audience segmentation and targeting · 2019. 6. 7. · audience segmentation and targeting: the...

AUDIENCE SEGMENTATION AND TARGETINGThe Building Blocks of a Better Customer ExperienceJUNE 2019

Lauren Fisher

Contributors: Ross Benes, Lucy Koch, Nicole Perrin, Jillian Ryan, Tracy Tang

AUDIENCE SEGMENTATION AND TARGETINGThe Building Blocks of a Better Customer ExperienceJUNE 2019

Lauren Fisher

Contributors: Ross Benes, Lucy Koch, Nicole Perrin, Jillian Ryan, Tracy Tang

Customer Experience 2019 A 4-Part Series

AUDIENCE SEGMENTATION AND TARGETING: THE BUILDING BLOCKS OF A BETTER CUSTOMER EXPERIENCE ©2019 EMARKETER INC. ALL RIGHTS RESERVED 2

AUDIENCE SEGMENTATION AND TARGETING: THE BUILDING BLOCKS OF A BETTER CUSTOMER EXPERIENCE

To deliver an effective customer experience, marketers must first know who their customers and audiences are. This

report focuses on how marketers identify audience segments and target groups today.

How do companies segment audiences today?

Most companies invest ample time and research into understanding their customers. And most have created broader segments or personas that the bulk of their customers can be mapped to. However, not all companies have mapped or identified these personas to segments and targets for digital and addressable media.

How are marketers identifying and targeting the right audience segments in digital and addressable media?

Marketers can use any number of data sets for their segmentation efforts. Most initially build their segments by starting with first- or third-party data, layering in added data sets as needed. The most-used data sets typically help marketers answer two core questions: Who is my audience? And how engaged is that audience with my brand today?

Shouldn’t marketers also care about how their audiences could or should engage with their brand in the future?

Absolutely. In fact, many companies increasingly rely on predictive analytics and machine learning to optimize and adjust their segmentation strategies.

WHAT’S IN THIS REPORT? This report focuses on how marketers identify and build audience segments within digital and addressable media.

% of respondents

Change* in Audience Data and Related DataActivation Solutions Spending According to USCompanies, 2018 & 2019

More69.2%

78.2%

About the same23.1%

15.4%

Less3.9%

2.6%

Not sure3.9%

3.9%

2018 2019

Note: includes analytics, data management/processing, DMP/CDP tools,onboarding, matching, etc; numbers may not add up to 100% due torounding; *2018 change vs. 2017; 2019 is expected change vs. 2018Source: Winterberry Group, "The Outlook for Data 2019: A Snapshot Intothe Evolving Role of Audience Insight" in partnership with IAB Data Centerof Excellence, March 5, 2019245841 www.eMarketer.com

KEY STAT: Investment in the tools needed to access or act on audience data is on the rise. Winterberry Group in partnership with the IAB found nearly four out of five US companies planned to increase spend on audience data and related data activation solutions this year.

CONTENTS2 Customer Experience 2019 (Part 1)—Audience

Segmentation and Targeting

3 The Customer Experience Imperative

4 Audience Segmentation for Digital and Addressable Advertising

5 Turning Personas into Segments for Digital and Addressable Media

13 Putting It Together: First- vs. Third-Party Data

15 Key Takeaways

15 eMarketer Interviews

17 Read Next

18 Sources


THE CUSTOMER EXPERIENCE IMPERATIVE

Customer experience initiatives are non-negotiable

for companies that expect to keep pace with

direct-to-consumer (D2C) brands and digital-first

companies that expertly tailor experiences to

their consumers.

Customer experience is a broad term, but we define it as all the touchpoints a consumer has with a brand. Put in words marketers might see frequently: It’s about building a data-driven, 360-degree customer view.

“Consumer behavior and expectations are changing,” said Doug Novack, managing director of business and industrial markets at Google. “Consumers are now experiencing an incredible amount of personalization with a lot of the apps and services they’re using on a daily basis. Consumers now expect that experience, so that raises the stakes.”

