perhaps eventual conversion, and use that sub-segmentation in subsequent causal testing.

So from this one view we have the capability to develop significant context around bounce rate (in comparison to non-bounce by channel per seg-ment), but to compare how different segments respond to the same page, we would have to turn to a table that takes away the time-series view of behav-ior and focuses on outcomes. Such a table would also give us the data we would need to develop correla-tions around segment, entry channel and bounce rate. If we identified differences between segments, we could then conduct causal experimentation around both the traffic from the source channel related to higher bounce, and the related landing pages to help focus on specific issues in either or both of these potential causes of bounce.

In summary, the website, the mobile app and email constitute the core of any organization’s owned media mix, and while the proportions of use for each of these may be changing as trends in digital engagement also change, some version of these three communications methods will continue to evolve. As people’s expectations for relevance in

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digital engagement continues to evolve along with these channels, so too do the requirements for data be applied effectively in establishing that relevance. As we learned in the previous chapter, establishing relevance that also delivers results begins with care-ful planning, and the ADAP can be an effective guide in that planning. In this chapter, we used the guidelines and requirements provided by the ADAP to begin the collection of data from our earned media channels, adding tagging documentation to the ADAP as we did. In the next chapter, we con-tinue our data collection efforts with a focus on the earned media engagement coming from SEO and Organic Social Media.

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115


Earned Media: Organic Social & SEO

We turn now to the ‘earned’ media channels in digital: organic social media and Search Engine Optimization (SEO). Earned media is exactly what it sounds like: the presentation of our brand in media through channels not owned by us, and without buying those exposures. Both social media and search engine exposures also have paid options in paid social media channel advertising and paid search engine marketing, so it is not correct to generally view the social media or search channels as specifically either organic or paid. This chapter looks at the organic, earned manifestations of marketing through social media and search engines, and the next chapter turns to the paid approach to both channels.

ChapterFIVE

5.1 History

The lesson behind the evolution of social media “research and analytics” since the dawn of Twitter is summarized nicely in a quote Nate Silver made about analytics for economics; ‘Improved technology did not cover for the lack of theoretical understanding about the economy; it only gave economists faster and more elaborate ways to mistake noise for a signal.’

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The first real corporate attention to social media emerged around some high-profile brand crises. As these were crisis communications problems, PR/commu nications was the natural organization to step in and address the crisis management strategy through social media channels.

This initial touch-point with social media prompted PR teams to propose movement into proactive attention to communications, particularly with a focus on “influ-encers” and communities, a logical extension of PR’s expertise in placing content with outlets that could generate impressions within relevant audiences.

The initial reactive crisis management objectives fueled a social media monitoring technology indus-try, which then quickly adopted the influencer idea to sell services supporting proactive social communi-cations. Today many existing social teams have grown from PR practices, and are striving to deliver value through proactive social engagement of vari-ous levels of influencers.

Around the same time, another natural extension of the organization into social media measurement emerged around customer support. In many organi-zations, the business value of investment in social communications were not immediately apparent (after all, how can you ever prove that a crisis was avoided?), so managers strove to find another driver of value, and the potential to address customer issues uncovered through social media was the most appar-ent candidate.

This emergence of this approach saw the overlap of call-centers and PR within these efforts, since a rea-sonable argument could be made that a visible public

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complaint was indeed a PR issue. This overlap, though often political and raucous for those involved, has helped to establish the need for the removal of organizational silos and a more holistic notion of cus-tomer experience, which seeks to generate engaged advocates for the brand through proactive experi-ence and brand building and responsive customer and relations.

As this first round of social media for business evolved, several established practices that would seem to have a natural fit with social media measure-ment — namely Customer Intelligence and Market Research, Direct Marketing, and Web Analytics — were nowhere to be found in this first round of aligning social media with business.

The hesitancy of these organizations to adopt the first round of social media measurement practices was likely rooted in a lack of fundamental under-standing of social media, the perception that it might be a ‘fad’, and ultimately the nature of the social media data available to them. While PR has long been accustomed to working from measures that at best extrapolate and estimate the potential impact of their work across broadly defined public constituen-cies based on volumes of impressions, the general audience served by an outlet, and sentiment of cover-age, these other disciplines are built around clearly modeled and structured data collection (a fired tag, a closed-ended survey response) and well-defined and validated segmentation of existing and potential consumers using demographic and behavioral data.

Thus, while the first generation of social media meas-urement allowed PR to gain more granular insight (and entry) into public conversations about the brand,

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it did so with what was typically a non-random sample of a company’s overall market, without verifi-able means of segmentation of the sample popula-tion, and with meta-data derived from qualitative tools like sentiment engines that were highly inac-curate — making Customer Insights skeptical about the value of the data.

Web analytics’ slow move to social media measure-ment was also rooted in the nature of the data avail-able — in essence, web analytics viewed social media as just one potential referrer amongst many, and for many large firms, certainly one of the least perform-ing channels compared to email marketing, organic and paid search, and online advertising.

In terms of understanding customer opinion and behavior, web analytics has been able to use cookies and site tagging to collect detailed information on site visitors’ behaviors on the site across the web. Through DMPs, the tracked behavioral data can be coupled with demographic and psychographic data to give insights around very specific segments of users who are known to be using our site. For the purposes of managing experiences on a website, available forms of social media measurement have had little applicability compared to this sort of data.

Increasingly though, web analytics functions are becoming the Marketing Science team’s focus at the center of the shift to ‘omni-channel’ analytics. From a content measurement standpoint, Marketing Science should approach social media monitoring and meas-urement with the same mindset it brings to advanced web analytics functions such as testing and optimiza-tion. As social media may be an important step on the path to conversion, web analysts must understand

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how to properly attribute social media to conversions, and must help guide the optimization of social media in that context.

The context measurement approach to social media measurement fits even more strongly with the strengths of web analytics. By drawing social graph data directly from primary and secondary social net-work APIs, analytics functions can use structured quantitative data to derive the types of objectives-oriented ratios and KPIs delivered through current digital analytics reporting and dashboards.

Web analytics has an established competency in building a data-driven management culture and pro-viding business insights from digital data. Thus, mar-keting science is a natural candidate to advance organizations from the raw (and dumb) volume or “count” metrics such as “Followers”, “Likes”, “Views” etc. to give managers integrated performance insights from ratios such as “comments/post”, “com-ments/page likes”, “links followed/re-tweet” and “conversions/social click-through”, which will be even more valuable when used in conjunction with the content analysis mentioned above.

Finally, as new solutions begin to align social media profile, content and relationship data with existing CRM databases, Marketing Science has a clear role in developing digital strategy and performance insights from these combined data-sets.

5.2 Organic vs. Paid Social Media

The development of data-driven omni-channel strate-gies requires an understanding of realistic objectives

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for each of those channels. As teams who have his-torically overseen the website have come to own the deployment and measurement of social media as the organization’s newly minted omni-channel digital team, the expectations for social media have been somewhat distorted. Because conversion to sales or some other macro-KPI is the central objective of the web team, the value of all other digital channels revolves around how well they assist conversion. This worked pretty well for a channel like email for exam-ple, which was basically an auxiliary owned media channel with the sole purpose of driving web visits, or for paid display which once again had the sole objective of driving web traffic. However, this same view does not apply as well to social media.

Fundamentally, what makes social media effective is the value of the content in the context of a social exchange. People will not engage with brands in social media if the only activity the brand takes in social media is a continued attempt to steer them to another channel where they are selling something. Unlike email and display media, where people are accustomed to clicking through to a webpage, any activity that starts in social media should be expected to stay in social media.

In organic social, the currency of value is certainly not impressions or click-throughs. The currency of value in social media is engagement, and more spe-cifically, engagement with people who are actually likely to purchase our product. Each time a qualified prospective or existing customer engages with us or talks about us in social media we are meeting the core objective of social media: being social. We are generating the most valuable type of awareness

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through peer referral, and we are creating affinity and deepening loyalty through our interactions.

If we’ve approached the design of our social media strategy with a Marketing Science mindset, then we’re also generating another type of value through each engagement — increasing our understanding of our customers and prospects. An understanding of who we’re communicating with is the most critical foundation for actually communicating effectively. Social media engagement strategies that don’t con-tinually incorporate insights about the interests and behaviors of the social audience will ultimately cease to drive engagement, so continually developing insights is as critical to an organic social media strat-egy as the basis for the creative copy and imagery that ultimately is sent through social channels.

Thus, just like our owned media categories, organic social media analytics require all three types of data from the first level of the digital analytics pyramid: performance data, context data, and research data.

5.3 Organic Social Media Strategy

If the core objective of social media is being social, the question for marketers then is; ‘what kind of business objectives and customer objectives can be achieved by being social?’ The answer to this depends very much on (1) the composition of the users in that medium, and (2) the nature of the particular social medium (hot or cold).

The first question asks which channels have users who look like the people we want to reach. Facebook

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and You Tube for example have a very broad demo-graphic of users that is becoming analogous to the types of audience that could previously be reached through television or a mainstream magazine. Twitter and LinkedIn also have diverse demographics in users, but these users expect to use these channels in ways that won’t work for every brand.

Outside of the big four as of this writing (Facebook, Twitter, YouTube and LinkedIn), the audiences of different networks (and there will be new ones emerging after this is written) tend to become skewed toward specific demographics and interests. For example, Google+ has an increasingly large and diverse base of users, but many of these are not truly active on the platform but are instead accounts that were established in order to use some other Google product. Thus, at the time of writing, the active users of Google+ are commonly older males, though know-ing Google, this may have evolved by the time you are reading this book. The image sharing network Pinterest currently skews female, while the blogging site Tumblr skews younger, as does the image sharing site Instagram and the image sharing app Snapchat.

These are just surface demographics around the range of age and gender found in each of these net-works. The decision process around what networks should be part of a brand’s social media strategy must consider a much more involved analysis of the demographic and psychographic composition of each network’s audience to determine which net-works engage people with whom that the brand would like to be social. Research and analytics into both our company’s market and the social networks’ audiences are of course key to collecting the data

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needed for this understanding. Analytics will also be needed to commence planning around the type and style of content that would be most influential to our target audiences, and of course to evaluate and opti-mize the performance of our engagement through these channels.

5.4 Inbound Organic Social Data Sources for Key Objectives

The data we will need for this multi-layered analysis of organic social media data comes from a wide array of sources. The most common source of social media data comes from the ‘native’ analytics interfaces or dash-boards for each social media channel (e.g. Facebook Insights). Other sources of data include the social media platforms’ Application Programming Interfaces (APIs), “Listening” tools, and publishing tools. These sources are represented in Figure 5.1 in the order of their applica-tion to strategy and execution.

Figure 5.1 Social Media Data Sources

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Sidebar: Structured vs. Unstructured Data, & Machine Learning

Before looking at this second data source for defin-ing and understanding target audiences, it is impor-tant to understand the distinction between structured and unstructured data, since so much of social media deals with unstructured data, while most of our other analysis tends to work with structured data.

Structured data is historically the most common form of data. As the name implies, structured data is data that has been collected and organized around a pre-defined structure, or plan, and which is typically stored in relational databases in tables of columns and rows. When we collect structured data, we know what type of data (text, numeric, etc) and the meaning of that data (name, customer ID, etc) will reside in each variable of this clearly defined set of variables, and we understand and have mapped the way in which variables create connections in the data across the data set. For example, we know that the “customer ID” variable in the “customer details” table can be matched to the “customer ID” variable in the “customer activity” table in order to connect that customer’s details with that customer’s purchases.

As mentioned, when working with social media data, we are more likely to be dealing with unstructured data than with structured data. When monitoring social media, we never know what we’ll get in each subsequent post we collect. Will it contain an image? If so, what is the image? Will it contain numbers? Will they be numeric characters or spelled-out? Will it contain a person’s name? Will it contain a prod-uct’s name? Will it reference the use of our product,

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or a response to our advertising, or an interaction with our support team?

With structured data, the answer to these questions would be inherent in the source of the data, and the organization and storage of the data would be very straightforward, with customer service interactions logged in the customer service database, product feedback in the market research database, and media response feedback in the marketing attribution data-base. However, because the data from social media monitoring and from the social media APIs is largely unstructured, before we can know what to do with the content of any given post, we must first determine what it contains.

So, before we can begin utilizing performance or predictive analytics methods against unstructured data, we must first provide that data with some struc-ture that our analytics methods can understand. This transformation itself typically involves an order of analytics known as “machine learning”. Because so much digital data is unstructured and needs to be classified and organized before it can be used, machine learning is a rapidly advancing field of com-puter science and artificial intelligence. Machine learning involves teaching the computer some initial rules about content, then allowing it to ‘learn’ and improve its ability to recognize aspects of the content with each new piece of content it receives.

There are two common forms of machine learning for the type of content we will encounter in social media and digital marketing more broadly. Natural Language Processing (NLP), which deals with teaching the computer to ‘read’, and Audio/Visual Pattern

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Recognition, which teaches the computer to hear and/or see, and is useful for image processing, voice to text processing and video analysis.

Most readers of this book will not be expected to run machine learning algorithms, but will instead encoun-ter machine learning when processing their unstruc-tured data somewhat remotely through the tools used in their organization for the analysis of social media data. What is important to understand for the analytics purposes addressed by this book is that the unstruc-tured data being collected from social media is not usable for performance analysis or predictive mode-ling without prior machine analysis to organize that data, and that the results from subsequent use of that data will only be as good as the machine learning algorithms that organized it.

Digital analysts should be sensitive to the nature of unstructured data, and should expect and prepare to account for much more ‘noise’ in data sets derived from unstructured data. Analysts can account for the noise that is inherent in unstructured data and build more effective data sets by understanding the data collection methods and the content classification approaches related to each unstructured data source being brought into analysis in the organization. Indeed, as will be repeated throughout this book, the core role of the analyst before any results are pro-duced is to take ownership of the data from which that analysis will be developed. No matter how skilled at the tools and methods for analysis, if the analyst is working from bad data, their analysis will ulti-mately be bad in terms of the guidance it provides the business.

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5.5 Applying Social Metrics

Context & Research: Defining & Understanding Target Audiences

As mentioned previously, the first step in an organic social media strategy is to match the characteristics of the target audiences we would like to engage with similar actual audiences in social media channels. This will often begin primarily as an evaluation of secondary research into social channel demograph-ics, psychographics and technographics (which measure characteristics of technology use). As ana-lysts, our primary source of data for this understand-ing will come from listening to the conversations in those channels to understand what topics and tone related to our interests prevail amongst our target audience, and accessing data representing user char-acteristics from our social network’s analytics inter-faces and APIs.

Social Media Aggregation and Listening Platforms

Social media listening applies text analytics algorithms to the content coming from social media channels, sorting and organizing that content by topic, author and sentiment. One primary utility of social media listening and analytics is in exploratory psychographic research, through the discovery and understanding of what matters to our target audiences in social media channels. Beyond the research application, many brands view maintaining a high volume and/or posi-tive sentiment around brand discussions to be a per-formance goal, and use social media listening tools

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to measure these goals. The measurement of volume and tone of conversation as a performance metric is frequently, and often legitimately, criticized as lacking clear association to actual business objectives, partic-ularly if the strategy behind social communications is not clear on the buying and product use habits of the audiences in those social channels.

One way in which listening as both a research and scoring system has come to have clear operational value to many brands is through customer relation-ship management and customer satisfaction measure-ment. This approach was pioneered by brands like AT&T and Comcast who had technically savvy users who were early adopters to Twitter and actively took to that channel to voice their complaints about ser-vices with these brands. These companies, and others like them, sought to move from reactively responding to complaints about their product as a risk mitigation exercise, to proactively opening Twitter as a customer service channel. This meant establishing customer service agents who would monitor and respond to customer service based mentions of the brand. This also created a need for integration of social media profiles (like a user’s ‘@’ Twitter handle) and their Customer Relationship Management (CRM) system profile. This use will be discussed further below in the sections on evaluating performance and optimizing social engagement delivery.

For listening purposes, Radian6 emerged as the leading social media listening platform by sales sometime around 2008, was acquired by Salesforce.com in 2011, and remains a leading selection for listening by com-panies of all sizes as of this publication. There are many choices for social listening platforms, some are

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integrated with other functionality, like the Salesforce solution’s integration with CRM or Adobe’s inclusion of an ‘Adobe Social’ module in the Adobe Marketing Suite, or a tool like Sprinklr, which offers both social publishing and social listening. Others, like Visible Technologies and Crimson Hexigon, are stand-alone with a specialized focus on social media listening.

The primary function of all social media listening tools is to take unstructured data from social media sources and build an organizing structure to reveal insights about the people, topics and attitudes related to that data. Some good free examples of the ways in which the data can be organized through all of these tools to guide insights can be found at Social Mention (http://www.socialmention.com/) which provides keyword, sentiment, user and hashtag extraction from searches on social media sources, and Bottlenose Sonar (http://sonar.bottlenose.com/) which offers a dynamic topic mapping chart around searches.

The key understanding for an analyst using social media is that, just like with any other method of analysis, the quality of the data you put into the analysis will determine the quality of what comes out of the analysis. The free tools above collect data from a basic text search, and the results will typically include content that is irrelevant to the intent of the search. Paid enterprise social media listening tools allow much more control over what goes into a search, which can be a blessing and a curse. If the social media tool is intended to be used for exploratory research to discover emerging topics and trends, then the search terms must be written to exclude very little content, since the next trend could be anything coming from anywhere. The extreme end of this is

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an effort to monitor the internet, which is a very big data exercise, and which makes the effort to identify that relevant and meaningful piece of information the proverbial search for a needle in a haystack.

The more common approach to search is to narrow and focus the terms. This limits the ability for discov-ery, but also limits the probability of irrelevant con-tent and improves the quality of analysis around what data is being collected. The potential issue in con-ducting analysis when limiting and focusing search terms is that the sample for the analysis is prone to confirmation bias. In other words, while we will now have topic and tone analysis that is more accurate for the content we have collected, our focused search may mean we are looking for what we’ve already determined we need to know, and we are missing content that would more accurately reflect what we don’t yet know, but should know, about our market, our consumers, and the perceptions of influencers around our brand and our market.

Thus, when done with an effectively balanced search strategy, the data being collected can give us a good sense of what is being said in social media by our target customers, about our brand, and about our audience’s interests more generally. While the vol-ume of positive or negative content around key top-ics is sometimes used as a performance measurement, such measures can be drastically skewed by the search terms around which the measure is taken, and so performance measurement through social listening should be considered with caution. The real value of social media listening comes from its ability to deliver insights around the opportunities to create and optimize influence with target audiences

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through social channels. Listening gives us a very good sense of what is being said, and the feelings and attitudes present in those conversations. Under-stan ding the ‘who’ of this audience is assisted by analysis of our audience’s characteristics and behav-iors as drawn directly from the social network analyt-ics dashboards and APIs.

Network Dashboards

Most social media platforms provide analytics around audience and performance through a dashboard view, and offer additional access to data through an API. The analytics provided by social media networks through their dashboards tend to focus on what the network considers performance metrics. Not surpris-ingly, the metrics are oriented around what the net-work is most focused on delivering, so these metrics typically center around views (impressions) and some type of engagement (like, comment, share, retweet, repin, repost, etc.). These metrics and the channel-specific dashboards in which they are deliv-ered are useful for measuring tactical success against marketing objectives in specific channels. The Facebook ‘insights’ dashboard (Figure 5.2) provides one of the more comprehensive examples of social media metrics.

This Facebook dashboard view gives a sense of the data a social media strategist or moderator will have to work with in evaluating their success over some period of time. While these terms are specific to Facebook, the type of content conveyed is what is common in most dashboards: (1) how many people ‘followed’ our account, (2) how many people saw

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our content, and of those, (3) how many people did some-thing with our content. Not every popular network has embedded analytics (i.e. Instagram does not as of this pub-lication), but for those that do, whether looking at Facebook, Twitter, YouTube, Pinterest, Tumblr or another of your choice, the fundamental measurement of “counts and amounts” will remain the same.

When the outputs being measured are related to audi-ence size or content impressions, it is difficult to place the value for any sort of business outcome on that measurement. The size of the crowd or the number of people who see our content are only the means to a more meaningful outcome. Putting too much weight on these measures creates the risk of building a strategy meant to drive “vanity metrics” and not much else. However, these metrics can be useful in providing context about our audience.

Though some contextual metrics are available within the ‘insights’ dashboards of most tools, they are also typically

Figure 5.2 Facebook

Insights

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very limited. For example, the “People” section in Facebook insights gives only gender and age informa-tion (Figure 5.3).