Most companies realize their customer experience falls short. A January 2019 survey of marketers and consumers conducted by The Harris Poll and commissioned by customer data platform (CDP) RedPoint Global found that far fewer than half of marketers in North America and the UK rated their ability to deliver an exceptional customer experience as excellent. Consumers were even less enthusiastic—just 18% of consumers across the UK, US and Canada said they felt brands did an excellent job at delivering an exceptional customer experience.

% of respondents

Marketers vs. Consumers in North America and theUK Who Think Brands Are Able to Deliver anExceptional Customer Experience, Jan 2019

Marketers Consumers

US 48% 22%

Canada 23% 15%

UK 31% 13%

Total 34% 18%

Note: ages 18+; "excellent" ability; read chart as 48% of US marketersbelieve brands have an excellent ability to provide an exceptional customerexperience, vs. 22% of US consumersSource: The Harris Poll, "Addressing The Gaps In Customer Experience"commissioned by RedPoint Global, March 27, 2019246304 www.eMarketer.com

The same survey asked marketers why such experiences were hard to deliver. Half of respondents cited real-time engagement as a challenge for creating an exceptional customer experience. Other marketing tactics such as personalization, a single customer view and omnichannel efforts were also common stumbling blocks.

But for nearly half of the marketers surveyed, basic customer understanding was a challenge. This illustrates the spectrum on which customer experience efforts exist today: Some marketers wrestle with the execution of these efforts, but a significant portion still struggle with building the necessary foundation.

Understanding ones’ customers is the foundation of any customer experience, but challenges exist. Only one-third of US marketing and technology professionals polled by digital analytics services firm Cognetik at Adobe’s March 2019 Summit said they had a good understanding of their customer and used data to improve the customer experience.

% of respondents, March 2019

How Well Do US Marketing and TechnologyProfessionals Feel Their Company Understands TheirCustomers?

Good understanding, and we use the data to improve customerexperience

34%

Good understanding, but not acting on data44%

Limited understanding21%

Little to no understanding1%

Note: numbers may not add up to 100% due to roundingSource: Cognetik, "Data Maturity Survey Insights," May 1, 2019247238 www.eMarketer.com

That’s unsurprising, considering how few marketers are satisfied with their ability to perform core functions necessary to understand customers. A January 2019 survey conducted by social news firm Social Media Today sponsored by marketing automation firm SharpSpring found that 44% of marketers worldwide cited audience research as a marketing tactic that took up the most time or was most challenging for their business. An added 28% of respondents mentioned audience segmentation.

The remainder of this report will focus on core tactics and frameworks for identifying and segmenting audiences and then finding those audiences in addressable and digital channels.


This report is the first in a four-part series that will address the customer experience by exploring each of the major building blocks required for an exceptional customer experience: audience segmentation, personalization, frequency management and loyalty marketing. By dissecting the customer experience into understanding who customers are, how to best speak to them, how often to speak with them and how to continue to grow the relationship post-acquisition, our belief is that marketers can better approach this broader subject with more expertise and accuracy.

AUDIENCE SEGMENTATION FOR DIGITAL AND ADDRESSABLE ADVERTISING

The phrase “know your customer” seems simple,

singular and straightforward. But when it comes

to knowing your customer within digital and

addressable media, it is often anything but.

Those embarking on customer experience initiatives are neither running blind nor starting from scratch. In the vast majority of cases, brands and companies have already invested ample time and research into understanding their customers—and created broad segments or personas that map to the majority of their customers.

While such information exists, it isn’t typically in the form marketers need for their customer experience mandates. In fact, business intelligence teams, market research teams and even product teams may be more in control of defining and creating segments than the marketers expected to reach them.

If one thing is clear, it’s this: The job of the marketer, particularly the customer experience-focused marketer, is to take those segments identified by other teams and translate them to each channel and touchpoint within the customer experience. But to make this work, companies need data—and a lot of it.

“Data is the new brand planning,” said Dave Grzelak, chief strategy officer at full-service agency The Shipyard. “Companies are now looking to move beyond the homogenous model that existed in the past where you have one or two personas and then the big ideas and campaigns to execute across those audiences. People are different, and data providers give us the ability to see those differences, get to much more niche audiences and understand what those audiences are interested in.”

A survey of US companies conducted by Winterberry Group in partnership with the IAB found nearly four out of five respondents planned to increase spend on audience data and related data activation solutions this year compared with 2018. Such solutions included everything from CDPs to DMPs and data onboarding tools.

% of respondents

Change* in Audience Data and Related DataActivation Solutions Spending According to USCompanies, 2018 & 2019

More69.2%

78.2%

About the same23.1%

15.4%

Less3.9%

2.6%

Not sure3.9%

3.9%

2018 2019

Note: includes analytics, data management/processing, DMP/CDP tools,onboarding, matching, etc; numbers may not add up to 100% due torounding; *2018 change vs. 2017; 2019 is expected change vs. 2018Source: Winterberry Group, "The Outlook for Data 2019: A Snapshot Intothe Evolving Role of Audience Insight" in partnership with IAB Data Centerof Excellence, March 5, 2019245841 www.eMarketer.com

More telling of the growing sophistication of such practices is the number of tools which marketers might use to identify relevant audience insights. A January 2019 survey of US and UK companies conducted by customer experience community CX Network in association with customer experience software provider Clicktale found respondents relied on everything from web analytics to voice-of-customer (VOC) data to customer journey analytics to gather necessary customer experience insights. More than half also relied on tools such as A/B and multivariate testing to inform their efforts, indicating a more sophisticated desire to identify the right data to power their customer experience insights and refine learnings along the way.


% of respondents

Data Collection Tools Currently Used by UK and USCompanies for Their Digital Experience Initiatives, Jan 2019

Web analytics86%

Surveys/feedback forms84%

Voice of the customer tools64%

A/B or multivariate testing56%

Journey path planners/analytics45%

Online reputation monitoring tools43%

Heatmaps42%

Session replays29%

Source: CX Network, "Defining Digital Experience" in association withClicktale, March 18, 2019246437 www.eMarketer.com

These are just some of the many tools marketers may rely on for audience intel to inform their segmentation strategies. The next section provides greater structure on how to choose between these tools and data sets by asking specific questions.

TURNING PERSONAS INTO SEGMENTS FOR DIGITAL AND ADDRESSABLE MEDIA

In this section, we explore the data sets and resources

needed to identify audience segments across

addressable media, both digital and traditional, by

breaking the process into three key questions:

■ Who is my audience?

■ How engaged is that audience with my brand today?

■ How could or should they engage with my brand?

WHO IS MY AUDIENCE? Most marketers initially rely on a handful of data sets to identify the core attributes or traits of their target audiences. The below chart describes the main data sets used in initial audience segmentation strategies today. For each set, we then identify the sources of first-party data (data owned directly by the brand), second-party data (data accessed via a partner or company that is in direct ownership of that data) or third-party data (data that is bought or borrowed from an intermediary who has access to others’ data sets) that may be used to build this data set.

Note that some sources of data and data sets appear across multiple categories. Certain tools may provide access to multiple forms of data.

Even if marketers aren’t necessarily interested in reaching segments in an addressable manner, identity data and identity graphs are still required if marketers wish to accomplish tasks such as managing message frequency across channels and devices, understanding who customers are across those devices, stitching together online and offline data sets and marrying other anonymized data sets (e.g., cookies, device IDs) with known customer information (CRM data, email, address, etc.).

Company-owned systems containing customer information such as CRM, CDPs and POS are common sources of key first-party information needed to identify audiences across the digital and physical worlds. Publishers and digital commerce platforms may have their own identity graphs. (Facebook and Google are two common examples of properties boasting their own cross-device identity graphs.) And in some instances, agencies may offer their own identity solutions.

Customer Data Platform (CDP): A system that houses customer-specific data, organized heavily around the use of first-party data to build customer profiles. Such systems allow for the incorporation of various data sets that can be cleaned and appended to these customer profiles. Marketers then sync their CDPs with other marketing and advertising systems to activate these profiles for their marketing efforts.

Customer Relationship Management (CRM): A system that houses customer-specific data such as their profile, purchase history and other relevant information the company needs to manage customer relationships.