The “Posts” section (Figure 5.4) gives high-level feed-back on what type of post is generating engagement.

After all of the preceding chapters, at this point in the book, you are hopefully thinking about how nice it would be if these various metrics could be evalu-ated together — for example, by looking at clicks through each type of content by gender and age. In fact, a problem of most social network’s insights pages is that they partition the analysis into pre-defined views that don’t allow for deeper dives and more refined analysis. For this, the analyst needs to move outside of the native dashboards, and into the data available from the Application Programming Interface, which may be accessed directly, or more probably by way of an enterprise social media man-agement application.

As the dashboards provided by social media platforms are built primarily to provide an understanding of the

Figure 5.3 Facebook

Insights — People

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‘counts and amounts’ for network-intrinsic outputs, they are a textbook example of pure descriptive performance measures. As noted, the interfaces limit the possibilities for contextual analysis, but even if they offered more pos-sibilities for analysis, they would still lack the most impor-tant context for our business: the business outcomes that were driven by those measured (and contextualized) outputs from each tool. The measurement of outcomes will be discussed in the section immediately below, but first we will consider the other descriptive context data that can be built around social network performance metrics through the use of social network application programming interfaces, or APIs.

Figure 5.4 Facebook

Post Metrics

Social Network Application Programming Interfaces

Application Programming Interfaces (APIs) are exactly what their name implies — an interface enabling some application’s data to be used in the programming of another application. APIs can provide read-access, which allows data to be drawn from the course application, and write-access, which allows data to be sent back into the source application.

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These APIs are the lifeblood for social media work-flow, publishing and engagement tools like Buddy Media (Salesforce), Sprinklr, HearSay, Spreadfast, Social ware and others, which provide process work-flow and multi-channel publishing and analytics. If Facebook or Twitter were to cut-off API access to any of these tools, their entire reason for existence as an interface to these network would be lost. But APIs exist to produce mutual benefit — the social media support tools wouldn’t work without the social network APIs, but the social networks wouldn’t be as useful to advertisers without the social media support tools.

What APIs allow for these social ‘middleware’ tools, they also allow for anyone who learns to access the API through a programming language; the ability to gain deeper access to more granular data within each channel, and the ability to combine data and insights across multiple channels.

The middleware tools typically use their read access to APIs to create integrated views of the more standard metrics already provided in the channel analytics, and make use of write access for publishing. As analysts, the more interesting data in developing context around our engagement in social media will be what we can access directly from the APIs.

Each API has its own intricacies for access, and requires some programming ability in JavaScript, so we will not address access here. The book’s supple-mental website (www.architectingexperience.com) has some pointers on getting started with the stand-ard channels. As an analyst, your primary ability

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should be to set the requirements for the data, then seek the help of a programmer if needed to access that data. So for now, we will set aside the question of how to get the data, and focus our thinking on how to apply the data to descriptive analytic uses beyond simple performance reporting.

Facebook Social Graph API

One of the richest sets of social API data comes from Facebook’s Graph API, so this will be our focus as we learn how to think, access and use data beyond the dashboard. This method for working through the Facebook API can be applied to any other social net-work API, even if there is less to work with in those datasets.

Facebook is a marketing company that offers value through its ability to use data for very accurate tar-geting, and the Facebook Graph API reflects the centrality of contextual data in Facebook’s business model. All of Facebook’s data is information about users and the context of their use; their friends, their interests, their level of engagement. Your ability to access this data about a person depends on “permis-sions” that are granted to you by each individual. Without key permissions, the information available to you in the API is relatively limited. With full per-missions, you will basically be able to recreate and fully analyze all the context around that individual’s Facebook experience.

Permissions are granted by users through requests made by Facebook apps, so the collection of the permissions needed for the desired analysis of

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contextual Facebook data must be explicitly built via apps. Any analyst seeking contextual data from Facebook needs to think through the design of every related expe-rience with the design team to identify how permissions might be obtained through that experience, and to consider how the request for permissions might also impact the experience. Users will typically be willing to give permissions that make sense in the context of the app’s intended use, but may decline to use the app if the requested permissions fall outside their comfort zone. Figure 5.5 is an example of a Request for Permission dialogue that is seeking more permissions than a user may be willing to give.

Analysts must therefore be aware that the data they are seeking through apps may not be made available to

Figure 5.5 Facebook

Permissions Dialogue

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them if the user does not clearly understand the purpose of such access. However, for the sake of our interest in fully exploring the API, we will proceed with this example as if we have been granted a com-plete set of permissions.

The permissions list above provides a good initial sense of what we might have to work with when granted full permissions. The list of details behind each of these permissions is too large to provide here, but is available on Facebook’s developer site (https://developers.facebook.com/docs/graph-api).

Social API Analytics Context & Research Objectives

The most important context available to us from social APIs is insight into customer segments. As reit-erated throughout this book, a clear understanding of the customer, their behavior and their motivation is central to our marketing strategy. As introduced in Chapter 3, statistical segmentation of customers and the development of personas for design around those segments is the way in which we develop this under-standing. In Facebook’s Graph API, the “graph” refers to the social graph, or the connections between people in a social network. This view into the social graph constructed around shared interests and activi-ties can very effectively deepen our capability to define and document accurate behavior and interest based personas.

The way in which data is referenced and accessed in the Facebook Graph API will be similar to methods you will encounter in working with other social net-work APIs. Access to data first requires authentication as shown above. With proper authentication, calls

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for data are then made programmatically, typically using PHP, JavaScript or through a mobile platform Software Development Kit (SDK). APIs typically con-tain access to hundreds of fields, which may be organized through a top-level categorization, as they are in the Facebook API, where these categorizations are referred to as “nodes”. Currently, the node related to an individual ‘user’ in Facebook contains over 50 fields of data, many of those with sub-nodes or deeper clusters of data about them accessible through the Facebook API. This is too much data to review in detail on these pages, so I again encourage you to become familiar with the Facebook API (and others) through exploration of their documentation and associated tools. One such tool in Facebook is the Graph Explorer, which is accessible once you establish a Facebook Developer account.

The Graph Explorer provides a graphic interface in which API queries can be easily run. The results of API queries appear here as they are returned through any method of API call, in JSON format (JavaScript Object Notation), a snippet of which is shown below.

{ “id”: “100000000000001”, “bio”: “Happy husband and dad & hobby guitarist. Writer, strategist and entrepreneurial manager working where culture, communication and tech meet. Kellogg MBA.”, “education”: [ { “school”: { “id”: “200000000000001”, “name”: “Kellogg School of Management” }, “type”: “Graduate School”]}

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The JSON data format and the mechanics of working with JSON require some learning around both the JSON format and your API accessing programming language of choice. Teaching programming to this degree is beyond the scope of this book, but luckily there are many excellent free resources such as W3schools.com that will help with this. However, even without a trained understanding of JSON, the method by which JSON organizes data should be apparent from the snippet above. As you see above, within the ‘user’ node, this user has a unique ‘id’ distinguishing them from all other Facebook users, which is followed by a ‘bio’ field, then by additional fields (organized in this case within the ‘education’ sub-node) presenting details into this user’s educa-tion. This snippet represents a small slice of the information returned from a call to the ‘user’ node with reference to a user ID, and this structure would repeat, with unique variable names, for as long as there is data.

For those with interest in the data collection aspect of digital analytics, it will be worthwhile to learn more about JSON and programmatic methods for accessing API data. However, for our consideration of API usage in this section, our focus is on the potential for insight into customers available to us through this source that is not available through the Insights view of the Facebook user interface. The extent of this understanding for Facebook, and for other social networks can only be realized through review of the API field documentation for each net-work. In terms of developing insights from this data, there is an initial challenge in that most of the data is “unstructured” and much of it is text based. Some fields such as ID, dates, age, gender, etc, can be

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This is a small portion of the data returned on this user. For the purpose of research to support content strategy, I may want to classify the content interests of my Facebook audience using this data. I might begin

compared and analyzed across records as presented, but the more text-based and descriptive fields will need to be parsed with text analytics algorithms to be converted to data that can be compared and ana-lyzed across records. For example, the snippet below is taken from a query on ‘user/likes’:

{ “category”: “Movie”, “name”: “Akira”, “id”: “307262422778684”, “created_time”: “2014-09-15T15:11:20+0000” }, { “category”: “Musician/band”, “name”: “Howlin’ Wolf - Chess Records”, “id”: “735854339772319”, “created_time”: “2014-02-19T20:38:23+0000” }, { “category”: “Book”, “name”: “On the Road”, “id”: “613324775416871”, “created_time”: “2014-05-01T23:56:03+0000” }, { “category”: “Author”, “name”: “Jack Kerouac”, “id”: “1438690426374001”, “created_time”: “2014-04-15T20:58:19+0000” }

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by grouping users around engagement with my con-tent (engagement with certain posts, conversion on site, etc). Within these groups I might be interested in a routine that would code and count the different ‘categories’ to identify relative levels of “likes” across movies, reading and listening to music. I might exclude “Author” records for the purpose of quanti-fying interest in categories of content across reading, watching and listening, but I might want to evaluate categories of authors against an external database to classify segments by shared interest in writing styles.

With the amount of data available from just this API as a start, and with the possibilities for analysis lim-ited only by the data mining and pattern recognition capabilities of the analyst and their supporting tech-nology, the question should not be whether to draw customer insights from social context data, but rather where to invest in getting the most useful insights to drive key objectives. As with all of our digi-tal analytics endeavors, the basis for determining objectives begins with documentation of the prob-lems we wish to solve, and an understanding of our current performance in solving those problems.

Optimizing Social Engagement Delivery

While most social media content is generated by users, brands may of course also take an active role in engaging users within social media. This social activity by the brand may be proactive via the intro-duction and moderation of content that will gener-ate engagement by target audiences, or it may be reactive, such as making responses to customer com-plaints that were aired publicly through social media.

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Figure 5.6 shows the basic tactical paths for both reactive and proactive social media activity by brands. From the standpoint of the digital analyst, what is important to note is the importance of social media listening at the outset of both the reactive and proac-tive paths.

Social media listening is conducted via specialized text analytics applications built to collect and analyze streams of social media content to identify topics, the sentiment of content around those topics, and the reach and influence of authors. The first platform to dominate the social media listening analytics market was Radian6, which since its acquisition by Salesforce.com and addition of capabilities beyond listening is still considered a very strong platform for social media monitoring. Other pure-play listening platforms (as of publication) include Synthesio and Visible Technologies.

Beyond pure-play listening tools exists a class of social media tools dubbed “Social Relationship Platforms” which not only collect data from social media for

Figure 5.6 Social Media

Tactical Paths

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analysis, but which also facilitate the publishing of content to social media channels. Leaders in this space (as of publication) include Sprinklr, Spreadfast, Hearsay and Salesforce.com’s Buddy Media. These platforms provide a more ‘full service’ approach to social media engagement delivery with process work-flow capabilities for creating, approving and publish-ing content, with integration to social network APIs for direct publishing, and with monitoring and per-formance reporting. Many of these platforms also have the capability to integrate to Customer Rela tion-ship Management (CRM) platforms, and with careful planning around customer data collection, if a record in the CRM database contain a social profile ID for that user, any content generated in that channel by that user can be linked to other data in the CRM sys-tem. Such integration from social to CRM can be immensely valuable in identifying the potential cost from the loss of a dissatisfied customer complaining through social media, and in providing context from social media conversations when engaging with customers in other channels also linked to the CRM database.

In terms of the value and applicability of social media analytics, all listening capabilities, whether pure-play or integrated into a ‘relationship platform’, share the same potential weakness; the analysis will only be as strong as the sources of content and the searches established against these sources. Because there is so much content in social media, Social Media Listening typically relies on data collection limited by keyword searches. If searches are created carefully and kept broad, then actual discovery of opportunities and issues can happen through social media listening. However, often social media listening keywords are

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established around preconceived ideas of what is important to monitor, creating a closed feedback loop between these preconceptions and the repre-sentation of the brand in social media that is subse-quently reflected through the tool. As is the case with analysis from any source of data, the social media analyst must be certain that the analysis they produce takes into context the breadth and depth of social media content searches providing the data for the analysis.

Social Media Performance: Evaluating Social Influence & ROI

When looking at the data accessible to us through APIs, we consider the contextual and research based analytics that could be derived from the data. With that in mind, let us now turn back to the perfor-mance aspect of social analytics, where the main problem to solve is always in developing a legitimate understanding of how to identify and value the busi-ness impact of the outcomes driven by social media efforts.

When measuring the performance of social media, it is possible (and easy via dashboards) to measure the counts and amounts of channel-specific interactions such as likes, comments, shares, plays, downloads, retweets, follows, etc. The question that typically arises around such measures is what value any of them have in contribution to business objectives such as qualified lead generation or sales. The rela-tion of social media to lead generation or sales is best established through an attribution model that ties channel and content engagement to an eventual

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conversion (perhaps several points of contact down the road). The topic of multi-channel attribution and a review of its methods will be addressed in Chapter 9.

5.6 Search Engine Optimization

While social media has become the biggest chal-lenge for earned digital marketing in terms of devel-oping and executing a measurably effective strategy, Search Engine Optimization (SEO) remains the original earned media channel, and maintains its own set of ever evolving challenges which data and analysis can be used to help identify and address.

SEO emerged as a specialization in the early days of digital marketing as a practice that understood the inner workings of search engine’s content crawling and ranking algorithms, and that applied that under-standing to drive their client site’s to the top of search results. As the sophistication of search algorithm’s increased, the ranking methods of the algorithms shifted to a focus on the quality of certain types of content related across sources versus the simple pres-ence of certain content (e.g. the site description tag) or the quantity of certain types of data (e.g. back-links), and the opportunities for ‘gaming’ these algo-rithms began to diminish, separating true SEO specialists from simple rule exploiters.

With contemporary search engines, SEO is not a simple matter of tweaking portions of pages and building volumes of links, but is instead completely inter-related with site design and content strategy. Search engines look deep into websites not just to assign relevant keywords with that site, but also to

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evaluate and rank the quality and relevance of the site’s content against those keywords. With the increasing utilization of dynamic page content, the impact of site design on search presentation is an important consideration from the beginning of the design pro-cess. Additionally, the correct set-up and mainte-nance of meta-data within the content management systems which will populate sites becomes a critical effort for search engine optimization. Thus, the SEO manager of today is less a simple editor of links and tags, and more a manager of quality assurance around the myriad aspects of site design, develop-ment and content that determine the site’s appear-ance in search results.

Defining, deploying and measuring effective SEO strategies requires data and analysis. These may be acquired and applied through tools ranging from free solutions like Google Analytics, AdWords, paid solutions like Moz and Alexa, and enterprise SEO systems like BrightEdge, RankAbove and Searchmetrics. Through any of these approaches, there are several pieces of core SEO data analysis required.

The first is information all about keywords. How will search engine view your site in terms of the keywords it finds within the content? Conversely, what are the keywords driving traffic around your key topic amongst your targeted audiences? Analysis of these two areas will reveal the core challenge of the SEO manager — matching up what can be found on the site with what people are searching for.

Understanding competitive keywords can be easily accomplished through any of the tools mentioned above, but is free through Google AdWords as long as

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you have set up an account. The “Tools” section of Google AdWords offers a “Keyword Planner” which allows for key-word identification for any page.

These results also provide listings of the competition per keyword and suggested bids, which indicate the value in terms of relevance to searchers for those terms. The ROI of SEO can acutally be determined by evaluating how much was saved on winning traffic on these terms organi-cally versus via purchased search results.

As mentioned previously, appearing at the top of a com-petitive search is no longer a game of simple presence or quantity of target key words, but with text-analytics software being used in indexing algorithms, the name of the game now is quality and relevance in the content of your site, and clarity in the way it is presented. One way in which Google suggests SEO managers convey the relevance of their content is through links from high-quality related sources. Google is now smart enough to know if links to your site are coming from link farms’ which exist solely for the purpose of creating links to try and fool search engines. There is no quick way to create high value links, as Google notes, ‘Google inter-prets a link from page A to page B as a vote by page A for page B. Votes cast by pages that are themselves

Figure 5.7 Google AdWords

Keyword Planner

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“important” weigh more heavily and help to make other pages “important”.’

To this last point, in its advice to “Webmasters”, Google provides this advice:

Make your site easily accessible

Build your site with a logical link structure. Every page should be reachable from at least one static text link.

Use a text browser, such as Lynx, to examine your site. Most spiders see your site much as Lynx would. If features such as JavaScript, cookies, session IDs, frames, DHTML, or Macromedia Flash keep you from seeing your entire site in a text browser, then spiders may have trouble crawling it.

The Lynx browser referred above is a text only browser that renders pages without images or styl-ing, breaking the content down to a human-readable form of what the web crawlers will see. If the content you want to be found in search engines cannot be found when viewing the site in this browser, then changes need to be made to the site.

As further proof of the point made earlier that SEO managers need to understand the design and mechanics of the entire site, note the technical aspects of the design relayed in Google’s statement that need to be evaluated if the page is not conveying content to search engines as desired. Before they can begin to solve problems with search optimization, SEO managers must be able to identify and frame problems for the developers who will ultimately help

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them solve those problems. There are some analytics associated with SEO, but less than come into play when buying keywords for specific targeted audi-ences in search engine marketing. More important than data analysis for SEO is the ability to holistically analyze the site and the environment in which it operates, which is where the information technology end of the digital analyst/marketing scientist role comes to play.

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151


Paid Media Analytics

Having toured the owned and earned channels for marketing communications, we finally arrive at the paid media channels. At its most basic, Digital Paid Media is an extension of traditional advertising, and as the channel name implies, paid media is where advertisers have traditionally paid out large portions of their budgets to have their message placed in front of consumers. In traditional advertising, these ‘impressions’ through paid media may be achieved through television, print, radio and out of home (OOH) placements such as billboards and bus-stop sidings. In digital advertising, impressions are still a currency of measurement for many formats — especially in digital display advertising — but as is to be expected with the digital format, engagement (e.g. click-through, field input, video play) has become an increasingly prominent measure of value and success, and in many cases, conversion against an outcome from that engagement is evaluated as the ultimate performance measure for paid advertis-ing. Because so much money is spent on paid adver-tising, measurement of efficiency and effectiveness in paid media advertising has become a pressing concern for digital marketing managers, who need to understand and try to optimize the returns they are receiving from their expenditures across an ever expanding array of digital advertising channels.

ChapterSIX

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Paid search results and display banners are the two best recognized and most utilized methods of paid media marketing.

Paid search, or Search Engine Marketing (SEM) is the paid counterpart to SEO, dedicated to ensuring the appearance of an appealing summary and link to an ‘owned’ property of the brand for every search on a targeted keyword. SEM placements against key-words are purchased directly from the targeted search engines, so in the United States primarily from Google and Bing.

Digital display marketing, or digital banner advertis-ing, extends the location for brand impressions to appear from beyond the search engine and into any digital publication that has elected to display advertis-ing. The traditional display ad was typically formatted into a rectangle in a banner location or a square in a side ‘rail’ location, but mobile advertising, video adver-tising, social media advertising and content-oriented ‘native’ advertising have created many new formats in which a brand’s message may be displayed.

This interest in measurement has prompted the emergence of hundreds of technology solutions for managing and measuring paid digital marketing. This chapter will begin with a summarization of the paid media touch-points, the ecosystem in which these are activated, the fundamental measures of performance for paid media, and the ways in which analytics are being applied to increase the efficiency and effectiveness of paid media marketing through targeting and programmatic buying.

6.1 Digital Paid Media Touch-points

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The process to get the brand’s messages in front of an audience can vary from simple to extremely complex, but always requires some form of intermediation between the brand and their audience. The diagram below represents the potential processes for this inter-mediation. As shown in Figure 6.1, the advertiser’s message is separated from the audience that they’d like to reach by several layers of exchange (as the mes-sage moves from left to right in the diagram below).

Frequently, and especially with larger advertisers, the first layer a message passes through to reach its audi-ence is an agency services layer in which a media agency works with the advertisers to determine the creative content, formats and channels that will most efficiently and effectively support the advertiser’s paid media objectives.

With the media strategy established, it is time for the advertiser and/or their agency to begin purchasing

6.2 The Paid Media Ecosystem


Ecosystem

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exposures to the audience in accordance with the plan. Figure 6.1 shows several different options for purchasing exposures on publishers’ sites, but from our standpoint as analysts, our first question should be how data is being applied to optimize the purchases and their outcomes.

Figure 6.1 shows three sources of data for optimiza-tion of media placements. At the top of the diagram is a Data Management Platform (DMP) driving information into both the advertiser’s planning pro-cess (potentially via the agency) and also potentially into a Demand Side Platform (marked DSP in the figure). DMPs are essentially giant “cookie jars” — generating their own data about user characteristics and behaviors across the web, and aggregating data from other sources as well, as shown by the feed of data from publishers, DSPs and exchanges into the DMP.

In recent years, leading DMPs have been acquired or developed internally by the largest players in market-ing technology, including Adobe’s Audience Manager and Oracle’s acquisition of BlueKai. Other leading DMPs for advertisers as of publication are Rocketfuel (following their acquisition of X+1) and Aggregate Knowledge.

DMPs integrate and standardize data from first and third party data sources across paid earned and owned media, allowing advertisers to develop audi-ences that are consistent and applicable across channels. DMPs will contain some tools to allow for data mining and statistical analysis of this inte-grated data, and can make this data accessible to any other tool that the advertiser choses to use to

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conduct such modeling. They also allow for audi-ences developed through such modeling to be com-municated back out to the networks and supporting tools (DSP) through which these audiences are accessed.

Armed, via the DMP, with a very specific understand-ing of the audience(s) they wish to reach, the adver-tiser may now reach out to publishers in two ways: either directly with publishers, or indirectly through networks and exchanges, which is the more com-mon approach, though there are many variations that this approach can take, beginning with the exchanges used.

As mentioned, Google and Bing are the top sellers of paid search impressions. For display impressions, the Google Display Network (GDN) is typically con-sidered the top advertising network, but there are scores of additional leading networks including Rubicon, Tribal Fusion, AOL and OpenX. Large advertisers (and their agencies) will often use sev-eral networks to achieve their goals. For our objec-tives in understanding what part Ad Networks play in the paid media ecosystem, we will focus on the Google Display Network. DoubleClick for Advertisers (DFA) is the premium ad server for GDN, and AdWords can also be used for display ad buying in addition to its more common use for SEM placement.

There are several different KPIs that are commonly used to measure paid media performance, and each measure reflects a variation in approach that is typi-cally driven by strategy, objectives and the data used to support those things.

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The way in which an advertiser engages with advertis-ing networks and exchanges is typically directly related to how they work with data in determining strategy and measuring performance. An advertiser without specific consumer insights allowing them to value and target different segments of consumers will look to their networks and exchanges to determine and target the highest value segments through trial and error. If the advertiser’s strategy does not include a measur-able conversion as the basis for determining value, then it is likely that the measurement of efficiency and effectiveness will be around lowering the cost as much as possible for the greatest corresponding number of impressions, which is typically measured as a metric called CPM, or Cost-per-Mille (cost per thousand impressions). Being rooted in impressions, or views, this measurement (and any strategy based around it) is removed from any notion of what results from an impression of an advertisement. This measure sees the delivery of an impression as an end result, as opposed to the means to an end. Of course, marketing outputs are not the same as business outcomes, so marketing strategies based on impressions and CPM, while they may be focused on optimizing spending against impressions, cannot claim to be focused on optimiz-ing any sort of business outcome, as there is no basis to evaluate whether the marketing output (a cost) actually led to a business outcome (a return).

CPM

CTR/VTR/CPC

While advertisers can be excused for measuring impressions achieved as a KPI when evaluating

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advertising performance in traditional print, televi-sion and radio touch-points as there is no way to effectively measure the response to those impres-sions, the capability to measure engagement with content in digital channels allows for much better measures of performance for digital advertising. Thus, advancing slightly from measurement of CPM are the measures of Click-through Rate (CTR) and Cost-per-Click (CPC).

The “click” referred to in these metrics is typically a “click-through” to an advertiser’s landing page or other target content. Click-through (CTR) is a nice measure as it is a ratio, and a measure of engage-ment, and while it still does not measure an actual business outcome, it is at least capable of measuring the response to content, and as such conveys data that can drive further optimization of effectiveness in creative content and channel placements.

View-through Rate (VTR) is a variation of CTR which has become increasingly used as a metric for measuring not just the immediate reaction to an advertisement via a click, but also the potential for delayed reaction to an exposure to an advertise-ment. VTR measurement uses a cookie to recognize when a visitor to a site arriving through any referring source has been exposed to a paid advertisement, and counts that visit against View-through rate, meaning that even though the visitor did not click immediately in the ad, the visitor has come through to the site at some point after being served (and pre-sumably viewing) the ad. VTR thus allocates credit for the visit back to the exposure to the ad. While this method likely addresses actual cases where the cause for a visit is delayed reaction to an advertising

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exposure, it likely also gives credit for visits to ad exposures that were not the cause for an eventual visit, or were much less effective than a subsequent brand exposure through another channel like social media or email. Approaches for trying to properly assign some ‘partial’ credit to every touch-point that contributes to a visit is discussed in the next chapter.

CPC is a measure of the efficiency of the CTR/VTR achieved. If optimizing to CTR, then CPC will rise and fall according to the cost of reaching the size and composition of audience that maximizes CTR. If optimizing the CPC, then CTR will be affected if the ability to reach an audience that is more qualified to click is limited by a ceiling on the cost of reaching that audience. Thus there are serious budgetary and strategic concerns at stake in balancing an interest in cost with an interest in outcomes.

Cost-per-Acquisition (CPA)

The Cost-per-Acquisition (CPA) metric focuses squarely on a measurement of business results in terms of customer acquisition or conversion. This metric looks past a click or view that begins the pro-cess of consideration and evaluates whether or not that click or view ultimately delivered a positive busi-ness outcome. Because outcomes (acquisition or conversion) often take place after multiple interac-tions with the brand occur over time and across channels, CPA cannot be measured in terms of paid media cost alone. Rather, paid media costs are added to all other marketing costs before that total

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cost is divided by the number of acquisitions that occurred in the period influenced by that marketing.

CPA is an input into Return on Investment calcula-tion, as the total cost to acquire each customer is the ‘investment’ that underlies the return. Thus, decreas-ing CPA will not positively benefit ROI if the decrease in spending causes a corresponding decrease in results/returns. As with the other measures dis-cussed above, optimizing efficiency in spend is only positively impactful on the business if it can be done without equally sacrificing the effectiveness of that same spend. In fact, an increase in CPA would not be bad for the business if it resulted in an even higher increase in returns. As such, CPA should be estab-lished as a target informed by expected return per acquisition and the desired ROI.

6.3 Targeting & Retargeting

The discussion of metrics above should make clear the importance of optimizing for the correct balance of efficiency and effectiveness. This balance is not something that is achieved equally for all prospective customers, because not all prospective customer have the same potential value, respond to the same content and calls to action, or can be found in the same channels. These differences in the value con-sumers place on the business, their interests and their touch-points necessitate organizing customers by segments based on their value, interests and inter-action points, and then ‘targeting’ each different type of customer with the offers, content and con-tacts that will be most relevant to them.

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Targeting

The most basic form of targeting is conducted through the selection of publications based on the readership of those publications. This approach can be implemented through direct placements with publishers or through selection of publication topics/audiences established by the networks and exchanges.

Advertisers may seek to advance their targeting beyond everyone visiting a certain publication to include addi-tional criteria. Advertising networks and exchanges can support targeting on nearly any measurable character-istic, so targeting is typically constrained only by limita-tions on time and information. Because Google AdWords offers the basics of display targeting, we will begin with a specific look at targeting with this tool — specifically with the AdWords Editor tool, which offers a client-side campaign editing interface for Google AdWords.

The planning and management of paid media is typically organized around campaigns. Let’s begin with the types of ads that can be managed through this tool for any given campaign (Figure 6.2).

Ad type names are relatively self-explanatory with the exception of WAP, which is an abbreviation for Wireless Application Protocol and really just means ‘mobile’ ads. As you can see in Figure 6.2, AdWords can deliver formats ranging from the simplest site-links and text ads to Dynamic Search, Promoted Video and Display ads.

The simplest method for targeting any of these ads is by keywords. Keyword identification can be done auto-matically using Google’s site analytics (content scrap-ing and text analytics) defined in the SEO section

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above to evaluate your content and competitive con-tent. When placing media on publisher sites, Google’s keyword targeting also evaluates the content by which searches arrive at those sites and the content of the site itself to place your ads against inbound searches and destination sites that best align with your defined keywords. Note the option above for “Dynamic Search Ads”. These are ads that not only use Google algo-rithms for placement, but which allow Google to insert content into the ad dynamically in an effort to provide the most relevant impression to any given consumer.

While the definition of keywords can be left to the recommendation algorithm, analysis of what is per-forming should be conducted by an analyst. It is also important to review the keywords that are associated with traffic to your site, and to use the “Negative Keywords” filter, which explicitly tells the network what words to not associate with your ad, to ensure that your content is not served against keywords that are not relevant to your content.

Figure 6.2 Google AdWords

Ad Types

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Expanding from keywords that should and shouldn’t be associated with your advertisement, paid media targeting can be refined around the basic demo-graphic characteristics of location, age and gender. These targeting criteria may be effectively used when consumer interest in your products can accurately be determined by characteristics that are this broad. For example, there are certain categories of prod-ucts (e.g. types of razor, types of clothes) that will be clearly differentiated by gender, or by age (e.g. medi-cal and retirement related products) or by location (e.g. local and regional shops, events, etc). Targeting on this criteria can be used to target relevant audi-ence and exclude irrelevant audiences for broad solutions (e.g. men’s vs. women’s clothing), or can be refined further to target more specific solutions (e.g. women’s clothing for warm winter regions vs. cold winter regions).

Refining targeting further will begin to utilize the advertising network’s data around visitor interests, or topics. Ad networks place cookies on all web browsers that help to establish their interests. The DMP BlueKai offers web users the ability to see what they know about them by visiting the BlueKai registry. (Active as of publication). The registry exposes the way BlueKai sees each web user as it aggregates data from web tracking cookies.

Within each of the categories (seen in Figure 6.3) will reside anywhere from one to hundreds of pieces of information stored within the browser which purport to define the user of that browser. For example, expanding the “Hobbies” and “Interests” tab on my browser revealed 80 (16 pages

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times 5 lines per page) pieces of information about my hobbies and interests. Not all were accurate, and Figure 6.4’s view reflects the level of detail related to each item.

I am an online shopper, interested in healthy living. I have not read a print magazine in some time, but I consume content from magazines online. I do vote, and I am interested in the environment. Knowing this about me, when I establish an audience around interests and other characteristics, ad networks will target ads who have selected these ‘interests’ toward me if I fit the other criteria for the audience (e.g. age, gender, location).

Figure 6.3 BlueKai Data

Categories

Figure 6.4 BlueKai

Category Details

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While all of the targeting often begins with an understanding of segments of customers, ultimately, data should allow targeting on a 1-to-1 basis with each consumer. This movement toward 1-to-1 mar-keting begins with the practice of “retargeting”, which uses information from prior interactions with a consumer to continually re-establish the efficiency parameters (expected return and acceptable cost) and increase the effectiveness of content and chan-nels by leveraging information shared (tacitly and explicitly) through these prior interactions.

At the most basic, retargeting is simply targeting people who have had some prior exposure to the brand in a channel where such exposure can be tracked. Using Google AdWords for example, target audiences for ads can be created based on cookies that recognize that a consumer has visited your site before. Audiences can also be created using “look alike” models to identify consumers who may not have visited your site, but ‘look like’ those who have visited your site based on relevant characteris-tics, or who have visited sites in your market (e.g. competitor sites). When the prior interaction with your content involves some type of engagement that sends a signal, the retargeting effort can be made more effective and efficient through evalua-tion of that engagement. For example, if the con-sumer reviewed or added a product to their online shopping cart in a prior visit to the website, then the retargeted advertisement may include an image of that item, and may include an offer designed to appeal to the expected or expressed interests of that consumer.

Retargeting

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When organic social media was discussed in the pre-vious chapter, it was noted that social media impres-sions and engagement can also be generated through paid methods. While content shared through organic social media will only reach the network that is already engaged with your brand with additional earned impressions passed along to members of those consumer’s networks when content is engaged with or shared in some way, paid social impressions can be purchased from across the entire social net-work audience. The purpose and approach to paid social marketing is similar to that of all other paid media marketing, with the key distinction being the direct interaction with the social networks as ‘pub-lishers’. We will use the Facebook Ads interface to explore the similarities and differences in creating and targeting audiences in social media, as it repre-sents the most options available for targeting in social networks.

Paid Social Media

Facebook Ad Manager

Facebook’s Ad Manager is the interface for develop-ing audiences, setting objectives and measuring results. Facebook encourages the establishment of objectives for each campaign, and facilitates the creation of tracking pixels to track these objectives. For example, if your objective with a Facebook cam-paign is to drive not just visits to your site, but com-pletion of some activity like a download or purchase, then you would create a tracking pixel and assign it to the ‘complete desired activity’ objective, then

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place it on your site in a place that visitors only reach after completion of that activity, such as a “Thank You” page.

As with any paid media tracking pixel, not only does tracking conversions offer performance and optimi-zation metrics (CPA) tied to business results, but it allows the network to optimize delivery of future ads to this business outcome based on an algorithmic seeking for consumers who look like prior convert-ers according to any combination of relevant charac-teristics and behaviors.

Figure 6.5 shows Facebook’s top-level chart for cam-paign performance tracking. The shape of this cart shows the common issue of ad “wear-out” that will be encountered in any medium; at some point all rele-vant exposures will have been made and the ad will either be showing up to the same people again and again, or will algorithmically be taken out of rotation to avoid such ineffective repeat exposures. When campaign are targeted to very specific audiences (such as the one shown), wear-out happens more quickly. The campaign above was optimized to a low CPA, which accordingly delivered a low conversion rate. The campaign above was stopped when conver-sions flat-lined near zero while cost reached equilib-rium at their highest rate.

Figure 6.5 Campaign

Conversions

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Facebook’s audience creation allows targeting simi-lar to other paid advertising. The most basic target-ing is built around age, gender and location. Targeting can be refined around interests and behaviors. On social networks, understanding of interests and behaviors are clearly measurable: from explicit expressions of interest in “about me” sec-tions, and from engagement with content within the network. Because engagement with content within social networks is social, targeting can also be estab-lished and broadened around any individual con-sumer’s social network relationships.

Facebook and other social networks also allow retar-geting. For example, Facebook facilitates the crea-tion of audiences from information you already have about consumers that can be matched with Facebook’s details about them; including email, phone numbers, or Facebook user IDs. Advertisers using Facebook apps to engage their audiences can retarget to users based on actions taken within the app. And Facebook offers tracking pixels that can create retargeting audiences from prior visitors to specific pages of a website.

Facebook Marketing API

As a counterpart to its Social Graph API, Facebook also offers a Marketing API which allows the audi-ence management, ad campaign management, con-tent management and performance measurement data and capabilities to be access from external applications, including 1st party and 3rd party sys-tems used for the aggregation and coordination of paid media planning and delivery across multiple

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networks. The API also allows for 1st party customer data, such as CRM, sales and consumer insights data to be ingested and utilized by Facebook.

Smart advertising networks (including social net-works) are adding value to their services by offering such read/write APIs as advertisers and their agen-cies are reliant on multiple networks and are becom-ing more sophisticated in how they exchange data with those networks to optimize their marketing efforts. Networks that do not accept 1st party data from advertisers for establishing audiences and tar-geting and that do not deliver data back to advertis-ers for performance evaluation and further planning are creating walled gardens that advertisers will not be able to incorporate into their paid media ecosys-tem. For even smaller advertisers, working across multiple interfaces with audiences defined in each to manage advertising to what is actually a single audience accessed across multiple channels is not only inefficient with regard to the time and effort required to manage multiple systems, but is ineffec-tive as well since impressions to the same person cannot be coordinated. As a new generation of mar-keting technology to facilitate integration and coor-dination across channels emerges, APIs such as Facebook’s — and not the network specific dash-boards and interfaces — will increasingly become the method of interaction with networks and their data.

6.4 DSPs and Programmatic Real-time Bidding

Re-orienting ourselves to the diagram that started this chapter (Figure 6.1), we can see that through the discussion previously we have addressed how to

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deliver targeted content directly with a publisher or through an ad network with insights about our tar-gets informed by data from a DMP and from the networks themselves.

The final approach to paid media buying outlined in Figure 6.1 (reproduced here again) is an algorith-mic approach called Real-time Bidding (RTB), which optimizes the efficiency or cost/benefit ratio around qualified impressions by considering how much each impression could be worth, and determining how much to therefore bid to have the chance to present content to that impression.

The real-time bid involves a micro-second negotia-tion between what the publishers or their represent-ative network proposes an impression is worth to interested bidders, and the value that all advertiser bidders are willing to place on that impression. In this exchange, each side is calculating a value of the impression, and will have a limit (bottom for seller and top for each buyer) they want to achieve in the exchange. In this exchange, Ad Networks have far more information than the ad buyers, and so are in


Ecosystem

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a better position to maximize the value they can achieve from selling an impression. While the net-work knows what all bidders are willing to pay to start, and can discover various maximums through the bidding process, any given buyer does not know what other buyers are willing to bid. Thus, the deter-mination of what to pay for an impression must be based on a clear understanding of the expected value of that impression.

This understanding of what each impression might be worth is a data-driven understanding. It requires that advertisers understand how any given audience member they might encounter translates into a potential consumer of their product. Taking the same characteristics that can be used for targeting into account (demographics, interests, prior online behaviors, prior interactions with the brand at any track-able touch-point), each prospective impression must be values for their likelihood to convert, and the expected value of such a conversion. And this data-driven decision is not a managerial decision, it is a decision that must be made and acted upon in micro-seconds.

The tool that allows advertisers to evaluate data about the consumer behind any potential impres-sion to determine their propensity and value is a DSP, and in Figure 6.1, it is shown as the system that intermediates between the advertiser/agency and the exchange through the process of RTB. Demand side platforms allow advertisers to integrate their data and interactions with multiple exchanges, and their audience data from their DMP so in addition to being the interface with exchanges for managing advertisers’ interests in RTB, Demand Side Platforms

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also allows integrated performance measurement and media planning across multiple exchanges.

RTB is also commonly referred to as “programmatic marketing”, as the decision about each ad placement is ultimately being determined by a computer pro-gram using decision algorithms. The core decision algorithms for RTB sit within the respective DSP technologies, but each advertiser will customize the variables and values used as inputs to their respective programmatic campaign. This process of customiza-tion of programmatic marketing algorithms for cus-tomer acquisition and remarketing is part of a larger movement in digital marketing toward the practice of “marketing automation”, which we explore in Chapter 7.

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173


Testing & Optimization. Marketing Automation. Attribution

Having completed our overview of the data and data-driven strategy considerations related to the paid, earned and owned media channels, we now turn to several approaches that allow us to begin maximiz-ing the effectiveness of our marketing delivery within and across channels through the delivery of this data.

ChapterSEVEN

7.1 Prescriptive Analytics: Testing & Optimization

The strategic and design processes behind digital content and user experience delivery often reach a point in their process where a decision about the ‘best’ approach is required. While this decision can often be effectively made based on professional experience, prior data and a knowledge of best prac-tices, there are also times when none of these can be applied to the question at hand, either because there are multiple and differing opinions around what experience, data and best practice suggest would be the best course, or because none of these are appli-cable to a new content or experience design chal-lenge we are trying to solve. In these cases, rather

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than guessing, the smart marketer will turn to testing or experimentation to develop a data-driven position on how to proceed.

Testing and experimentation can be done within paid, owned or earned media by two approaches: A/B/n testing or multivariate (MVT) testing. A/B/n testing is the more straightforward of the two approaches. In this approach, a control version “A” is tested against a change in one variable through one or more test versions (designated “B” through “n”). In MVT testing, there are multiple test versions, each of which contain some unique combination of several variables being tested. Multivariate testing is beneficial when there is a hypothesis that proposes that a change of several variables will result in a bet-ter outcome than a change in any one variable, and allows the marketer to run a single test to under-stand the lift provided by each possible combination of changed variables as opposed to needing to run a sequence of A/B tests for the same understanding. The challenge with MVT testing is in the rapid growth of versions as variables are added, and the size of sample therefore needed to reach statistical significance in the results. In a simple A/B/C test, traffic to the element being tested is split 33/33/33 between the three versions, which means that we can quickly collect a large enough sample of visitors in each case to make the results statistically valid. In a MVT test of three variables however, there would need to be 27 versions to test all possible combina-tions of all three variables, meaning each version gets less than 4% of total traffic. With such small numbers coming to each version, the time it will take to reach statistical significance for each version will be quite long.