Point of Sale (POS): Data detailing the in-store transaction between a customer and merchant.


Identity: device-level; online-to-of�ine (O2O)

Demographic: age, gender, ousehold income

Purchase

Firmographic: company size, revenues, industry, etc.

Behavioral: marketing-speci�c data

Psychographic: attitudes, interests, lifestyles, values

Nonmarketing data

Data Type First-party source

• Customer relationship management (CRM)—e.g., email, address, phone number• Point-of-sale (POS)

• CRM• Customer data platform (CDP)

• CRM• Billing/invoice systems• In-store/POS

• CRM• CDP

• Website and app analytics • Analytics from multichannel marketing hub (e.g., Salesforce, Oracle, Adobe, SAP, etc.)

• Surveys• User pro�les• Website/app analytics (e.g., speci�c interest in content types)• Social analytics

• Call center• Merchandising• In-store, POS• Billing/invoice

Third-party source

• Identity resolution provider (standalone or as offered via DMP or DSP)• Panels (e.g., Comscore, Nielsen)

• Data reseller (e.g., Epsilon, Experian, Acxiom, etc.)• Panels (e.g., Comscore, Nielsen)• DMP offering access to third-party data sets• DSP offering access to third-party data sets

• Data reseller (e.g., Kantar, IRI, Experian, etc.)• O2O data onboarder

• Third-party data reseller (e.g., Dun & Bradstreet)• Site scrapers/crawlers

• Ad server• Attribution• Panels/surveys• Data resellers (e.g., intender data)• Search data

• Data resellers• Panels• Reputational/social monitoring and listening tools

• Data reseller (e.g., foot traf�c data)

Second-party source

• Publisher/digital commerce platform (e.g., login info, email, username, etc.)• Retailer (e.g., POS data)• Agency• Data co-op

• Publisher• Data management platform (DMP)• Demand-side platform (DSP)• Data co-op

• Retailer • Publisher/digital commerce platform • Data co-op• Loyalty/credit card companies

• Publishers • Data co-ops

• Data co-op

• Publisher • Data co-op

• Publisher-provided analytics and reporting • Platform-provided analytics and reporting (e.g., DSP, exchange, location analytics platforms, etc.)

Source: eMarketer, May 20, 2019

SELECT DATA SETS USED TO IDENTIFY AND SEGMENT AUDIENCES ON BASIC ATTRIBUTES, MAY 2019


Marketers can gain greater access to known customer data and identity graphs with data co-ops. Such co-ops may be erected with brands targeting similar-type audiences or even accessed via marketing service providers such as Adobe or Oracle. And many DMPs and demand-side platforms (DSPs) also offer cross-device targeting as part of their product. These platforms increasingly offer online-to-offline (O2O) identity resolution as part of their services. Still, other marketers may work directly with an identity resolution provider to take advantage of cross-device intel or work with an O2O data onboarder to integrate physical in-store and marketing data with digital audiences and insights.

Data Management Platform (DMP): A system that is used to collect and process data that’s used for marketing and advertising purposes. Like a CDP, it is capable of ingesting, cleansing and organizing data sets into profiles. It is also commonly integrated with other marketing and advertising technology to activate those insights. Unlike CDPs, DMPs have historically been centered more on analyzing third- vs. first-party data sets.

Demand-Side Platform (DSP): A system used to automate ad buying in a data-driven way. DSPs are commonly used in programmatic advertising, particularly to access digital display inventory.

Online-to-Offline (O2O): The integration and flow of data from digital and physical touchpoints. In marketing, O2O integration typically looks to provide a more holistic understanding of ones’ customer for measurement, segmentation or targeting purposes.

Each identity solution works differently, but most rely on either known customer information (also known as deterministic data) or anonymized information (also known as probabilistic data) to map their audience universe. Today, most identity graphs leverage both deterministic and probabilistic practices. As browser privacy settings continue to crack down on cookies and regulation such as the California Consumer Privacy Act (CCPA) nears rollout, some wonder how sustainable certain probabilistic data sets will be in the future (more on that later).