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It is worth taking a small sidebar here to ensure a shared understanding of the concepts of statistical significance and sample size since they are central to running a valid test. When we present the results of a test, we want to be able to say that we have some level of confidence that the results we got from the test represent the reality we could expect when the test version was exposed to everyone engaging in the experience. Ensuring a random distribution in the sample is a requirement for being able to claim that our test represents what we could expect to see in reality. The next requirement is to ensure we receive a large enough sample of responses from this random distribution to give us a reasonable expecta-tion of the range of outcomes we could see. In work-ing with a random sample, we will not expect that we can even predict the exact outcome we will see from our total population of visitors with 100% certainty, but we can strive for as little error as possible in our estimate. A typical goal for testing is 95% signifi-cance, which means that there is only a 5% chance that our results are based on an error in the sample; e.g. our sample was not perfectly random, and/or we have not seen a truly representative sample of the population. If we have a random sample, meaning anyone engaging in the experience has an equal chance of seeing the test vs. the control, then we need only to ensure a sufficient sample size to achieve statistical significance at our desired level.

To determine the sample size required, we need sev-eral pieces of information to input into our formula. The first is the level of significance, which should

Sidebar: Statistical Significance and Sample Size

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typically be no less than 95%. The second is the amount of change we want to detect, which we call the Minimum Detectable Effect (MDE). If our cur-rent conversion is 6% and we want to be able to detect an absolute increase or decrease of 0.5% (meaning a result of 5.5% or 6.5% would be pre-sented with 95% statistical significance), then our MDE is 0.5%.

With these three variables — significance, current conversion and MDE — we are ready to calculate sample size once we have dealt with a second con-cern over error in the estimate. With our selection of 95% significance, we’ve addressed the potential error of declaring our results to reflect reality when they actually do not. But there is an alternate poten-tial that we may make the error of missing an effect that actually does exist in reality. To address this error, we can define a ‘power’ of the test, which is the probability that the test will detect the defined effect. Just as 95% is a commonly acceptable level of significance, 80% is a commonly acceptable level of power, meaning we have 80% confidence that we will capture the effect if it exists.

Most testing tools will manage power internally, but will allow significance to be adjusted as desired. There are sample size tools included in testing tools or available in many online calculators which allow the input of current conversion rate, desired signifi-cance, minimum detectable effect and even power, and will output the same size required to fulfill all of these criteria. For example, for the criteria defined above, we would need 35,824 subjects per version (A and B) of the test, so a total of 71,648 subjects.

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Given the sample population required for the simple A/B test defined above, the sampling challenges and design complications inherent in MVT testing should be clear. Accordingly, we will focus on the A/B approach to testing for the remainder of this chap-ter, though it should be relatively obvious how most of these principles can apply to MVT testing as well.

The first step to testing is to define and document the goal you are seeking from the test. Test goals are most commonly related to improving performance around a key performance indicator (KPI), such as increased click through on display advertising, or increased engagement with a web page’s primary call to action. The goal may be defined in terms of a specifically quantified increase, but typically goals are simply targeted against a significant lift, the higher the better.

With the target KPI defined, the next step is to iden-tify one variable that we think could be changed to create significant improvement to our target KPI, and to explain, in the form of a hypothesis, why that change will produce improvement. Often, organiza-tions will reach this step and realize they cannot actually explain why they think a change might pro-duce results, and are simply seeking to see if an alter-native would work better than the existing approach, without any basis for thinking it would. In this case, a test without a hypothesis being tested is not actu-ally a test, it is instead an experiment. Running experiments to better understand how marketing works is not a bad idea, so there is no problem with this approach as long as it is acknowledged explicitly,

Designing a Test

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and is not an experiment masquerading as a test and trying to hide the lack of a hypothesis.

Whether we are running a test with a hypothesis, or conducting observation through an experiment, in an A/B test, our objective is to see a lift in outcomes by the alteration of a single variable. In a test, the jus-tification for the variable we chose and the change we make to that variable are defined in our hypothesis. The variable we chose may be related to any number of elements in the digital experience, it may be loca-tion of an ad, image or text, it may be the content of the ad or copy of the text, it may be the structure of a form, the sequence in which content is presented, or the way in which navigation is structured. In all of these cases, our basis for improving performance of the experience will typically be related to resolving an experience barrier or improving an experience driver. These are several of the most common driv-ers/barriers:

· Clarity. A lack of clarity in the reason for or pur-pose of a message or experience is a sure barrier to performance, just as increased clarity can be a driver of improvement in performance. You don’t want content viewers or site visitors to be confused by or lost in the experience you deliver, and if there’s a chance that they are, then increasing clar-ity at that point is a good testing focus.

· Consistency. A close cousin of clarity, a lack of consistency throughout the experience a user has in digital channels can create a sense of incongruity, and drive people away from the experience before they reach the point of conversion. A common area of missed consistency is in display advertising that drives users to a landing experience that does not

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align with what was communicated in the ad. Content, copy and UX that does not maintain logi-cal consistency throughout the site or across chan-nels (e.g. being unable to find something on mobile that was easy to find in a laptop browser) can also generate a negative response. Failing to remember details about a user within a site experi-ence as moving from page to page or section to section (e.g. what was provided in one form is asked for again on a subsequent page) is a major failure in consistency, especially as users expect sites to remember them (with their permission) from visit to visit.

· Value/Urgency. In display advertising, on the website, in email, and even occasionally in social media; at some point in the digital experience the visitor should feel compelled to act. A digital expe-rience that fails to convey a sense of value or urgency somewhere within its most engaged ele-ments is clearly not working hard enough to drive users to a conversion. An effort to more effectively convey value and/or urgency is perhaps the most common basis for testing, and rightly so. If the path to conversion is indeed being presented clearly and consistently but users still aren’t con-verting, the next clear area of focus is in ensuring that there is effective communication around the value or urgency that users should feel in following that path.

· Friction. A well designed and/or well tested expe-rience will present users with a clear and consistent experience that guides them toward a compelling presentation of value and/or urgency to act. The next barrier to conversion is friction in the process required to complete the conversion. This is most commonly related to content input required by

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users, for example, forms with too many fields or too many options in several look-up fields, or forms that are not designed well for input on mobile plat-forms. Technical issues, such as pages that are slow to save or slow to load, can also cause friction. Forms that can be started but not saved, requiring re-entry on a subsequent visit, violate good experi-ence in terms of both consistency and friction.

· Anxiety. A close cousin of friction, anxiety causing elements include a lack of clarity in the reason for the collection of information, or requests for cer-tain forms of personally identifying information (PII) such as social security number. While person-alization and consistency are typically desired by users, there are times when the insertion of per-sonal information or a sense of over-familiarity within digital communication (especially off-site display re-targeting) can seem ‘creepy’ and create anxiety.

While our focus in A/B testing should be on trans-forming just one variable, the transformation may address more than one of the drivers/barriers defined above. In a test, improvement in at least one of these areas should be the basis of the hypothesis.

Context, Context, Context

In creating and evaluating the results of a test, it is not only the hypothesis of the test that matters, but the way in which it is designed, conducted and evaluated as well. The importance of sample size has been discussed already. Another consideration around the sample to ensure statistical significance is the requirement that the sample is truly random

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in that it proportionately represents participation by every type of person (segment) you might engage in the experience being tested beyond the test environment.

With a random sample and sufficient sample size to reach statistical significance the test can be put into production with a target to reaching 95% statistical significance. While the test is running, the analyst can begin to define expected outcomes by segment where the characteristics or behaviors of certain seg-ments may inform how they are expected to respond to our efforts to reduce barriers or increase drivers of the experience. Not everyone is made anxious in the same ways, or has their anxiousness resolved in the same way. Likewise, not everyone perceives value in the same way.

Once the test has reached 95% significance, the ana-lyst should explain the outcome as clearly as possible. If the test version showed lift, the analyst will restate the hypothesis in explaining the result. If the test ver-sion did not produce a significant lift, there will be no explanation for the failure to achieve this result in the test data itself. In such cases, the reason for the failure to produce an outcome is that the hypothesis was incorrect, or more pointedly, the basis for the hypothesis was incorrect. The results of a failed test do not contain clues as to what to do differently in the next test, they only contain clear guidance on what would not work if tested again.

One valid ‘dig’ area in the evaluation of test results is in outcomes against segments. As stated above, the attitudes and characteristics of different groups may cause them to respond differently from the average

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response. Isolating responses from specific segments and evaluating results for these segments against the entire sample may reveal that what did not work for the entire population does in fact seem to work for a small portion of the population. With the capabil-ity for dynamic targeting inherent in digital market-ing, such knowledge can be very beneficial; though we may not push the tested approach to production at large, we can use targeting to serve the tested approach to members of the segment that respond better to the tested version than to the control version.

Common Test Tool Functionality

There are many strong testing tools available for marketers, which range from the free Google Experiments inside Google Analytics, to robust SaaS solutions such as Optimizely and Monetate, to enter-prise solutions such as SiteSpect and Adobe Test and Target. The capability to build increasing com-plexity into tests, such as MVT, sequencing and cross-channel integration, the built-in ease of integration with other tools, the number and size of tests that can be conducted and the integrated analytics for tests tends to drive cost, but all testing tools have several areas of functionality in common.

· Test set-up. Google Experiments takes the most basic approach, requiring two different page files to be tagged as test and control options, while tools like SiteSpect and Optimizely allow test designers to dynamically change aspects of a page with graphical HTML editor. All tools allow defi-nition for whether the test population will be split

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evenly across versions, or whether some versions get more traffic than others. All tools also require that a “goal” be defined as the KPI against which the test is being run, and that a target level of sta-tistical significance be defined.

· Test administration. All tools, from free to large enterprise editions, also provide common basic administration capabilities. All tools have built in sample selection methods to ensure randomization of the sample, and to monitor for statistically valid sample size and call the test once the desired level of significance has been reached.

Blind Spots and Dark Patterns

We end this section on testing and optimization with an area of caution when it comes to testing. Organizations tend to become very supportive of testing the more they understand how beneficial it can be in driving business performance. However, it is important that testing focused on marketing KPIs (CTR or clicks on the CTA) can clearly define how those KPIs also drive business results. When tests are designed solely to drive marketing KPIs without clear understanding of the impact of those results on overall user experience and/or business results, the tests are prone to ‘blind spots’ in terms of whether they are actually helping the business, and if they might actually be doing more harm than good when considered as part of a bigger picture.

A few years ago, I worked with a client who sold a high-consideration product offline through large retail chains, and used digital marketing in search, web, social media and email to guide comparison

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shoppers through product feature descriptions and value propositions over a multi-touch decision jour-ney in the online environment.

Because this client was not able to track users from the site to the store to truly understand how digital marketing efforts contributed to sales, their execu-tive team had decided to evaluate the value of digital (and the performance of the VP of Marketing in delivering that value) in terms of a ‘proxy’ measure for sales, which they determined should be a click on the “Where-to-Buy” (WTB) call to action for each product on the assumption that at least some of these would actually convert to sales.

Under pressure to deliver what executives considered to be “conversions”, the digital marketing team was asked to find ways to increase WTB conversions. In evaluating opportunities to drive up this KPI, one digital strategist noted that the Canadian version of the site had a higher WTB rate than the US site. On investigation, it turned out that the Canadian site did not show pricing on the product detail page as did the US site, but instead showed pricing only on the page served from the WTB click. Seeing this as the basis for the higher rate of WTB clicks on the Canadian site, the US team decided to test a change to their site that would match the Canadian site, moving pricing off of the detail page and behind the WTB click.

As a result, WTB clicks on the US site did increase. Luckily, this client allowed our analytics team to accompany this test with a satisfaction survey. Although the KPI was improved, the survey showed that satisfaction with the site was much lower in the test version than in the control, and many comments

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suggested that the frustration caused by having to look for pricing had soured them on the idea of selecting this brand’s product. While the test had achieved a result in increasing a marketing KPI, it had actually done so by decreasing clarity and increasing friction, and the result of this degraded user experience was an overall decrease in the value of the site to users and the business, despite the increase in the marketing KPI. Needless to say, we advised the VP of Marketing not to implement the winning result, and began to help the organization rethink how they measured the value and success of their digital marketing.

This case illustrates the unintentional sacrificing of good user experience in exchange for a business outcome in an approach that I call a “blind spot” because the organization has become blinded to the bigger picture in focused pursuit of a marketing KPI. However, there are many unfortunate cases when the sacrifice of user experience from manipulation of that experience in pursuit of a marketing goal is not unintentional, but is in fact quite intentional. The creation of such intentional manipulations of experience in pursuit of a KPI have become known as “dark patterns”.

Dark patterns are intentional experience design ‘pat-terns’ driven by the desire to maximize short-term outcomes without concern for the longer-term conse-quences. Thankfully for web users everywhere, since 2010, a group of user experience professionals has been focused on documenting these deceptive mar-keting practices whenever they are found via their website http://darkpatterns.org. This group of user experience designers has identified 14 dark patterns

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that companies engage in to intentionally deceive users in pursuit of higher performance in certain KPIs. While pursuit of higher performance in mar-keting is the objective of testing, if the proposed test option appears to align with any of the dark patterns below, the analyst should raise their concern over the use of a strategy that uses deception to increase a KPI.

Following are definitions for a few of the more rele-vant patterns of concern from the Darkpatterns.org website:

Bait and Switch: The user sets out to do one thing, but a different, undesirable thing happens instead. This is one of the oldest tricks in the book, and it is very broad in nature — many dark patterns involve some kind of bait and switch.

Disguised Ads: Adverts that are disguised as other kinds of content or navigation, in order to get users to click on them.

Forced Continuity: The user signs up for a free trial on a website, and in doing so they are required to enter their credit card details. When the trial comes to an end, they automatically start getting billed for the paid service. The user is not given an adequate reminder, nor are they given an easy and rapid way of canceling the automatic renewal. Sometimes this is combined with the Sneak into Basket dark pattern. This dark pattern was previously known as “Silent Credit Card Roll-over” but was renamed since the term “forced continuity” is already popularly used in Marketing.

Hidden Costs: A hidden cost occurs when a user gets to the last step of the checkout process, only to discover some unexpected charges have appeared, e.g. delivery charges, tax, etc.

Roach Motel: The “Roach Motel” is a broad category of Dark Pattern that subsumes most types listed on this site. Put simply, a Roach Motel makes it very easy for a user to get into a certain situation, but then makes it hard for them to get out of it when they realize it is undesirable. Email newsletter un-subscription is a well-known example — whereby

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7.2 Marketing Automation

The previous chapter introduced you to the practices of programmatic and algorithmic targeting and re-targeting in paid media channels. In essence, market-ing automation is the practice of programmatic and algorithmic targeting taken beyond paid media and applied additionally within any owned or earned media channel that a user might encounter through their decision journey. With effective marketing auto-mation, brands can ensure consistency and continuity in the user experience for each user by ‘remember-ing’ and learning from all prior interactions with that user, and by tailoring each ‘next’ experience a user has with a brand based on information the user has already provided (tacitly or implicitly) and models that evaluate the probable barriers and motivators on the path to conversion for each user.

it is typically easy to subscribe, but much more effort is needed to unsubscribe. The revised CAN–SPAM 2008 rules state that this prac-tice is forbidden for emails that have a primary purpose ‘to advertise or promote a commercial product or service’. (Unfortunately, CAN–SPAM does not cover ‘transactional or relationship’ messages.)

Sneak in Basket: The user attempts to purchase a specific item. However, somewhere in the purchasing journey the site sneaks an addi-tional item into their basket, often through the use of an opt-out radio button or checkbox on a prior page.

Trick Questions: The user is required to respond to a question (typi-cally in the checkout process), which, when glanced upon quickly appears to ask one thing, but if read carefully, asks another thing entirely. This pattern works because it is normal for users to employ high-speed scan-reading on the web — see Steve Krug: ‘We don’t read pages. We scan them.’)

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There are two distinct data and analytics challenges posed by this effort to guide users through a set of experiences designed to maximize their probability for conversion. The first is the challenge of recogniz-ing a given user as the same individual across the array of paid, earned and owned media touch-points. This is a problem we can refer to as establishing and maintaining a unified or universal customer profile. The second is the challenge of predicting what type of engagement will increase the propensity of any given individual to convert in this visit or in a subse-quent visit. This is a problem we can refer to as dynamic propensity modeling, which we will review with other analytic methods in Chapter 8.

Data Integration

The challenge in building a universal customer pro-file begins with the nature of device constrained data collection. In web and mobile analytics, anonymous data about a user is tied to a browser, so that a single person visiting a site in the morning from a PC and in the afternoon from their phone will appear to be two different people with two different visitor IDs to the analytics system. Without data integration, their click on a display ad several days ago will not be tied to their email ID in determining what promotion they should be sent. Their unsubscribe from the email list will not be tied with their identity for display targeting, or with either their web or mobile visitor ID, Their like of a sponsored post on Facebook will not be connected to their other behavior in response to display ads or web activity. And perhaps most dis-turbingly for the brand, despite having taken some or all of these actions over the past few days, none of

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these ‘signals’ about their experience and intentions will be recognized or acknowledged when this person finally contacts the call center for the purpose that their prior actions might indicate where they able to be analyzed (e.g. as a highly qualified prospect for a specifically searched product, or as a dissatisfied customer awaiting a very late shipment, etc.).

In the example above, a unified customer data pro-file would allow the brand to recognize that this is one single person engaging in different ways across multiple channels over time, allowing the brand to anticipate this person’s interests and needs and to make their best effort to address those interests and needs (and the prior activity around them) at each next point of engagement. The better the brand is at addressing interests, the more satisfied the customer or prospect, and the more likely they are to convert (or repeat, renew, refer or upgrade).

Establishing the unified customer view requires care-ful data design to establish a universal identifier that works across all channels. This is most easily achieved in owned touch-points that allow login against some-thing like a customer ID, which will associate each touch-point specific ID (e.g. laptop browser visitor ID and mobile browser visitor ID) with each other via the shared customer ID, usually coordinated within our Data Management Platforms (DMPs). This approach would also tie these touch-points to the Customer Relationship Management (CRM) system containing the customer ID, and to a call center call that asks for a customer ID (or links to that ID via a customer phone number). From the call center, a purchase or a registration/login we may also connect an email address with this customer ID.

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Integrating our owned channels in an experience that requires some kind of authentication requires planning to design each channel’s experience to col-lect a common identifying variable in each of these multiple channels (see Figure 7.1) and effort in the integration of those multiple channel data sources via that field, but with a commitment to that design and integration effort, the path to integration is rela-tively clear. The next challenge arises in the effort to expand this integration to the inclusion of paid and social media impressions and engagement.

With the connections we’ve made through our owned media, we can understand the user by their behavior across all of our owned channels, and by all the information we have about them within our CRM database, which can include demographic and transactional data. We can augment this with addi-tional demographics and data about each user’s perceived interests and actual engagement with con-text across the web through our DMP, which links any user we’ve encounter and tagged (pairing their

Figure 7.1 Cross-channel

Customer Data

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unique DMP ID with our universal ID) in owned or paid (DSP) media to all of the information the DMP compiles about them from other 3rd party data sources (anonymized offline transaction data, anonymized paid media impression/engagement data, etc.).

As shown in Figure 7.2, information about a specific person’s response to our advertising can be pro-vided via a DSP which pairs our universal customer ID with that individual’s unique DSP ID. With these additions we can understand any individual cur-rently present in any of our paid or owned channels in terms of their prior engagement with us in any other channel, as well as with regard to an anonymized categorization of their interests and behaviors across the web and offline via our DMP. We may also attempt to pair a social profile (or pro-files) with the universal customer ID by asking for a social profile or social sign-up in various channels (shown in CRM, in Figure 7.2, but could be web or app also) that are already associated with the univer-sal customer ID.

If users on our owned properties do not sign in, we will accordingly not be able to link them across devices or touch-points. However, we might still be able to understand them categorically based on the way they are categorized by the DMP, and we will be able to target them in paid via our DSP with this knowledge plus any information we connected about their visitor ID while they were anonymously on our site. While this is less than the ideal defined above, it is still better to have some cross-channel context than to have none.

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Figure 7.2 Extended Cross-

channel Customer Data

Marketing Automation Platforms

Marketing automation tools use (and often help to establish) cross-channel customer identification and statistically-based prediction of the best next action for each customer to automatically deliver those actions across channels and over time.

Figure 7.3 gives a crude representation of the type of cross-channel experience that might be “sequenced” or “orchestrated” with the help of a marketing auto-mation platform. As mentioned at the start of this section, marketing automation tool like Marketo, Oracle’s Eloqua, Salesforce.com’s Exact Target and IBM’s Silverpop can be used in an algorithmic approach, meaning programmatic and algorithmic targeting taken beyond paid media and applied addi-tionally within any owned or earned media channel that a user might encounter through their decision journey. In this use-case, marketing automation ‘remembers’ and learns from all prior interactions

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with each user, and tailors each ‘next’ experience with a user based on information the user has already provided (tacitly or implicitly) and models that evalu-ate the probable barriers and motivators on the path to conversion for each user.

However, marketing automation can be a prescrip-tive practice as well, and in fact, many implementa-tions of marketing automation start with prescriptive approaches wherein the marketing team pre-deter-mines the steps and timing that should be taken with users by setting up various “if/then” conditions such as: ‘if visitor visits ads a product to their cart but does not return for three days, send an email referencing items in basket and select an appropriate offer to prompt purchase’. This is the straight ‘automation’ side of marketing automation. If the understanding of customers are associated strategies behind the rules are good, then the result of the rules will also be good. But if the rules programmed into the sys-tem are based on partial or faulty understandings of customers and their motivations, then setting them as rules may cause more harm than good.

Marketing automation tools are typically applied first to an advertiser’s owned channels; web, mobile and email. These tools use their own cookies and tags to track users and their engagement across these

Figure 7.3 Cross-channel

Cross Time

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channels over time, and coordinate delivery in these channels via this user-level data. When this data is integrated with CRM systems and DMPs, and synchro-nized with paid media tracking, the data available for real-time decision making can become very robust. A marketing automation system can be a simple auto-mation tool; automatically executing the same pre-determined reasoning and tactics that might have been executed without software, but can now be delivered more quickly, and with more granularity in the approach. However, when cross-channel behavio-ral data integrated with contextual user insight is available, rather than building their potential for marketing performance on some combination of aggregate insights, experience and management intu-ition, the predictive analytics aspects of these market-ing automation should be activated an applied.

In the marketing materials produced around these tools, the specific predictive methods are left pur-posely opaque, presumably to defend from revealing proprietary competitive information. The specific data ingestion capabilities and models applied to that data are different across all offerings, and in evaluating Marketing Automation options, potential buyers of these systems should seek to understand how easily they can incorporate predictive analytics through the tool to apply dynamic machine intelli-gence to their marketing automation approach. Even as all of the specific approaches will have vary-ing degrees of difference, the underlying approach to machine learning for dynamic delivery remain largely the same:

1. Based on a comparison of an individual’s charac-teristics and other context to those of prior users,

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score the propensity or likely probability of con-version for each individual encountered through marketing channels.

2. Use dynamic content delivery capabilities to per-sonalize engagement based on propensity, dynamically delivering push-messages (email, mobile/text and ads) as optimized against the best probable response for each user, suppress-ing or escalating certain forms of engagement based on user score, and dynamically selecting the best content for each interaction. (This will be typically be done in integration with a DSP for delivery to display media).

3. Continually evaluate the ‘lift’ in probability to convert created by sequences of channel and con-tent interaction for users of different types over time, and update probabilities for propensity scoring and delivery optimization scoring accordingly.

This last step, evaluation of incremental lift by chan-nel (and ideally the specific content within that channel) across the entire path constituting the cus-tomer decision journey, and the assignment or “attri-bution” of credit for a conversion to each channel, has been a well-established topic of analysis outside of marketing automation for some time. While the integration of such insight into dynamic automation is the logical next step for this approach, a first gen-eration of Attribution technology providers have helped established the vision for the benefits of such measurement executed well, and have dealt with the challenges to this approach arising from discon-nected channel data.

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Cross-channel attribution is a method used in con-junction with cross-channel data to understand how all of our marketing channels are working together, and which are doing the most work.

The basis for that decision in a single channel like paid display, or across multiple channels, comes from a (machine learning based) understanding of how prior converters who either (1) shared a similar path to this point, or (2) are significantly similar to this person at this point ultimately proceeded from this point to a conversion. Not only can the model compare and evaluate large numbers of paths through multiple channels with regard to the respec-tive conversion value of each, but it can also predict how much each channel contributed by evaluating the probability that the same outcome could have occurred with at least one of the channels removed.

7.3 Cross-channel Attribution

Figure 7.4 Attribution —

Paths to Conversion

An example of how the attribution algorithm ‘thinks’ can be seen below. Figure 7.4 shows three different customers’ paths to a decision to buy or not. (In this case, let’s say a cart left abandoned for over 30 days is considered a lost sale).

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We see that Customer 1 had four interactions with the brand, in four different channels, and ulti-mately made a purchase. Customers 2 and 3 each had just three interactions with the brand, and while Customer 3 purchased, Customer 2 did not. We see that Customers 1 and 3 who both purchased also both engaged in channel 4, while Customer 2, who did not purchase, also did not engaged in channel 4.

Now, our attribution modelling algorithm will not determine that Channel 4 is an important driver of purchase (thus earning a higher attribution credit) because it was present in 100% of purchases and absent from 100% of lost sales, or that channel 3 deserves less attribution credit because it was only present in 50% of sales from a sample of just three observations, but it will begin to make these determi-nations as it collects thousands of observations, ulti-mately resulting in a weighting of each channel’s apparent influence over purchase in the context of all other channels that were also engaged by users and did or did not contribute to purchase in some number of cases.

This fills in one piece of the puzzle regarding our prediction as to the propensity that a customer we encounter in a channel will have to purchase if we engage them effectively in that channel, but it is only a piece of the puzzle, because these predictions come from a mean frequency of occurrence of com-binations of channels against all of our observed cases, and can be made more precise when we move from the general case which ignores much of the context around this path through the channels to more specific cases that account for more context.

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199


Data Management, Models, and Algorithms

ChapterEIGHT

8.1 The Applied Digital Analytics Playbook (ADAP) Part Two

Having defined the who, what, when, where and why of our digital marketing strategy through the first two sections, the third section of the ADAP now begins to describes how all of this will happen through data by defining how we will get and use data to drive the digital marketing experience.

ADAP Section 3.1 Segmentation

The description of segments is perhaps the most critical fundamental practice related to the strategies and tactics defined in this book, as an understanding of the customer, their behaviors and their motiva-tions resides at the core of every other aspect of data-driven strategy and methodology discussed herein.

Segmentation is simply the organization of custom-ers into shared groups through top-down (predeter-mined) or bottom-up (statistically) based clustering around their observed characteristics. Demographic characteristics are the most commonly used top-down

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variables, with segmentation by gender, age, ethnicity, income and geography typically emerging from even the most basic customer insights practice. The most common form of bottom-up segmentation for market-ing is developed through RFM (Recency, Frequency and Monetary) modeling. RFM modeling uses data from customer’s transactions, and groups customers according to the recency, frequency and monetary value of those transactions.

These two common forms of segmentation reflect a business-centered mindset in planning versus a con-sumer-centered mindset. Although this is informa-tion about customers, it defines customers in terms of their value to the business. However, customer-centered marketing should also be able to under-stand the value of the business to its customers, and should understand the customers on their own terms. For this, segmentation methods must turn — typically with a statistically informed view — to a set of characteristics known as “psychographics”.

Psychographic variables cover a wide spectrum of data about customers’ attitudes, interests, opinions, values and beliefs. Traditionally, such data has been collected through marketing research, but the value of the results of psychographic marketing research can vary greatly since data is collected through sub-jective self-reflection and self-reporting, which are notoriously prone to unintentional bias. With digital data collection, we are able to get more objective insights into customers’ psychographic traits through the observation of online behavior, including social network activity and content engagement across multiple media touch-points. In fact, the potential

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The next two sections of the ADAP, “Data Collected” and “Data Accessed”, document specific details regarding the sources of data that will be used for our descriptive, prescriptive, predictive and adaptive analytics.

The Data Collected section, Section 3.2, is where we provide details in terms of the approach and integra-tion around the data that we will be directly respon-sible for collecting and managing through our various 1st party channels via tagging or through 3rd party sources via pixels. Here is where we will outline how we will obtain data from our website, mobile apps, social media, email, organic and paid search and paid display media, and what that data will be. In answering this second question, we will also need to identify how data from each source will be joined with each other source by aligning the channel spe-cific unique ID for each user in each channel with their common universal user ID that spans all

insights that can be generated from the observation of online behavior are so valuable that the next gen-eration of digital user experience design will increas-ingly be required to consider how to design explicit opportunities for insight collection into the user experiences it builds. Thus, this third section of the ADAP begins with the documentation of the data we will need to construct segments or profiles and apply targeting rules or algorithms against those segments or profiles.

ADAP Sections 3.2 Data Collected & 3.3 Data Referenced

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channels. The question to answer here is what we can use from each channel to identify that user as someone who already exists with reference to another channel, i.e. with email address, a customer id, or a device identifier of some sort that would appear in multiple channels.

In this section we should document the formats in which data will be accessed or extracted from each channel (e.g. file export or API), how we expect to transform data into a common format, and how we will then load data to our own systems and provision that data for the analytics purposes that will be out-lined in Section Four.

The Data Accessed section, Section 3.3, is where we provide details in terms of the approach and integra-tion of data that we will be accessing from a separate system such as CRM, Call Center or Point of Sale systems. In these systems, we are not responsible for nor do we have control over the data fields that populate the system. Once again, we will want to identify a field in each of these systems which can be used to link a given customer in these systems with their records in all other systems by integration through the universal customer ID. We will also want to identify the same extract, transform and load (ETL) considerations for these sources as our owned sources, so the prime distinction between these two sections is that in the first we have control over what data we chose to collect, and need to articulate those choices to create the data, while in the second the data available has been pre-determined, and we are simply articulating what we plan to access from that existing set of data.

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The fourth and final section of the ADAP continues the definition of how we will use analytics to support the optimal user experience with data by applying data as analytics through each of the levels of the analytics pyramid.

In describing our “Descriptive Analytics” approach, we outline our performance reporting approach, including the method and cadence of such report-ing for various stakeholder across the organization, as well as the KPIs relevant to each of those stake-holders. We outline how we will provide context analytics around performance for each of these stakeholder groups. Do we expect to conduct ‘deep-dives’ into performance on behalf of these stake-holders, or will we be designing interactive dashboards that can guide our stakeholders into their own deep-dive exploration of context around performance? If the later, what type of pre-defined data categorization will we want to provide? Finally in this section, we will document how we anticipate delivering descriptive analytics data to the research organization.

In describing our “Prescriptive Analytics” approach, we simply outline the performance data and analyt-ics we recommend for use in determining testing needs and the context data and analytics we sug-gest for formulating hypotheses. We will also out-line the specifics of the testing tool we have elected to use, including the integration of data from other sources (for context or conversion) with that system.

ADAP Section 4: Analytics Plan

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Any existing repository of prior test results should be referenced in this section. Similarly, if we are aware of tests desired or planned by our organization at the time this document is developed, we can reference our test planning document, or document reposi-tory, from this section.

In the last section of the ADAP we describe the way in which data flows into “Predictive and Adaptive Analytics” across the digital marketing environment. This may be as simple as reference to the channels in which 3rd party algorithms are being applied, such as stating our strategies for programmatic buying through a DSP, algorithmic cost optimization or con-tent targeting in media or other channels, and/or marketing automation learning algorithms. If we have developed, or expect to develop, our own predictive or adaptive marketing algorithms, such as a proprie-tary propensity model, or if we intend to modify/customize the ‘starter’ models provided by our DSP or attribution tool, the need for and approach to customization would be documented here as well.

Applying the Data: The Next Steps

Once we have designed our data collection and transformation in a way that binds together each view of a single consumer as comprehensively as pos-sible, and have this integrated data from across paid, earned and owned media accessible to our analytics tools and marketing technologies, we are ready to begin using that data to conduct digital analytics.

There is a fundamental rule of data for marketing, the better it is integrated around a customer, the better the ability to market to that customer.

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The most common application of data to decision making has been via data scientists and analysts and their skill in extracting meaning from large sets of data, and in communicating that value to the organi-zation in ways that allow the organization to take action. The use of visual and increasingly other sen-sorial methods to communicate the meaning in data is known as “visualization”. The practice of finding and extracting meaning from data is often called “data mining”.

8.2 Data Mining & Data Visualization

Mining: Cluster & Factor Analysis

Perhaps the largest latent opportunity for competitive advantage in marketing is in more precisely under-standing the customer’s influences, and in identifying the best opportunities and channels in which to engage those interests. Improvements in the level and precision of understanding around customer traits and motivations can be used to subsequently improve predictions around the response to various marketing approaches, and to optimize the delivery of marketing experiences as these scenarios are executed.

As we integrate together behavioral data for each customer we engage across multiple platforms, we gain the opportunity to expand our intelligence about what types of behaviors drive desired out-comes, and which lead to undesired outcomes. This understanding of “what works” and “how much” via attribution analysis aids our intelligence into the value of various combinations of paths, but attribu-tion alone leaves many questions about marketing optimization still unaddressed.

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The first important question remaining after attribu-tion has explained the relative contribution of media channels on a path to conversion is the question “with whom”. The second is the question “why”. Although they may all be united in having at least some interest in what we are selling, it is critical for marketers to recognize that each customer is unique. In evaluating the performance of our marketing (and attributing value to multiple channels along the way), we should also evaluate with whom the marketing performed well, and with whom it per-formed poorly. Within each of those performance-based segments, we may then ask whether there are some distinguishable groups for whom marketing performed less well than others. In analyzing data for this purpose and forming segments from the bottom-up (versus pre-defined top down group-ings), the best suited data-mining technique is known as “cluster analysis”.

There are many statistical models and supporting algorithms that can be used to perform cluster analy-sis, the most standard of which may be k-means clus-tering. Put simply, k-means cluster analysis is a method of segmentation of data about customers into groups based on the degree of similarity between any individual observation and the mean value of various clusters of observations. Creating such clus-ters requires strong computational support as each value in the data set is iterated through in considera-tion of its relation to the various possible averages from various clusters of all other data in the set. The “k” in the “k-means” approach indicates that the number of clusters is not predetermined. The right number of clusters is determined by an evaluation of the extent to which adding an additional cluster

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(k+1) to the calculation reduces variance within clus-ters and increases variance between clusters.

Clustering is easiest to visualize with two dimensions or variables X and Y, but it can be conducted with any number of dimensions and variables, which is where the value of data collection and an experi-enced analyst comes into play. Since the clusters cre-ated by the analysis will be dependent on the variables provided for the analysis, determining what might be fed into a cluster analysis might involve some consideration of how knowledge about these variables might be acted upon. The variables or dimensions within clusters that cannot be influ-enced by us as marketers will provide us with context around context to which we must react, while the variables that we can influence will allow us where we may express this reaction along with the characteris-tics or behaviors around which we can proactively engage our consumers.

In mining and understanding our data, “Factor Analysis” is an extremely useful tool for reaching an understanding of the meaningful influences on con-sumers hidden within a large set of data. Motivations, perceptions and beliefs are all ‘latent’ variables — meaning that we cannot measure them directly, but we can observe their existence through their influ-ence on the things we can measure, which in digital analytics includes behaviors. With behavioral meas-urement, we see exactly what people do, but as meas-ured at the top of this section, we have less initial insight into ‘why’ these behaviors might occur. When we can link an individual’s behavior with other characteristics and traits (e.g. via 1st party CRM and 3rd party DMP data), and with external

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context including competitors’ actions, marketing and larger economic conditions, and other context variables, we create better opportunities to find unique patterns of influence on consumers across multiple variables that we can either react to or pro-actively address.

However, before we begin to build models to help us interpret these influences and predict the expected outcomes when these influences are altered in some way, it is typically worthwhile to conduct an explora-tory factor analysis on our set of variables to find common unobservable factors that underlie and influence the factors we can observe. By this method, not only do we shift from a focus on variables that are the results of an influence to focus on the influence itself, we also accordingly reduce the number of vari-ables we must consider in subsequent models.

8.3 Predictive Analytics & Machine Learning

In this chapter we have mined for patterns in the data via clustering and factor analysis, and created value from the data through visualization. We now take our refined understanding of our data and our ‘aha’ insights and apply these to the prediction of future outcomes. Though the importance and value of prediction to marketing efforts has been stated repeatedly throughout these pages, perhaps three key benefits bear repeating.

1. Predictions can be made during planning to opti-mize inputs to the consumer journey in terms of optimal initial allocations to channels, content and user experience as determined by a forecast of results.

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2. Predictions can be made during the delivery of the consumer journey to identify and rank pros-pects (new customer or existing cross/up sell, renewal, etc.) and their propensity to convert.

3. Predictions can be made during the delivery of the consumer journey to deliver the Best Next Experience (BNE) across channels optimized to revenue, cost per conversion or some other KPI.

Put in the simplest terms, prediction is an estimate or forecast of future outcomes based on knowledge of the past. The best way to establish these forecasts about the future is to identify factors that have occurred in the past that appear to have influenced the outcome we are seeking to predict. Thus, our factor analysis previously gives us the raw material from which we may start making decisions.

The most common mathematical approach to making predictions is via regression, which is the use of an equation to explain, or model, the interactions between independent variables in relation to the dependent outcome. The most straightforward (pun intended) approach to regression is the linear regres-sion model, which fits the mean of a predicted linear and continuous range of outcomes as closely as pos-sible to the set of observed outcomes based on the distance of the mean of each predicted outcome from the means of the observed outcomes surrounding it.

When the dependent variable is not continuous but discrete and bounded, the logistic regression may be used to transform the variable to a continuous range.

When the order in which the dependent future events may occur matters to our analysis, regression

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will not be sufficient to find the temporal or sequen-tial structure of the predicting independent variables. In these cases, time-series approaches to prediction include autoregressive models, moving average mod-els, and the combination of these such as ARIMA (autoregressive integrated moving average models). The recognition of the need for time-series analysis because of the importance of sequence in a predic-tion may and should be recognized by the analyst. For help in selecting the best model for dealing with sequence, contact your local statistician.

Any of these approaches may be appropriate for the first and second purposes described previously; to determine the optimal inputs in terms of the best forecast of outcomes (what has typically been called marketing mix modeling), and to forecast or predict the likelihood of a customer to convert given all relevant known variables.

Finally, Classification and Regression Trees (CART) analysis produces predictions using a decision tree approach to prediction. The random forest model is a common example of this approach (illustrated in Figure 8.1).

In this approach, a number of individual probabilis-tic decision trees are constructed and the mean or mode prediction of the individual trees is presented as the prediction. This model accounts much better for multiple potential next steps in a series of steps than does regression, and as such is suited to the third purpose mentioned earlier; predicting the BNE across channels at each step a customer makes optimized to a forecast of potential revenue, cost per conversion or some other KPI.

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Machine learning takes prediction from the realm of what can be conducted and interpreted by the human analyst, to rapid, iterative multidimensional processing through which a computer learns as it passes through each iteration of analysis, and applies that learning to the next iteration.

This shift in the use of machines from passively pro-cessing predictive models to actively developing bet-ter models moves us up the analytics pyramid from predictive analytics to adaptive analytics.

“Neural networks” are the coolest sounding, and also most recognized method of adaptive analytics/machine learning. This method for machine learning along with support vector machines both make use of a computational approach called “Gradient descent”.

Figure 8.1 CART

Visualization

Adaptive Analytics: Machine Learning

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Neural network models take a variety of input varia-bles and pass them through an adaptive weighting process to find the combination of adaptively weighted inputs that best aligns outputs from the model with outputs from the training data.

This approach is built on a structure of individual ‘perceptrons’, which act like neurons in a real brain, taking some input, processing it, and firing the out-put to the next neuron (Figure 8.2).

Let’s say we want to train our machine learning algo-rithm to make an offer when it recognizes a person who is an existing customer and does not currently have an item in their basket or a person who has an item in their basket but is not an existing customer. We could try to evaluate this with simple rules such as AND and OR, which will produce linearly separa-ble results. The Figures 8.3 and 8.4 illustrate what is meant by this.

Let’s place “basket” (B) and “no basket” (No B) on the X axis, and “customer” (C) and “not customer” (No C) on the Y axis. If we evaluate customers who are a customer and have an item in their basket, we get the following linearly separable result in Figure 8.3, meaning that the division between the condition that meets the criteria and the conditions that don’t can be divided with a straight line. The only true result is produced when both conditions are met.

Figure 8.2 Neural Network

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We can equally evaluate whether any given person is either a customer or has an item in their basket, with the only false result being when a visitor has neither an item in their basket, nor is a customer (Figure 8.4).

Neither of these linearly separable results allow us to recognize someone who is a customer but does not have an item in their basket, or a person who is not a customer but does have an item in their basket. Put into logical expression, such a condition is known as an “exclusive or”, or XOR (Figure 8.5).