Demographic data describes an audience based on their age, gender, household income or education. Marketers regularly ask customers to share this information at some point in their customer life cycle, and insights may be housed in the CRM or CDP. Many also turn to third-party data providers and resellers to access audiences with these characteristics, but publishers and data co-ops can also provide secondhand access to this data.

Demographic data such as age and gender is still very valuable. In an April 2018 poll conducted by attribution firm LoopMe and B2B market research agency Sapio Research, audience and demographic data was among the most- and second-most valued data forms according to US marketers.

For B2Bs, firmographic data (data that describes something about a business, such as its size, revenues or industry) is often more important than demographic data, especially for account-based marketing strategies. Some companies have amassed firmographic data on their target audience over time, and they store it in their CRM. In some cases, B2Bs might also use a CDP. However, use is largely restricted to enterprise-sized companies today. Publishers are also sources of firmographic data if they require businesses to register and specify these data points as part of the registration process. Data co-ops and resellers are also common means through which companies access the firmographic information needed for audience segmentation. And website scrapers and crawlers may also help B2Bs identify added details on target companies.

Marketers also rely on behavioral data for greater audience insight in ways that can’t be identified through basic demographic information.

“Standard geodemographic segments don’t always tell the complete story,” said Ric Elert, president of Epsilon’s Conversant. “You can have a baby at 22 and 42, and so you can’t look at standard age, location or income. You have to look at the products that people are showing interest in and then talk to them based on that type of view.”

Web and app analytics—and reporting provided by marketing hubs powering email, search or social advertising efforts—can be common sources of first-party behavioral data. Marketers also frequently gain access to behavioral insights from the publishers and platforms they work with that can tell them more information on the types of ads or offers audiences are engaging with in real time and for how long. Marketers may also turn to third-party ad servers, search data and their own attribution tools for further intel on which messages resonate with audiences.


“Real-time behavioral data is the biggest low-hanging fruit for brands right now,” said April Mullen, director of consumer-first marketing adoption at Selligent Marketing Cloud. “I continue to be shocked by the number of brands that don’t leverage the breadcrumbs of the real-time data exhaust with their customers. Every digital interaction gives you information about what your customer wants and what they’re interested in.”

Panels, surveys and data resellers can offer insight into other forms of behavioral data, such as intender data, that can be used to further segment audiences.

Purchase data is a very specific form of behavioral data. It can describe what an audience has bought, when it was purchased and how frequently. For many companies, purchase data is commonly used to segment audiences as customer vs. prospect or high- vs. low-value. Purchase data is also critical for calculating customer lifetime value (LTV), which is another common input for segmentation efforts.

Apart from web analytics which may track online purchases, marketers can also access this data via POS, their billing systems or partnership with a loyalty or credit card company. Companies working with digital commerce platforms may also collect data, though it may not always be provided at the user level.

Customer Lifetime Value (LTV): The predicted value of a customer across their life cycle. LTV can be calculated using actual spend and customer behavior, among other indicators, to project total spend.

Psychographic data offers insights into the values, attitudes, interests or lifestyles of audiences. Such data is not only useful to better understand who audiences are, but also for determining how to best speak to or engage with them.

In some cases, companies solicit interest and lifestyle data from users when they sign up. Such information can also be collected firsthand via surveys. 360i is one company that uses surveys to collect psychographic data. According to Jackie Mockridge Mattina, senior vice president of consumer insights and analysis, the digital ad agency will send out surveys to its consumer panel to identify what motivates the consumer, be it fear, tradition, perception or their community. From there, the agency works to identify the segments that best match to these psychographic traits and the messages that will most resonate with them.

Web and marketing analytics tools can also provide insight into the types of content audiences are drawn to, which can be a proxy for interests. For Ben & Jerry’s, interest or propensity to drive social justice or environmental change is a core trait the ice cream maker wishes to identify in its audience to drum up support for its social mission.