A simple neural network will help us find this result. Figure 8.2 shows a very simple neural network pro-cessing our two inputs “X” and “Y”. In the middle layer, known as the “hidden layer”, at the top we have a perceptron processing the AND rule, and at the bottom one processing the OR rule. In this sce-nario, the “X” perceptron receives four stimuli; “Basket, No Basket, Basket, No Basket”. The “Y”

Figure 8.3 AND Condition

Figure 8.4 OR Condition

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perceptron received four corresponding stimuli; “Customer, Customer, Not a Customer, Not a Customer”. In response to evaluating for “customer AND basket” for each pair, the top AND perceptron outputs “true, false, false, false”. And in response to evaluating for “customer OR basket” for each pair, the OR perceptron outputs “true, true, true, false”. When these pairs are combined in the XOR percep-tron, the combinations are able to find the “exclu-sive or” condition: the two cases where the outputs do not match.

This is a very simple approach to a neural network. There can be, and typically are, more than two inputs. In one marketing application we might pro-vide it with a visitor’s gender, prior visit status, cus-tomer status, days since last purchase and current type of interaction. It is this ability to provide any combination of factors as input for a decision that makes neural networks so powerful.

One important element of the way neural networks work, which was not mentioned previously is the

Figure 8.5 XOR Condition

Figure 8.6 Neural Network

Decision

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weighting they provide to each exchange from percep-tron to perceptron. This weighting is how neural net-works adjust their ‘understanding’ of a combination of inputs to ‘learn’. Weighting was not shown in the example because the simple nature of the problem made the need for weights unnecessary. However, let us now consider that each of our results is being used to predict a binary outcome to purchase or not, noted as 0 or 1. Let us say that True outputs from our hidden layer are given a value of 1, and false outputs are given a value of 0. Finally, say that each output from the hid-den layer carries a weight of 0.5. Let’s look at our out-puts in Figure 8.7, and add the actual outcome for each case as well.

We see that with the model, our prediction was cor-rect in one out of four cases, and halfway correct in another. With this information, our model could adjust weights. In this case, for our understanding, we will adjust only the weights shown, but an actual network would adjust weights from the inputs to the hidden layer, and would contain an “error” or “bias” perceptron, the weight of which could also be adjusted. For our purposes, this network would likely adjust the weighting for the AND output down, and the weighing for OR up. If they were weighted (.75, .25) then the output would be (1, .75, .75, 0). We have now improved our prediction for the second actual occurrence, but we have also increased our error. To fix this error, the network would weight the

Figure 8.7 Weighted

Neural Network Decision

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input into the hidden layer, giving higher weight to the “X” inputs than the “Y” inputs, and would con-tinue reweighting at every layer of the network until the predicted outputs were optimized in alignment with the actual outcomes against which the network was being ‘trained’.

One of the most popular methods by which neural networks and other machine learning algorithms ‘learn’, e.g. adjust the weights of each connection, is an optimization method called gradient descent, through which local minima (for an optimized cost function or utility function) are established.

Figure 8.8 represents a multidimensional “decision space” — capable of taking on any number of dimensions. In the diagram, the algorithm has set three starting points then evaluated the steps needed to take to move from each starting point (error, shown by the height dimension) to the local opti-mum (the point at which a single step in any direc-tion will not lead to further decent).

Neural networks ‘learn’ or receive training via gradi-ent decent algorithms, which adjust the weights in

Figure 8.8 Decision Space

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Historically, “propensity modeling” is a form of “lead scoring”, meaning an evaluation of the expected value of a customer at some point in the sales pro-cess. Such scores are used to evaluate leads from sales lead generators, such as online affiliates or lead aggregation sites, email lists, and directly received leads through landing pages.

Dynamic propensity modeling provides the mar-keter (or marketing system) with a prediction of whether (and how much) engagement in any given channel in which we find a person will increase the probability that a person will convert against each next micro-objective, and how each conversion will increase the likelihood of that same person ulti-mately converting against the macro-objective. It can also determine what creative/UX option amongst many would be most effective in that given channel, should we chose to engage. This is essentially similar to the decisions we are making in programmatic (RTB) ad buying, where we consider how each given

the hidden layer via gradient descent until each node in the hidden layer has found the weighting which works in conjunction with weighting in every other node in that layer to the optimal reduction of error in the model.

Machine leaning algorithms are useful and used in the dynamic delivery of content, whether within pro-grammatic targeting and retargeting or personalized user experience delivery.

Applied Machine Learning/Adaptive Analytics: Dynamic Propensity & Targeting

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impression will potentially impact our sum outcome, and weigh that against the impact on our sum cost.

A propensity model seeking a higher degree of accu-racy will also augment simple frequency derived probabilities with context related to the value and nature of purchases, observations of time-based influence from seasonality to time of day, geographic and demographic data about the customer, and of course all other context derived from the integrated view of the customer we created in the previous sec-tion. The prediction of such a propensity model will be derived from not just the expectation of the value of the channel itself in driving results, but the expected value of that channel in driving results for a person like the specific individual in that channel at that moment. For example, we might see that calls to the call center have typically converted much higher for new customers who have previously used a tool on the website versus those who have not, while calls to the call center have tended to end with-out purchase for current customers who were look-ing to upgrade their service, and also used the tool, but did not complete the transaction online.

With the vast number of variables that can uniquely describe a customer, their interests and their prior engagement with our brand, propensity models must sort through a large set of possible relation-ships between persona, history, channel/content/UX, and possible outcomes to find the statistically relevant relationships, but once those initial rela-tionships have been found, the model can continue to learn and dynamically adjust the propensity or probability that any given customer will convert, and the expected value of that conversion, with each new

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piece of information gained about that customer. With that dynamically changing insight into whether a customer is likely to convert also comes the under-standing of what content in what channel would make them more likely to convert. With that under-standing, we can turn to marketing automation soft-ware to help create the most relevant and effective experience for each customer at each contact we have with them.

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221


The Cultural and Organizational Impact of Data

Analytics is more than just data. Great analysis requires transformation of data to information, of information to knowledge, and hopefully, over time, of knowledge to wisdom. This final chapter is an attempt to share perspectives on our society and the culture of businesses as new sources of knowledge which will hopefully be transformed into wisdom around the practice of analytics.

ChapterNINE

9.1 Visualization

The purpose of analysis is to identify patterns and provide explanations for those patterns in ways that inform actions to optimize desired outcomes. Given this purpose, it would seem that really good analysis should provide readers with a quick understanding of an important pattern, clear insight into key driv-ers of the pattern, and immediate guidance in how to respond to this information.

Visualization of data and analysis taps into the human hardwiring to respond to visual stimulus and therefore provide a potentially powerful approach to establishing and interpreting patterns. Unfortunately,

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this powerful potential is not always realized through visualization, as visualization efforts often have an unfortunate tendency to become more about form than about substance. Often this is due to an absence of substance in the data itself. For example, social media dashboards provide clear visualizations of age and demographics around engagement with con-tent, but ultimately, there is little action that can be stimulated by this visualization. In other cases the triumph of form over substance in visualization arises from the temptation offered by tools like Tableau to present data in an array of different for-mats. While experimentation with certain visualiza-tion methods may help to reveal data in a clearer way, the options available may also tempt analysts to try and force data into a visualization for the sake of dressing up a dashboard or report. In his 1983 book The Visual Display of Quantitative Information, Edward Tufte referred to such dressing up of data as “chartjunk”, noting that if the purpose of a visualiza-tion is not to reveal new insight that could not be reached through simpler representation, then the simpler representation of data is probably better.

A formula for the value of visualization which can be helpful in guiding decisions around data visualiza-tion efforts to avoid the manufacture of chartjunk has been presented by John Stasko, a Professor in the School of Interactive Computing at the Georgia Institute of Technology. Professor Stasko proposes that the value of visualization (V) is as follows:

V = Time + Insights + Essence + Confidence

This model asks analysts to consider four key factors in deciding whether a visualization approach is the

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right way to present your data. The first value in this model is Time, or more precisely, the extent to which a visualization can help reduce the time required to answer a range of questions about the data. In a con-ference presentation in which this model was pre-sented to the public1, Stasko lists the types of tasks whose effort can be reduced via visualization includ-ing retrieving values, sorting and applying filters, calculating values, determining range and distribu-tion, exposing anomalies, and illuminating clusters and correlations.

The second value in the model is Insight, the delivery of which is of course the primary purpose of any type of analysis. Professor Stasko observes that good insights can be characterized by the extent to which they are complex, qualitative, and perhaps most importantly, unexpected, or challenging to prior conceptions and understandings, providing an “aha” moment around a problem that may have seemed inexplicable or impenetrable prior to the insight.

Next in the value of visualization comes the extent to which the visualization captures the Essence of the data. It would appear that an ability to distill a num-ber of variables or factors into an essential picture would result from a maximization of the time ele-ment of this equation, and would be a key driver of the insights that could be drawn from the visualiza-tion. The chief take-away here is that to capture the essence of the data, the visualization must provide both an understanding of salient details along with a view of the big picture. There is a tendency for ana-lysts to begin ‘drilling down’ for insights, and to drill so far that context around the analysis becomes lost (e.g. finding a pattern in the movement of a single

1 The slides can be found

online. The conference was

the 2014 Eurographics

Conference on Visualization.

See Further Reading for

the URL link.

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stock without considering movement within its industry, or the market at large). The capability to deliver “aha” insights comes from providing the big picture within the data and a detailed cross-section at the same time.

The last factor in this equation is the Confidence that the visualization inspires, as clearly the visualization will have no value if its readers do not trust the data or the analyst. Trust in an analyst is established over time, and trust in data typically follows trust in the analyst. With that said, any time a visualization or analysis of any sort challenges pre-conceptions, it stands subject to doubt. This creates a conundrum for the analyst; our primary objective is to create “aha” moments giving unexpected insights from the data which challenge preconceptions, but we must recognize that challenging preconceptions can create doubt that must be overcome through a method of presentation that establishes confi-dence. The analyst who creates “aha” insights but does not recognize the importance of confidence in analysis and the effect of challenges to the status quo on confidence may develop amazing insights that no one uses, which is an unfortunate position to be in. Balancing the insight produced with a rec-ognition of the response it will receive and a plan to address questions of confidence rising from that response will be able to continue delivering unex-pected insights that people will welcome and act upon.

An example of the wrong way to present visualiza-tion with devastating consequences comes from the tragic Challenger Space Shuttle launch. Setting aside the handwritten nature, the visualization below

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violates several of the variables in the V = T + I + C + E approach to visualization.

Figure 9.1 shows a visualization produced by a group of engineers seeking to cancel the launch of the Challenger space shuttle in the lead up to that tragic, fatal launch. The engineer’s valid concern was that a part called an “O-Ring” had been known to malfunc-tion at lower temperatures, and the temperature at launch was expected to be low enough to cause a malfunction. The engineers conducted their analysis and produced the results shown above. As we know, this information was not enough to convince the launch managers to delay the launch, and on January 28th, 1986 the shuttle blew apart 73 seconds into its flight, killing all seven of its crew.

Figure 9.1 Challenger Pre-launch Analysis

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This is a case in which the value of visualization was critical. Granted, in 1986 the engineers did not have the benefit of the data visualization capabilities we have today, but let’s evaluate where the value of their visualization was diminished such that their warn-ings were not conveyed as well as they might have been.

This analysis does fulfill the Time requirement for value in visualization, saving the reader from the requirement of pulling and sorting the history of O-Ring damage for themselves. Where the visualiza-tion fails is in its conveyance of Insights and Essence. The key insight that might have stopped the launch would have been an analysis of how extensive O-Ring damage could lead to the entire craft exploding, and a prediction of the likelihood of O-Ring damage given the temperature. The insight delivered in the analysis above could be argued to show that more O-Ring damage has occurred at temperatures above 60°F than below 60°F. It also could be argued to show that launches can be successful even with O-Ring damage. Thus, the insight that was meant to challenge the status quo was not effectively delivered through this visualization. Additionally, this presen-tation fails to convey the essence of the data. It does convey the breadth of the data, including the super-fluous data point “wind speed” in the analysis, but in conveying all of the picture, it fails to put the salient details in the context of the most relevant aspect of the big picture; the extreme different in damage caused at the temperature below 55°F compared to damage caused above that temperature, and the lack of experience with a launch at temperatures below 50°F. By even using just an ‘average’ degree of dam-age at each decreasing 5°F range instead of showing

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all damage rankings, the increasing level of damage as temperatures dropped would have been much more apparent, and the lack of data at the range in which the launch was taking place might have been much more apparent.

The engineers who prepared this visualization tried their best, and I credit them immensely for their effort. Their ability to establish the Confidence value for their visualization was beyond their control due to filters and biases in their organization. Some very common drivers of organizational “groupthink” — familiarity, social proof and escalation of commitment — were amplified in what was characterized as “Go Fever” sur-rounding this (and every other) impending launch. Cutting through this Go Fever would have required a heroic effort at data presentation that unfortunately these engineers did not have the time or tools to deliver.

Figure 9.2 John Snow

London Cholera Map

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Flipping now to the other side of the coin, we will go even further back in history to look at what is widely considered to be the grandfather of all visualizations and an example of value in all four areas; a map of a cholera outbreak in London produced by John Snow in 1854 (Figure 9.2).

John Snow was a doctor in London when a Cholera outbreak struck the city in 1854. At the time, the cause of Cholera was still unknown, with many dif-ferent (incorrect) theories existing around how diseases spread. Snow went to the impacted area and began to collect data, and his analysis of the data helped him determine that the common fac-tor amongst all of the cases he observed was that they had all consumed water drawn from a single well (on Broad Street). He quickly conveyed his findings to authorities who removed the handle from the well. After his analysis had been responded to, Snow created the above illustration to visualize his analysis. Clearly, this visualization provides value around time saved in data collection and analysis. The insight is conveyed through an effective cap-turing of the essence of the data; showing the details of cases in the context of the bigger picture map of the surrounding area. And as evidenced by the fact that Snow’s analysis was acted upon ever before the visualization was produced, it seems that consumers of Snow’s analysis had great confidence in him.

The implications of the approach taken to visualiza-tion are clear. Valuable visualization saves time in processing data, conveys an unexpected insight, cap-tures the essence of a large data set by showing detail in the context of the big picture, and does so in a way

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As discussed above, information is powerful when it is visualized, and it may be even more powerful when it is felt. In the first chapter of this book, you were introduced to Marshall McLuhan’s concept of hot and cold media, which implies the power of media to change the environment around it in a sensorial way. But beyond the senses, McLuhan’s theory is one of the impact of temperature change on the environ-ment, specifically in terms of expansion and contrac-tion; some new communications media cause culture to ‘heat up’ until either some aspect of culture ‘melts down’ or a countervailing cool medium emerges to cool things down. But what does it mean to ‘heat up’ and ‘cool down’?

A “hot” medium is one that engages multiple senses as completely as possible, and requires very little ‘reading’, interpretation or completion by the receiver. McLuhan observes that a completely hot medium essentially evokes a type of hypnosis in the receiver, and offers early big-band jazz with its intri-cate composed and conducted scores and its associ-ated Jitterbug dance as examples of “hot” media. Conversely, a “cool” medium is one that carries very little information on its own, and requires the receiver to interpret and/or complete the message. McLuhan proposes that a completely cool medium would evoke pure fantasy/hallucination, as the receiver must provide every aspect of information.

that instills confidence, while less valuable visualiza-tion misses on one or all of these points.

9.2 The Information Society: Media Cycles & Feedback Loops

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As an example of less extreme “cool” media, McLuhan suggests the evolution of jazz into its “cool” stage (yep — you’re seeing the cycle emerge), with its freeform flow and long, improvised solos, and the emergence of unstructured dances like the Twist, which did away with the intricate coding of the Jitterbug and let dancers improvise as they liked, bringing their own interpretation to the music. Mirrored sunglasses have always been “cool” as a communication medium because they require the receiver to complete the face/expression of the wearer, while the latest fashions (the tighter the bet-ter) are always “hot” because they add information about the wearer (e.g. that they have ‘currency’ in physical, temporal and financial terms).

McLuhan’s book offers an incredible array of exam-ples into the cultural impacts of 20th century hot and cool media, so I encourage you to add it to your reading list. What these examples illustrate very con-vincingly is that ‘the medium is the message’. Perhaps you’ve encountered this phrase before and were possibly aware that it comes from McLuhan. Taken without the context of the book, it still intrin-sically makes a certain kind of sense, but it makes much more sense when considered in the context of media “hot” and “cool”. In this context one can observe that if effective sustained communication requires the constant interplay of “hot” and “cool” media to keep a balanced temperature (to avoid cultural melt-downs and freeze-ups), then ‘the mes-sage’ embedded in any medium is its cultural ‘tem-perature’; does it shout or whisper? Does it hypnotize or cause hallucinations? Does its temperature depend on complimentary or conjoined media?

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McLuhan observed that broadcast television was pri-marily a hot medium, with its rapidly paced plots and pacing, its laugh-tracks and scores, and its inter-spersed intense bursts of advertising providing strong emotional cues and leaving no time to consider what’s just been seen or what’s coming next. This notion of television as a ‘hypnotizing’ force seemed to resonate, becoming a very strong cultural meme that persists today. Unfortunately, McLuhan died long before the internet took its current form and social networks created a new form of cultural inter-action, because his take on these new media would have certainly helped us understand not only just how technology is shaping society, but more impor-tantly for the innovators among us, what is likely to come next in terms of communication technology based on the culture’s need to ‘heat’ or ‘cool’ what has come before. While I certainly cannot claim McLuhan’s insight, I can attempt to extend his framework against the media that have emerged since McLuhan’s last observation.

Figure 9.3 illustrates the feedback and cycles between various digital media over the last 20 years.

Email (Cold) At the beginning of what has become our contemporary digital media landscape, the early internet was a very cool medium. Everything was text based, and communication was not nearly as instan-taneous as it is today, messages were posted on ‘boards’ or sent via email and typically waited for response until the receiver accessed the board or account and saw the message. While it was possible to share files, nothing could be viewed immediately, but required download (very slow over dial-up

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modems) and frequently also decompression. In short, the reader had to bring a lot to the medium to get to the meaning in any message.

HTML (Cold) offered a profoundly new method of non-linear writing, but despite its radical departure from linear writing, it was still primarily about writ-ing, and the prodigious use of links on almost every word in early HTML coding meant that readers still had to do a great deal of work to uncover the full meaning embedded in an HTML-based medium.

Browser (Hot) It was the Mozilla browser that helped the web ‘heat-up’ in the mid-1990s. The Mozilla browser allowed multi-media elements to merge with the written aspect of HTML, and guided web devel-opment away from the early practice of dispersion through links and more toward emersion through ‘content’.

Broadband (Hot) The immersive nature (and ‘hot-ting-up’) of the web was exponentially expanded with the introduction of broadband/‘high-speed’

Figure 9.3 21st Century

“Hot” and “Cold” Media

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access and the emergence of animation and stream-ing audio. It is really no coincidence that this hot new communication technology became so over-heated as to create a “bubble” in internet develop-ment, which then burst in 2000–2001 (the dot-com bubble).

Mobile and Text (Cold) Meanwhile on a separate technological track, mobile technology was develop-ing in a “cool” state. The emergence of smaller and smaller phones with added functionality like con-tacts memory, voice dialing and most importantly text messaging was a very cool development for everyone who lived through it. Communication via voice and text were both cool in McLuhan’s terms as well; conversations were frequently lost due to spotty service and really lame battery life, and text messages were written in entirely abbreviated forms, requiring readers to complete every word with omitted vowels, syllables, grammar and punctuation.

Blogs (Cold) As the dot-com bubble burst, a cooler form of web communication, blogging, emerged to fill in the void. In its initial form, blogging was a very text-heavy medium built around a single page, quite a cool-down from the multi-media, multi-page web-site that had just melted down. In contrast to the emergence of digital specialty firms during the dot-com bubble, blogging brought a DIY ethic back to the web, allowing anyone and everyone to communi-cate anything at any time, a characteristic which lent itself (legitimately) to comedic (and occasionally tragic) criticism of the practice. While some blogs (e.g. Drudge Report, Huffington Post) took an approach that eventually led them to more legitimate media status on par with the digitized print outlets (e.g.

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NYT.com), the “Blogosphere” itself was (and is) a largely cool medium (as anyone who attempted to mine meaningful data from it in the early days can attest); a universe of poorly-written and partial com-munications or completely abandoned efforts with huge vacuum-filled gaps in-between.

P2P/Torrents (Cold) At this point in the early 2000’s as a new DIY ethic emerged from the excess and col-lapse of Web 1.0, peer-to-peer (P2P) file-sharing emerged as the primary point of user activity at the center of the emerging new web. Napster, a music file-sharing service was the poster-child for this new generation of software and social interaction, but sharing was not constrained to Napster. BitTorrent software turned home computers into networked file-servers, and in conjunction with home broad-band, these file-sharing applications transformed the internet from a text-delivery system into a true media platform which made previously platform-constrained and copyright protected content sud-denly ubiquitous. The nature of the technology is itself the very description of a cool medium, frag-mented pieces of content (bit packets) being assem-bled by the receiver into a cohesive whole.

Social Networks (Hot) With blogs leaving much to be desired from an interactivity standpoint, and with content sharing becoming the centerpiece of the new web, the cool vacuum of the post-dot-com web needed some heating up, making the environment perfect for the introduction of platforms like Friendster (2002), MySpace (2003), and ultimately Facebook. These platforms simplified posting, (low-ering the expectations around posts since a sentence could suffice where blogs had seemed to want

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paragraphs), allowed multi-media to be easily and seamlessly integrated (reducing the complexity of installing and running torrents, though also reduc-ing access to copyright-infringed content) and most importantly, offered ready-made audiences through the capability to find and add ‘friends’ or connec-tions who were willing to receive posts and contrib-ute their own in return, both through the established network members as well as the drastically improved personal SEO which these networks provided (mean-ing Google had something it could find and show under a search on a person’s name).

YouTube (Hot) The ‘re-heating’ of the web reached a fever pitch with the expanding popularity of what was initially known as “vlogging” (for video blog-ging), and the emergence of YouTube (2005) as the center of the web’s video universe. Critics derided the channel as just another vanity outlet that would shrivel up and die as the novelty wore off and people tired of dealing with video quality that was pretty bad by any standard, and the long wait times or frequent interruptions while video buffered due to the lower bandwidth connections of the time. Instead, within a short period of time, video quality improved rapidly, access to better connections expanded to a broader population, and most importantly, people went crazy for a type of video content that wasn’t available any-where else.

YouTube was the birthplace for two concepts that digital natives take for granted today. The first is “going viral”. Something about YouTube videos com-pelled people to share them with friends and acquaintances, and the most broadly (and quickly) spread videos were considered to have gone “viral”,

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spreading from person to person until everyone has caught the fever (see Numa Numa [2005] for pre- and early YouTube viral video and Gangnam Style [2012] for something more recent).

The second concept spawned by YouTube is the widespread recognition of the “meme”. The meme is a concept introduced by Richard Dawkins, who ear-lier in his career had established the concept of the “gene”. Where the gene is a part of the assembly instructions for a living organism which carries instructions for how to assemble the building blocks of life, a meme is similarly a part of the blueprint for a culture, providing the building blocks of culture (individual minds) instructions for how to assemble together a common way of seeing or interpreting things. The “virality” of certain videos on YouTube (and subsequently everything imaginable, from ‘LOL cats’ to my personal favorite so far, ‘Sad Keanu’) exposed the reality of the meme — a theme or concept that not only required some cultural knowledge for interpretation, but which also shapes or evolves a culture (or more correctly sub-culture) through its existence.

Clearly, the ability to go viral and create a feverish response to content that actually shapes culture belies the “hotness” of this new medium, which when coupled with the heat of social networks could no longer be contained on laptop and desktop computers.

iPhone (Hot) Enter the iPhone in 2007. The iPhone channeled the heat from social networks and video away from the bounds of the computer monitor and into a handheld format allowing the

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hot and viral content of the Internet to be accessed anywhere, at any time. The iPhone also marked the jump of mobile technology from cool (voice and text) to hot, and engendered the heating up of texting itself in the form of Twitter, a development that triggered a new cooling phase of digital technology.

The New Digital Primitivism, or The Rebirth of Cool

McLuhan observes that the aural and visual storytell-ing medium of pre-literate cultures was an inher-ently cool medium, requiring the listener to add their own interpretations to the images being painted by speech or drawing. By 2007, the heat generated by the first (primarily hot) 15 years of expansion in digital media had jumped the bounds of even laptops and was now completely mobile and univer-sally accessible. With a web of highly produced sites and social networks with content that merged audio, video, text and image to convey entertainment, information and advertising, a simpler, more basic and human form of digital engagement was bound to emerge.

The first 15 years of digital expansion had also engendered a significant cultural change; the com-ing into being of digital natives, children of the developed world who since their first encounter with technology had known nothing but Facebook, YouTube, Torrents and smartphones. More than any-one else perhaps, the Digital Natives who had been born and raised in the feverish environment of digi-tal expansion were in desperate need of some kind of cooling counterbalance in their culture. In fact,

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since the turn of the century, the most distinct sub-culture amongst millenials has been Hipsterism, or the pastiche of ‘retro’ (or more ‘primitive’) cul-tural modes of cool based primarily on beatnick (1955–65), 80’s pop, and grunge (1990s) fashions in clothing, music and entertainment.

Twitter (Cool) Into this environment comes Twitter. The origin story of Twitter emerges from the center of digital ‘cool’, the South by Southwest Interactive (SxSW) festival in Austin Texas. Less than a year old in March of 2007, Twitter’s founders saw an opportunity to build a highly influential and technology-literate software evangelists, and by promoting their ‘one-to-many’ messaging concept around this conference, saw their usage increase 200% over the course of a week. The Twitter experience is now well-known, 140 character messages that could be sent either to indi-viduals (like a traditional text message) or to groups of ‘followers’. During the 2007 conference, Twitter was rapidly adopted attendees to build their per-sonal social network without having to enter details into a contact managers or pocket a business card, since following and being followed was made easy by Twitter. And Twitter was a natural for sharing quick updates on what was happening around Austin, both in conference sessions, and more importantly, around the parties that were happening outside of the conference.

Like texting, Twitter is a cool medium in that messages must be abbreviated and condensed. A raw Twitter stream is as close to chaos as a communication medium is likely to get — a rapidly refreshing stream of random and unconnected missives from a pool of contacts without any overarching categorization and

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sorting structure in place. As a means of creating some order from the chaos, Twitter’s users developed their own structure with the introduction of the hashtag (#) to indicate the tweet’s topic or category, and Twitter’s engineers responded by generating auto-matic links on hashtags that search for all other tweets also carrying that hashtag.

Twitter’s emergence in 2007 is inextricably linked with the iPhone’s own rapid adoption and the subse-quent interest in location-based communication. With mobile technology and a simplified communi-cation medium, people seemed naturally inclined to share details about where they were and what they were doing at any given moment. However, despite its growth and popularity, Twitter usage still remained a bit of a subculture, with tacit and explicit rules about correct forms of engagement and interaction, and with a strong aversion to intrusion by corpora-tions looking to tap into a highly influential audi-ence. As a cool medium, getting engaged and staying engaged in Twitter required some effort. Many peo-ple, especially younger digital natives found it much more difficult to build their network in Twitter than in the hot Facebook network (especially since most of their friends didn’t see what Twitter could give them that Facebook already didn’t). Once on Twitter, separating the wheat from the chaff was difficult, and being as cool a medium as it was, staying engaged with followers seemed to demand a high-degree of response. So for the digital natives, Twitter as the first wave of the new ‘cooling’ phase was not as natural to adopt as it was for the attendees of the SxSW confer-ence in 2007 who likely skewed to Gen-X who had been engaged with cool digital media before.

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Tumblr (Cool) Despite the weak attraction to Twitter as the first of the new cool media, millennial digital natives were still unknowingly ‘burnt out’ on hot channels and in need of a cool escape. By April of 2007, in a sure sign of needing to escape from the hot expansion of the media, vintage hunting and retro-worship had become the core of ‘hipster’ iden-tify. In that same month, digital media felt its first cooling wave in the form of Tumblr. Like Twitter, Tumblr is considered a “micro-blogging” platform in that it is designed specifically to allow users to share content and interests in small snippets. However, unlike Twitter, but very much in keeping with the emergence and appeal to the natives of a more primitive digital communication approach, Tumblr is used primarily to share visual content rather than text. Adding to the cool component of Tumblr and the primitive tendencies it generates, a good num-ber of the most popular Tumblr sites are dedicated to user generated “photo-shopped” pastiche images which photo-realistically portray imaginary or fantasy scenarios. The use of (as realistic as possible) visual imagery to generate humorous or serious allegorical or metaphorical reflections on reality has been a common cultural thread from the most primitive to the most cultured, and the visual medium still typi-cally has a more effective and immediate impact on the receiver than does the written word. A picture still speaks a thousand words, and in conjunction with their fluency in cultural memes, Tumblr has allowed digital natives to express complex and pro-found intellectual and emotional positions and views through snippets of imagery and text.

This trend to the visual will not be confined to mil-lenials in the developed world, but will expand

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across societies and cultures and into subsequent generations. Dr. Don Schultz of Medill IMC who travels broadly through the developing world shared with me his belief that the refinement of methods for the expression and reception of ideas through images (along with ubiquitous mobile accessibility) will more broadly open the digital world to societies with large-scale illiteracy. In these cultures, image-based digital literacy will emerge as the prevailing common literacy, while traditional text-based literacy will stagnate further. The extension of the digital world, digital creation and digital commerce to these developing nations can have a have profound impact on current structures of economic and social stratifi-cation both intra- and inter-nationally.

In the developed world, textual literacy will continue to be a focus of general education as written com-munication will never be fully replaced as a source and conduit of power. Undoubtedly however the nature and content of writing and the standard for literacy will change with subsequent generations, so that the definition of textual literacy to the graduat-ing class of 2033 will be evolved from yet distinct (through both additions and subtractions) from what is considered literacy in 2013 (just as graduates in 1993 in meeting their standard of literacy had been through handwriting classes back in grade-school and BASIC programming language courses in middle-school that 2013 grads likely never encountered).

Last.fm and Spotify (Cool) What will be common across generations in 2033, as it has been through human civilization, will be the appeal of music. Back here in the first decades of the century, the new

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primitivism and cooling of digital culture was extended in 2008 with the introduction of the Pandora mobile application, which suddenly gave smartphone users free and legal access to a library of music ranging from classical to the newest rap music. This service, which spawned follow-up mobile apps from similar services like Last.fm and Spotify, deliv-ers volumes of music on a mobile device that could only have been dreamed of at the computer down-load speeds, memory constraints and copyright infringement concerns of just a few years earlier.

What was more unique about these new music shar-ing services than their licensing agreements and technical architecture however was their foundation in social networking. More-so than Pandora, Last.fm and Spotify made sharing music and comments across a social network a key component of their platform. Spotify’s sharing architecture, including shared playlists and ability to share and comment on songs or albums across friends is helping quickly erode the lead of earlier platforms.

Pinterest (Cool) The next popular product of the cool digital wave was Pinterest, an almost entirely visual social network where image sharing by cate-gory or interest is assumed, and text is optional. The layout of Pinterest is quite elegant and translates nicely to a tablet device (i.e. iPad or Kindle). From first use, Pinterest’s user experience is highly intui-tive and guidance for new users is visually driven with subordinated text.

Snapchat (Cool) Snapchat is a logical spawn of two cool media; the digital image and texting, and its rapid adoption by digital natives in the 13–23 age

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range illustrates the extent to which cool digital media are the preferred form of engagement for the coming generation of consumers that marketers will address. Snapchat is cool taken to its extreme in digi-tal media, It is the anti-Facebook: an image of a sin-gle point in time that exists for only a short time for a limited set of viewers before self-destructing. No timeline, no history, no record of any communica-tion — just an instantaneous exchange that disap-pears to make way for the next.

Pinterest and Snapchat will not be the last catalysts for the cooling of digital culture. If Dr. Schultz is cor-rect, then the ‘primitivization’ of culture through digital media content still has a very long lifespan ahead of it. At the same-time, hardware is already igniting a re-heating of the digital space as we develop ever more integrated, constant and ubiqui-tous digital interfaces.

iPad (Hot) The iPad was the first spark in the reheat-ing of digital media following the long cooling period that followed Facebook and the iPhone. The iPad shifted all media into the mobile space, and shifted culture’s expectation of the type of digital experience that could be access and consumed while away from a computer. It also revolutionized the human-computer interface by making touch interactions so common that any person under five years old living today probably first tries a swipe motion to interact with any screen they encounter. The integration of touch-screens into service envi-ronments (bank, airport, dining, etc.) show another fork in which public screens will be widely available for short context and transaction based digital interactions.

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Google Glass and Occulus Rift (Hot) Google Glass extended the evolution of the screen and human-computer interaction with a screen that was con-stantly affixed to the personal view, providing real-time contextual information to the wearer while recording the wearer’s context. Though at the time of publication, Google Glass is undergoing a meta-morphosis, the 2014 purchase of Occulus VR by Facebook shows how a classical hot media (Facebook) envisions hot digital media expanding into the realm of fully immersive virtual reality, which will likely allow technology to fulfill McLuhan’s observa-tion that the ultimate hot media will create total emersion to the point of hypnosis.

Application to Digital Strategy

The brief insight into the history of digital technology in the context of hot and cold media should make it clear why effective 21st century digital marketing requires an applied understanding of the “producer” mindset of much of the digitally native target popula-tion, and the “temperature” of the various digital channels through which this population can be engaged. Ineffective digital marketing wants to force hot content like buy now messages through ‘cool’ channels like Twitter, Tumblr and Pinterest. Effective digital marketing understands that ‘the medium is the message’, that cool media require cool content, and recognizes that its social network wants to be involved in content generation within the medium.

As engagement with digital interfaces and content experiences becomes ever more ingrained in cul-ture, the trade-offs and balance in preferences for hot and cool communications will always be

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maintained and expressed through the behaviors of media consumers. An ability to anticipate these pref-erences by understanding and anticipating the expansion and contraction of hot and cold media amongst various elements (or segments) of culture will be essential to digital media planners, communi-cations strategists, experience designers and the technologists who develop and evolve the channels that deliver these hot and cold digital experiences.

As mentioned earlier, mass media is dying, and subse-quently all media is becoming personal media, allow-ing each media consumer to create their optimal mix of hot and cold media. Media mix modeling is now done just as actively on the consumer side as it is by brands (perhaps more actively), meaning brands are facing thousands of variations of potential media mixes through which to build their relationship with their target consumers. The ability to effectively lever-age the right combination of technologies and con-tent to build and maintain brand relationships with digital natives requires an understanding of who they are, where they are, what media they engage with, why they consume that media, and why they should be interested in having your brand involved in their personal marketing media mix. This understanding of what is relevant to your target consumer requires data and analytics, and the scientific approach in the collection and application of data.

Digital Analytics as a Medium

Before moving on though, we must consider one final medium in our analysis of 21st century media; the medium of digital analytics itself. Like any of the

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media previously discussed, and perhaps even more-so in this case, analytics are an extension of our senses and a ‘mediating’ channel between our senses and the outside world. Analytics allow us to compre-hend the reality of situations at a level of detail that would otherwise be impenetrable by our senses and our thinking.

We have already said in this chapter that analytics create relevance, and that relevance delivers results. As with any medium, the results of analytics are con-tingent on the nature and quality of what passes through them, and how the results are made mani-fest. Data is the source medium of analytics; the better the data, the better the analytics, regardless of the way in which they are presented. It is not always safe to assume, but for the purposes of this thought exercise, let’s assume we have good data, our interest in discussing analytics as a medium is in examining the ways this data can be presented as analysis.

The most common method for the presentation of analytics in marketing is through a spreadsheet, either alone or in combination with a ‘report’ in PowerPoint built from the spreadsheet data. This is an ice cold manifestation of the analytics medium. First, spread-sheet analytics tend to be conducted on a scheduled cadence, which means that by the time the analysis is produced, it is looking at data that tells a story about a moment in the past, but is no longer completely current. Second, there is nothing at all immersive about these presentations. With PowerPoint, there is no chance for the information consumer of analytics to ‘dive deeper’ into the data — what you see is all you

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get. With spreadsheets, it is possible for the informa-tion consumer to warm up the medium a bit by dig-ging past the initial presentation of charts and graphs into the data itself, but not without first becoming an expert on the structure of the file and the location of reference data. Spreadsheets do not pull most people in — rather they tend to require some effort in digging.

The next most common method in the presentation of analytics in marketing is through dashboards, which warm things up a bit compared to spread-sheets and print reports. First, dashboards allow for the presentation of much more current or even real-time data. Second, dashboards allow the infor-mation consumer to dig through the initial presen-tation of data to deeper levels of analysis along a multitude of trajectories limited only by the bound-aries of the data. Thus, good dashboards can be a warm or even hot medium for analytics, but this is determined by the quality of their design and the comprehensiveness of the data in the context of their intended analytical purpose. When the analyt-ics allow better informed decision making, and the decisions made can confidently be put into effect and optimized at the speed at which the analytic insights are updated, we have a hot system. Typically though — in a problem that will be discussed in detail in coming chapters — the data being fed into individual dashboards still does not provide all the information needed to confidently make decisions. Furthermore, even when decisions can be made from dashboard analysis, organizations have yet to evolve their management practices to work at the speed of digital analytics, so the application of

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decisions is delayed until the basis for the initial analysis becomes cold and the decision may no longer be relevant — an issue which will be dis-cussed further in section 9.3 on organizational change for data-driven marketing.

The hottest form of marketing analytics is the “pro-grammatic” medium. Here, there is no human interpretation of the most current data, except through pre-established code designed to let com-puters analyze large amounts of data to instantane-ously make and act upon decisions that would take people hours, days or weeks to accomplish. The heat of this medium is precisely in the immersive nature of its outcomes — when computers are con-tinually evaluating a consumer’s context to deter-mine and deliver a relevant marketing experience, the result is the presentation of a more highly per-sonalized media experience, and the subsequent immersion by the media consumer in that experi-ence. Here, there are fewer generalizations that don’t quite fit the media consumer and cause them to step back. Instead, there are better predictions about what will be compelling and motivating that cause them to lean in further. And as they lean in and engage, a learning feedback system is created, with the analytics program ingesting the nature of this engagement as more data, and correspondingly adjusting the decisions it makes around subsequent engagement with this person based on prior interac-tions. (If you’re thinking this sounds like Artificial Intelligence, you’re right.) This hot form of analyt-ics is the type at play amongst today’s leaders in analytics (and AI) — Facebook, Google and Amazon chief amongst them, with Netflix, Spotify, and a flood of rapid innovators behind them.

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The hot programmatic application of analytics and artificial intelligence to experience creation is the ultimate objective of most digital marketers, whether they recognize it or not, and thus, it is the topic towards which the subsequent chapters build. So, to close out this chapter, we should apply our under-standing of hot and cold media to a projection of where the analytics medium is headed.

As more and more media producers (marketers and their brands included) achieve the goal of program-matic application of analytics to experience, the media environment for consumers will become hot to the point of being supercharged. A personaliza-tion and programmatic bubble will emerge, with a resulting rapid cooling ‘contraction’ to follow, which will ultimately consolidate the data and technology for personalization amongst a few central sources. This prediction of the eventual contraction and con-solidation of highly personalized consumer insights and their application to experience delivery is not meant to discourage marketers from making the effort to establish marketing science and digital ana-lytics capabilities now. To the contrary, the under-standing of how to build programmatic experience delivery and real-time analytics as a foundation for business is critical to any company wishing to grow through the 21st century. Right now is the time for organizations to learn how “hot” programmatically applied analytics can transform their organization, and to begin the transformation to the type of data-driven and carefully programmed experience deliv-ery that will separate the firms that dominate the economy of the late 21st century from those that fail to adapt to the power and speed of digital informa-tion and thus disappear over the coming decades.

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The underlying purpose of this chapter up to this point is a belief that a deep understanding of the influence of digital on culture (and the consumers in it) is a requirement for companies that wish to thrive through the 21st century. However, even more important than the possession of this understanding itself, is the ability for companies to act on this understanding. It is likely that most every company doing marketing today feels a need to be ‘data-driven’ in their activity; I hear this from my largest clients to my smallest. In this pursuit, it is important that companies understand that becoming data driven is a process of change, and that change is often difficult. Change is difficult because it takes individuals and organizations into the unknown — it requires new thinking and new behavior — and for most people it is easier and more comfortable to keep doing what they already know how to do than to learn new ways of thinking and acting.