According to Jay Tandan, US digital marketing manager at Ben & Jerry’s, the ice cream maker looks to identify individuals based on those who have signed petitions or called their congressional representatives regarding issues they advocate for. Ben & Jerry’s also looks to better understand how its fans are engaging with different issues they talk about on social media or on its website. With this information, the company can serve more relevant messages to key audiences to ensure they take action to help drive the company’s progressive values. When possible, Ben & Jerry’s also factors in the geography of that individual to know which local, state or federal issues might be most relevant to surface. “There are a few different sources of first-party data that provide us with an indication of how passionate they are and which issues resonate the most,” Tandan said. “And from there, we can follow up with a more personalized message to drive them to take action.”

Nonmarketing data is also sometimes used to further define audience segments for digital and addressable media. Companies pair call center activity, merchandising information, POS and in-store data with CRM and other known customer data to further refine audience segments. For some companies, particularly those where these data sets and their teams are siloed from marketing, this can prove challenging. Nonetheless, companies may look to their own systems or they may look to leverage in-store or POS insights from data co-ops or partners. And in some cases, select types of data can be purchased via third parties, such as footfall or foot-traffic data. Here, such data could prove valuable for segmenting audiences based on their propensity to visit brick-and-mortar vs. shop online.

HOW ENGAGED IS THAT AUDIENCE WITH MY BRAND TODAY? Some marketers may rely solely on the previously highlighted data sets; however, others push data collection and analysis further to understand how individuals within those segments are responding to or engaging with their brand today.


Voice-of-customer (VOC)/customer satisfaction

Sentiment

External data: weather, competitor, economic, etc.

Customer life cyclejourney data: marketing-speci�c data

Customer life cycle/journey data: purchase and loyalty data

Data Type First-party source

• Call center logs• Surveys/Net Promoter Score (NPS)

• Natural language processing (call center activity, emails, etc.) • On-site ratings and reviews

• Website and app analytics • Analytics from multichannel marketing hub (e.g., Salesforce, Oracle, Adobe, SAP, etc.)

• Customer relationship management (CRM)• Billing/invoice systems• In-store/point-of-sale (POS)

Third-party source

• Data resellers (e.g., iPerceptions)• Panels

• Ratings and reviews on external sites• Reputational/social monitoring and listening tools

• Analysis of competitor website, �nancial earnings, etc.• Data resellers• Search analytics/data • Panels/surveys

• Ad server• Search data• Panels/surveys• Attribution

• Data reseller (e.g., Kantar, IRI, Experian, etc.)• Online-to-of�ine (O2O) data onboarder

Second-party source

• Data co-op• Loyalty • Publishers/specialty data owners (e.g., The Weather Company)

• Publisher-provided analytics and reporting • Platform-provided analytics and reporting (e.g., demand-side platform [DSP], exchange, etc.)

• Retailer • Publisher/digital commerce platform • Data co-op• Loyalty/credit card companies

Source: eMarketer, May 20, 2019

SELECT DATA SETS USED TO IDENTIFY AND SEGMENT AUDIENCES BASED ON THEIR EXISTINGRELATIONSHIP WITH A BRAND, MAY 2019

Purchase data is often a common signal companies use to answer this question (for example, dividing audiences into prospects and customers). But within these segments, not all individuals feel or act the same. While the behavioral data described above helps marketers better parse some nuances, other data sets can give marketers greater insight into the true nature of their relationship with their audience and allow them to segment audiences further.

Voice-of-customer (VOC) data, is one of the most commonly used data sets to better understand the true nature of a company’s relationship with its customers. Companies pursuing customer experience initiatives rely heavily on the insights of this data that’s often collected via call center logs, email interactions with clients and company-issued surveys. Some companies may

standardize VOC feedback analysis using a Net Promoter Score or other grading or scoring systems. Companies lacking internal access to this feedback may turn to outside panels to solicit customer satisfaction information.

Although this data largely applies to customers and not prospects, it is still valuable for brands looking to understand the strength of their relationship with those individuals.

Net Promoter Score (NPS): A tool used to predict how loyal an individual is to a company. By asking individuals to rate the company on a scale of 1 to 10, “How likely is it that you would recommend our company/product/service to a friend or colleague?” it can be used to gauge customer satisfaction.

Voice-of-Customer (VOC): Data that gives insight into customer wants and needs.


Companies will also rely on sentiment data for similar insight into consumer attitudes. Like VOC data, call center logs, emails, on-site ratings and reviews and other inbound communications between a brand and its audience can be sources of insight. But unlike VOC data, many interviewed for this report felt that sentiment data went well beyond customer satisfaction to understanding the true feelings an individual has for a brand.

“People lie about customer satisfaction,” said Brad Birnbaum, founder and CEO of customer management platform Kustomer. “It’s a lot harder to lie on sentiment.”

Collecting sentiment data is on the minds—and action plans—of many US and UK professionals involved in managing their company’s digital experience. CX Network and Clicktale’s January 2019 poll found that more than half of respondents said they wanted to collect data on customer emotions, behaviors and mindsets in the future.

% of respondents, Jan 2019

What Data Will UK and US Digital Experience (DX)Professionals Want to Collect in the Future?

Customer emotions, behaviors and mindsets 52%

Customer intent 47%

In-page behavior 34%

Source: CX Network, "Defining Digital Experience" in association withClicktale, March 18, 2019246436 www.eMarketer.com

Today, many firms rely on natural language processing tools to review communications and systematically categorize them by feelings or other relevant emotions. Kustomer analyzes all customer-to-company communications across channels such as email, chats, texts, phone calls and more and assigns each a sentiment score. That score may change over time, depending on the content of communications during a particular period or communication frequency.

Such a score may then be combined with other data sets such as purchase or order propensity data. From here, Kustomer can segment audiences exhibiting negative sentiment who used to place multiple orders per month but have not made a purchase in the past 90 days. The company can then look to nurture or win back disgruntled or disengaged customers.

Public ratings and reviews can also be valuable sources of sentiment. 360i’s Mockridge Mattina said her agency scrapes public ratings and reviews across the web for sentiment and intel into how customers think and feel about a brand and its products. For a tourism client, the

agency combed reviews of the top 100-frequented spots in that city or destination to parse out attitudes toward a destination. They also looked at publicly-available review information, such as the person’s location, to further segment sentiment by residents vs. visitors.

“That allowed us to segment and understand the different motivations of what brought visitors to that destination, and it helped us inform future media plans,” she said. “With the right technology and natural language processing, we can define different clusters of consumers and behaviors.”

Slightly fewer than half of the respondents polled in the CX Network and Clicktale survey reported plans to collect customer intent data. That data is commonly used to understand the customer life cycle or customer journey. In fact, user experience testing firm UserTesting found 57.3% of companies worldwide already conducted user research studies to better understand the online and/or offline customer journey.

While the phrase “customer journey” might imply that such insights are largely collected and used for prospecting, companies taking a more customer-centric approach are looking at the journey in a more holistic manner. The diagram above speaks to this by listing common sources of customer journey insights as it pertains to marketing-specific data as well as purchase and loyalty data.

As noted in our May 2018 report, “Understanding Customer Engagement: How to Map and Make Sense of the Metrics that Matter,” there are an infinite number of sources of customer life cycle data. While the table above lists some of the most common sources of journey-specific insights across first, second and third parties, bear in mind that the best way to identify which sources of data are most important is to map ones’ customer journey.

For skin care company Curology, the customer journey is an important part of segmentation when it comes to products.

“We want to mimic the personalized attention they get from their medical provider,” said Fabian Seelbach, CMO at Curology. “By focusing on the customer journey, we can look to provide more information and engagement throughout someone’s Curology journey.”

https://prolink.emarketer.com/content/understanding-customer-engagement




For more on how companies like B2Bs can map the customer journey and tailor their content marketing efforts to that journey, read our April 2019 report, “Creating and Distributing Content for the Customer Journey: How B2Bs Can Influence Audience Behavior Through Strategic Content”.

Finally, external data can help companies to identify factors and influences beyond their control that may be affecting their relationship with their audience—for better or worse. Time of day, seasonality or weather, competitive and economic data are just some of the external data sources that companies may factor in to their segmentation strategies.

“It’s not always historical data on a specific customer that you use to shape a program,” said Dennis Becker, CEO at Mobivity, a company that helps retailers drive customer traffic. “There might be more real-time data like current weather or environmental situations around a store or area to work off of.”