This fact of human psychology is based on evolution-ary preference; human beings have limited cognitive processing power, and must allocate thinking resources accordingly. The reduction of uncertainty (via the development of patterns) in the day-to-day environ-ment frees up thinking ability that would otherwise have been applied to sorting between the important and the unimportant new stimuli in the environment. By routinizing the ‘little things’ and limiting the intru-sion of unimportant stimuli in our day-to-day activity, we leave more processing power for those things we deem important. Or at least that’s the idea.

9.3 Organizational Change for Effective Digital Analytics

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Our psychological tactics to sort through all the stimuli in our lives and make sense of the most important are known as bias or heuristics, and often fall victim to their own efforts. In organizations, in addition to the ‘familiarity heuristic’, two other com-mon heuristics tend to create challenges for organi-zational change. The first is a heuristic known as “social proof”, which is an observed tendency for individuals to conform to the activity they observe from others in a group, particularly when confronted with ambiguity or uncertainty. This can also be thought of as pack or herd behavior. The second is a bias or heuristic called “escalation of commitment”. This interesting bias results in people justifying an increasing level of commitment to prior decisions even when confronted with new evidence that indi-cates that abandonment of the current course of action and development of a new approach would be more productive.

When the familiarity, social proof and escalation of commitment heuristics operate together in organi-zations, we find places where managers are reflex-ively investing ongoing effort in strategies and tactics that should be reexamined based on changes in the business situation, within organizational structures that should also be reexamined, and with teams that find it difficult to challenge the way things are done as long as everyone else also seems to think this is the right approach. The example earlier in this chapter of the “Go Fever” that gripped NASA in advance of the Challenger launch is a tragic example of the danger these biases can cause for organizations.

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Tackling the biases and heuristics which limit the capability for change within organizations requires explicit organizational change strategy. Some of the best recent thinking on organizational change has come from MIT. This is not surprising because MIT is a driver of technological innovation, and the suc-cessful adoption of any innovation requires a change from an old way of acting (without the technology) to a new way of acting (with the technology). One of the leading thinkers at MIT is Peter Senge, who first became known in the area of organizational devel-opment with the publication of a book titled The Fifth Discipline in which he introduced the idea of a learning organization; a company that puts learning and change at its core to ensure ongoing transforma-tion and growth.

At the center of the learning organization is the cul-tivation of “systems thinking”. Systems thinking is the ability for an individual to unravel the context of their environment, or the ‘system’ in which they operate, into a series of separate threads that collec-tively form the system. One thread people may con-sciously discover and account for is how their own patterns of thought and activity translate from seem-ingly insignificant day-to-day attitudes and actions into a component part of the organization and how it works, and as such how, from the inside out, they actually create the forces which they may have previ-ously perceived of as an impersonal organization acting on them from the outside in.

Systems thinking can be summarized well with the questions, Why are we doing this? and ‘what could

Systems Thinking

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we being doing differently? As Senge puts it, ‘The fundamental rationale of systems thinking is to understand how it is that the problems that we all deal with which are the most vexing, difficult and intransigent come about, and to give us some per-spective on those problems [in order to] give us some leverage and insight as to what we might do differently’.

Senge outlines the benefit of systems thinking as pro-viding organizations with ‘the ability to discern non-obvious areas of high leverage’, or in other words, to develop competitive advantage through the ability to create and act upon change. As Senge puts it in a 2011 interview produced by IBM, ‘…when you ask, what does it take, in a business context, for people to start to discern non-obvious areas of leverage, the answer is, a very deep and persistent commitment to learning. There are a couple of features to this com-mitment to learning. One is, I have to be prepared to be wrong. Again, if it was pretty obvious what needed to be done, we’d already be doing it. So, I’m part of the problem…my own way of seeing things, my own sense of where there’s leverage, is probably part of the problem. This is the domain we’ve always called “mental models”. If I’m not prepared to challenge my own mental models, the likelihood of finding non-obvious areas of high leverage is low.’

As described earlier in this chapter, the purpose of analysis is to identify patterns and provide explana-tions for those patterns in ways that inform actions to optimize desired outcomes. As also mentioned ear-lier, these patterns have more value when they pro-vide unexpected insight, or in Senge’s terms, when they provide ‘non-obvious areas of high leverage’.

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Of course, finding these unique insights will not happen until we begin to understand how our pre-existing mental models, biases or heuristics are limit-ing our perspective, and until we recognize that we ourselves need to change for the system to change. Nowhere is this truer than for managers in a business that is looking to become more data-driven. In such an environment looking to undergo organizational change, not only is the manager challenged in the implied shift of their role from ‘answer definer’ to ‘question asker’, but they will also be tasked with guiding the organization’s ability to find ‘non-obvi-ous sources of high leverage’ in the data, and to apply these to achieve results. Engendering systems thinking is the place to start. For an understanding of how to begin this process, we turn to a second MIT professor — Otto Scharmer, a Senior Lecturer at Sloan School of Business and co-founder of the Presencing Institute, the origin of Theory U.

Theory U

The success of an intervention depends on the interior condition of the intervener.

— Bill O’Brien, CEO, Hannover Insurance

The quote is provided within Otto Scharmer’s book Theory U: Leading from the Future as it Emerges, and per-fectly summarizes his underlying message. Scharmer shares Senge’s view of environments in need of change as systems that have been built and main-tained by the very people who need to make the changes, and the related importance of each indi-vidual’s recognition of their role in (re)structuring the systems of which they are a part. The underlying

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principle of Scharmer’s Theory U is that positive change can only emerge when individuals stop ‘downloading’ from the existing system and instead begin to use new information and creativity to start bringing the best possible future into the present, a process he calls “Presencing”. This concept of Presencing is grounded partly in the requirement captured in the quotation that starts this system; the requirement that individuals who hope to intervene for good in their systems must be consciously present within those systems to take the first step away from the default state of downloading, which can also be thought of as acting reflexively from the familiarity, social proof and escalation of commitment heuristics described earlier in this chapter.

Recognizing and addressing the heuristics, filters and biases that shape the perspective of each of us as individuals in our environment is a key first step to enacting the type of change that will transform our organization to be more “data-driven” and able to act on unexpected insights. Scharmer’s Theory U also provides a nicely structured understanding of the organization, and the organizational life of indi-viduals that provides context around the manifesta-tion of our collective internal conditions into the external conditions we perceive as a system.

Scharmer outlines three layers of organizational life that collectively compose what we think of as the ‘system’ in an organization:

1. Objective Structures: This is the dimension of the organization as defined by the organization chart, and structured through established tools and processes.

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2. Enacted Structures: This is the dimension of the organization as defined by informal social net-works. While the objective organization dictates ‘who is in charge of what’, the enacted structure tends to determine how things actually get done. This structure is where ‘who you know’ matters more than your position in an org chart, and ‘what you know’ matters more than your defined job function.

3. Personal Sources of Enactment: This is the foun-dation of the organization, and the source of all activity in the enacted structures and systems described above. This is the position from which each individual in the organization takes action, and is defined by the collective perspectives, atti-tudes, biases and filters of each individual in the organization. When filters and biases are high, perspectives are narrow, and attitudes lack energy and positivity, the organization will be inflexible and resistant to the change it will sorely need.

Because so much activity inside organizations is guided by heuristics and processed through filters, any proposed change that might result in a need to leave this comfort zone will be perceived as a threat unless, (1) it is offered with context that links the change required by each individual to a bigger picture vision of an organizational outcome, and (2) solves something that these individuals agree needs to be solved.

The “U” in Scharmer’s Theory U is essentially a ‘U-turn’ for individuals and the way they work within and perceive their organizations. The shape reflects a process of ‘diving-down’ to a point of deep under-standing of oneself and one’s role in generating the

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present of the organization, then building back up on the other side of this process to ultimately act as the embodiment of a new and better way of organi-zational work. On the left, or downward sloping side of the “U”, there are four stages in shifting from the current state to a place of readiness for change.

The default state of organizational awareness is “Downloading/Conforming”. Here the individual speaks from what is expected/learned. This is clearly the antithesis of an analytical and data-driven mind-set and environment. True analysts will not do well in this environment, and data or analysis that contra-dicts the established conformity will be rejected with-out reason.

The first level of awareness deepening is to “Debate/Confronting”. Here the individual begins speaking from what they think. While this type of awareness is at least capable of diverging from other views, for each individual acting from this position, it is very unidirectional and defensive; their thinking and per-spective flows out into the organization, but little is let back in to shape or transform their beliefs. Heuristics and biases still rule the day in this world, they are simply less shared. An analyst who can only present what they think and cannot hear other per-spectives will be limited in the value of insight they can deliver. A manager who thinks this way will use data when it supports their position, and reject data when it does not.

The next level of awareness is one of “Dialogue/Relating”. Here the individual understands that they are part of a system, and speaks from the standpoint of seeing themselves as part of the whole. This is

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clearly a much more productive position for all involved. Analysis produced from this perspective will be cognizant of the needs of others and the sys-tem as a whole, and as such will be more valuable to all. Managers who work from this perspective tend to be focused more on enabling the best options even if they come from others versus constraining activity to only those ideas which come from them. In this environment, data and models that point out a bet-ter direction will be accepted and utilized.

The deepest level of awareness is “Presencing/Connecting”. Here the individual is not afraid to speak from what can emerge, and to enable and join a flow of ideas and creativity across the organization. Analysts who can work from this position will regu-larly produce insights that are welcomed as percep-tive, creative and able to help drive innovation and progress in the organization. Managers who work from this perspective are allowing their organiza-tions to create a sum that is greater than its parts. Data and models are welcomed if they resolve the need to spend energy and effort on something that is just as easily or better managed algorithmically, and accordingly free up the organization’s thinking for more creative pursuits.

When a critical mass of influential individuals in an organization have collectively reached the presencing level of awareness, the organization is certainly ready to become data-driven, and to make the most of that readiness in terms of quickly developing creative responses to the ‘non-obvious sources of high lever-age’ that a readiness for continuous discovery ena-bles. But not all organizations have achieved a level of dialogue or presencing that makes them ready to

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In his book, Leading at the Speed of Change, Darryl Connor defines the roles that people in organizations will take around any sort of organizational change. As explored above, there will typically be a large con-tingent who are initially resistant to change. When it comes to change related to data and the transforma-tion to more data-driven practice, the likely first line of resistance will come from the “Targets” of change, the managers who are responsible for the current system of data utilization, and the teams that are accustomed to doing their daily work within in that system. This latter group is also the first group that is likely to spawn advocates for change as the benefits of the potential change are defined, and those individu-als capable of dialogue and presencing begin to envi-sion a better system as a result of the change.

As Connor has observed, there are typically three other roles working to enact the change for the Targets of change. “Sponsors” are the formal leaders in the organization who provide political capital and resources in support of the change effort. These are the individuals who can use organizational authority to mandate that the change should happen. However, even with the contribution of mandate, time and budget, as systems are organic products of the indi-viduals within them, mandates do not become enacted simply based on their presentation by the

go where the data tells them to go. So how can a reader of this book begin to initiate change in their own perspective, and then in their organization, to become able to find and apply insights from data?

Leadership and Change

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organizations formal leadership. As Scharmer observed, there is an ‘enacted’ system in organiza-tions underlying the formal structure with a set of relationships and informal processes that determine how things actually get done.

The role emerging from this enacted organizational structure is the “Advocate” for change. Even with strong sponsorship within the formal system of an organization, a change effort without Advocates in the enacted layer is sentenced to failure. While for-mal authority provided by sponsors in leadership in critical for enacting change, support for change pro-vided by advocates who fall within the Target popula-tion that will be most affected by the change is perhaps the most powerful form of advocacy for getting the idea for change off the ground and into practice. People in organizations understand that they have to follow the letter of the law as dictated by the Objective structure, but without belief in the spirit of the law, this typically translates into going through the motions around a new ‘process’ that is viewed as an imposition, and is eventually aban-doned due to a lack of results. Advocates for change are those who believe in the spirit of the vision for change that underlies a managerial mandate, and who are able, through dialogue and presencing, to translate that vision into terms that can be appreci-ated by their peers.

The role that draws most heavily from the practices of ‘presencing’ to bring the future into the present through organizational change is the “Change Agent”. Many of you reading this far into this chapter will be interested in taking on this role in an effort to help your organization begin utilizing some of the approaches to digital analytics outlined in this book,

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and hopefully the discussion in this chapter has pro-vided you with guidance into how this may be accom-plished. As you seek to implement change, ensure you have Sponsorship, recognize your Targets, develop Advocates, and cultivate those Advocates into other Change Agents. Use the ideas defined by Senge to shed light on the system, defining the prob-lems of the system that most need to be addressed, and using data to find ‘non-obvious sources of high leverage’ that can be used to address them. And use the ideas defined by Scharmer to establish a position of presencing in your ‘personal field of enactment’ to place the most creative power behind your effort to drive change, and to draw others as Advocates and supporting Change Agents into your cause.

Overcome Organizational Inertia

We will end this chapter with some guidance on spe-cific strategic and tactical considerations to make in efforts to build a more data-driven organization. These questions return us to the “Data Applied” sec-tion of the ADAP, and may be asked and answered as a preface to that section. In auditing the organiza-tion to prepare for change to a more data-driven structure, ask the following questions:

• What data is used in the design and delivery of digital experiences?

• What data is used in the evaluation and optimiza-tion of digital performance?

Where is communication design work being done from guesses or by rote process?

Is there data available that should be used but isn’t?

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Are there design or performance questions that need data that is not available?

Is performance viewed in business siloes versus integrated across channel, like the customer experience?

As a Change Agent fueled with the creative power of a presencing mindset, seek to expand your new mindset to others. Through the development of the ADAP, you will have made a giant step toward change by defining a data-analytics strategy that can become a part of collective dialogue. To see the change through, ensure that Sponsors, Advocates and Targets — even initial resistors to change — have all contributed to the dialogue (which in the case of resistors will have been debate initially, but can become dialogue as they are engaged in that way).

Finally, return to the principles for valuable analysis and visualization defined above to ensure you are building frontline capabilities that actually deliver the results that have been envisioned through the process of change. While sophisticated analytics solu-tions will create opportunities to identify non-obvious areas of high leverage, these solutions must manifest in interfaces and visualizations that are so simple and useful that managers and employees will want to use them daily. As you implement analytics, do not allow the effort and investment be so heavily weighted towards models that frontline usage gets overlooked, since models and analytics are only truly valuable to the business when they can be used to drive positive results for the business and the consumer alike.

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263


Conclusion

Thank you for taking the time to read and learn more about digital analytics and marketing science. If this book has done its job, then the preceding pages have answered that key question facing you as a marketer: how do I deliver content and experience around my brand that is relevant enough to drive engagement in the user’s current context?

The answer begins with the documentation of busi-ness objectives, consumer objectives and the resulting marketing objectives to begin your Applied Digital Analytics Plan, and emerges from diligent considera-tion of the other foci of the ADAP:

1. Defining the problems that data can solve;2. Identifying sources of data (existing and

potential);3. Collecting, managing and analyzing data;4. Overcoming organizational and cultural inertia;5. Applying data and analysis to solve the problem;6. Evaluating the outcomes.

In evaluating outcomes, you will now be aware of the first rule of digital engagement: relevance drives results. If the outcomes you evaluate through perfor-mance analytics are not performing as desired, you will look for ways to increase relevance through fur-ther application of data and analytics.

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In evaluating the opportunities to increase relevance with data, you will look beyond simple performance measurement to evaluate the context around that performance, and to conduct research needed to augment that context.

You will form hypotheses that prescribe potential solutions and test how well those prescriptions deliver the desired results. You will incorporate knowledge gained from context analysis, research and prescrip-tive analytics into predictive models, and ultimately you will allow those models to guide automated and adaptive experiences that use machine learning to optimize the efficiency and effectiveness of market-ing delivery for each person you connect with, at each point of engagement.

You now have a very good base understanding of how to conduct data collection for owned, earned and paid channels, and you understand the value of plan-ning and implementing experience designs and information technologies that connect data from each of these channels around a common ID that aligns interactions across channels and over time with each unique individual who engages in those interactions. You also understand the challenges of making this unified data infrastructure work, but are prepared to work with partners in technology and across the business to define a realistic path toward achieving unified data and a cross-channel view of the customer.

In working across the organization to achieve this customer-centered data-driven approach, you are prepared to face resistance and inertia, and you are armed with an understanding of what creates such


Conclusion 265


resistance and inertia, and ideas for how to face and overcome negative heuristics and patterns of down-loading with systems thinking and Theory U. And in driving meaningful and relevant engagement with consumers, you are able to consider not just the con-tent you are driving, but also the impact of the medium through which content is communicated. You recognize that different channels have different impacts based on how their “hot” or “cold” nature aligns with the ‘temperature’ of the sub-culture(s) to which your audience belongs.

So, although you have reached the end of this book, you are just at the beginning of what will undoubt-edly turn out to be a long and interesting journey as a champion for consumer-centered data-driven mar-keting. As you follow the path that proceeds from these pages into your own next chapter, please stay connected via this book’s counterpart website (www.architectingexperience.com) for access to supporting material (e.g. the ADAP template), expanded think-ing around this book, and to share your experiences and ideas with peers and colleagues.

Further Reading

Connor, Daryl R. (1993). Leading at the Speed of Change. London: Random House.

McLuhan, Marshall (1965). Understanding Media: The Extensions of Man. New York: McGraw-Hill.

Stasko, John (2013). The Value of Visualization…and Why Interaction Matters. Presentation at the 2014 Euro-graphics Conference on Visualization (EuroVis’14). June 9–13, 2014. Swansea, Wales, UK. Available at: http://www.cc.gatech.edu/gvu/ii/talks/eurovis14-capstone-stasko.pdf.




Sage, Peter (1990) [2006]. The Fifth Discipline: The Art and Practice of the Learning Organization [2nd ed.]. London: Random House.

Scharmer, Otto (2007). Theory U: Leading from the Future as it Emerges. San Francisco, CA: Berrett-Koehler Publishers.

Tufte, Edward (1983) [2001]. The Visual Display of Quantitative Information [2nd ed.]. Cheshire, CT: Graphics Press.


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Index

A/B test, 174, 177, 178, 180

Application Programming Interfaces (APIs), 123, 125, 127, 131, 134–136, 138, 139, 142

Applied Digital Analytics Playbook (ADAP), 49, 50, 55, 69

Big Data, 32, 35, 130

campaign tagging, 84, 86, 100

Click-through Rate (CTR), 157, 158

Connor, Darryl, 259cookies (web), 77, 78, 102cool medium, 229, 237Cost-per-Acquisition (CPA),

158, 159, 166Cost-per-Click (CPC), 157CPM, 156, 157Customer Decision Journey

(CDJ), 10, 15, 16Customer Journey, 8Customer Relationship

Management (CRM), 29, 30, 37

dark patterns, 183, 185, 186data, 49, 50, 55, 56, 57, 154Data Management

Platforms (DMPs), 14, 28, 103, 105, 118, 154, 162, 170, 189, 190, 194, 207

demand chain, 7–9, 15Demand Side Platform

(DSP), 28, 46, 105, 154, 155, 170, 171, 191, 195, 204

Demographics, 199Digital Data Stack, 26

Earned Media, 27event tagging, 84, 86, 87,

90extract, transform and

load (ETL), 202

hot medium, 229, 231, 247

Integrated Marketing Communications (IMC), 15, 55

JavaScript, 74, 76, 77, 78, 83, 86, 92

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JavaScript Object Notation (JSON), 139

Key Performance Indicators (KPIs), 39–41

machine learning, 125, 126

marketing science, 21, 22, 26, 29, 38, 47

McLuhan, Marshall, 229–231

media, 2–4, 14, 19middleware, 135multivariate (MVT)

testing, 174, 177, 182

neural networks, 211, 214–216

organizational change, 248, 250–252, 254, 259, 260

owned media, 26

page tagging, 92page tagging (web), 94Paid Media, 27personas, 57, 58, 64, 69programmatic marketing,

see Real-time Bidding (RTB) 47, 171, 217

psychographics, 200purchase funnel, 10

Real-time Bidding (RTB) see programmatic marketing, 168, 169

Recency, Frequency and Monetary (RFM), 200

Retargeting, 164Return of Investment

(ROI)Return on Investment

(ROI), 43, 45, 65, 68, 145, 148, 159

Scharmer, Otto, 254Senge, Peter, 252segmentation, 199, 200,

206Stasko, John, 222structured data, 117, 124,

125systems thinking, 252–254

tag management, 92, 93targeting, 152, 159Theory U, 254–256Tufte, Edward, 222

unstructured data, 124, 126

View-through Rate (VTR), 157

visualization, 205, 208

b2242_Index.indd 268 11/27/2015 12:48:39 PM

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