Weather is one of the most common sources of external data. It is used to segment audiences based on anticipated needs. Automotive manufacturers frequently rely on weather inputs to craft segments most inclined to buy specific types of vehicles. Pharmaceutical companies selling allergy medication pay close attention to pollen counts in early spring, and outdoor brands such as REI or The North Face might monitor weather patterns to target audiences likely to attend sporting events in need of rain gear, snow gear or weather-appropriate tailgating equipment.

Competitor data can also provide deep insights on segmentation that extends beyond what one already knows. A marketer may think a customer belongs in a particular segment, but competitor data can provide a more holistic view of the relationship that customer has with the category or industry as a whole.

“A customer might give you 50 trips a year, and you think, ‘Wow, they’re really loyal,’” said Mark Weinstein, senior vice president and global head of customer engagement, loyalty and partnerships at Hilton. “But then you find out they’re a road warrior for business, and they actually stay 60 times a year with your competitor. So yes, the leading indicators of engagement are important, but share of wallet is really critical to make sure we’re getting full engagement.”

HOW COULD OR SHOULD THEY ENGAGE WITH MY BRAND? Segmentation strategies are evolving, so much so that marketers no longer just stitch together data sets to understand their audiences. Many also look to machine learning and modeling to bring those data sets to life.

December 2018 data from consulting and advisory firm Winterberry Group in conjunction with the IAB showed roughly one-third, or $2.26 billion, of the US market for audience data activation investment went to data analytics, modeling and segmentation (which also included measurement and attribution) in 2018.

Today, marketers rely on machine learning and predictive modeling to accomplish a variety of tasks within audience segmentation, including using predictive analytics to create microsegments to identifying lookalikes, new audiences or ideal channel combinations and ad creative for each individual or segment.

Data suggests using machine learning to accomplish these and other tasks is paying off. As part of its annual US Effie Awards, marketing community and resource firm Effie polled 156 US agency and marketing professionals acting as awards judges to see if they thought machine learning has had an increased effect on marketing. More than half of respondents surveyed in March 2019 said yes.

% of respondents, March 2019

Do US Agencies and Marketers Think MachineLearning Has Had an Increased Effect on MarketingEffectiveness?

Yes54.5%

No19.2%

Not investing enough to answer26.3%

Source: Effie Worldwide, "US Effie Awards" as cited by Forbes, April 12,2019246950 www.eMarketer.com

But for machine learning and predictive analytics to work, certain building blocks must be in place. One of the most important: data hygiene.

https://prolink.emarketer.com/content/creating-and-distributing-content-for-the-customer-journey





“A lot of our clients understand there are basic building blocks that need to be in place before you add on propensity and behavioral characteristics and attributes,” said Devon DeBlasio, senior product marketing manager at global information services provider Neustar. “First, you have to understand who that person is, and make sure that data is accurate. It’s like a paint by numbers. If the numbers in those squares aren’t accurate, then the color you place in those squares won’t make a pretty picture.”

Agency DEG Digital uses predictive modeling to augment its segmentation strategy. According to John Stauffer, the agency’s managing director of strategic planning and channel strategy, the firm might use one of multiple models to predict LTV, purchase propensity or basket analysis.

“We might run 5 million customer records through a model and score down to the individual customer level propensity scores with zero being very low and one being very high,” he said. “That’s great insight for us because we can then use that score to test, optimize and send much more targeted messages to that customer list.”

Adobe also relies on machine learning and predictive analytics to better inform client segmentation strategies and understand how and why certain segments are performing. Ryan Fleisch, head of product marketing for Adobe’s Advertising Cloud DSP, said the company uses its AI tool Adobe Sensei to look at distinct segments, for example, purchaser vs. nonpurchaser. By loading all the corresponding first-, second- and third-party demographic, behavioral, psychographic and other data sets into Adobe Sensei, the company can use machine learning to identify commonalities among those who bought—and those who didn’t.

“You might find that people that spent 10 to 15 minutes on sites and visited this page and clicked on this product review have a three times higher propensity to convert, so you might decide to segment those out and go find more users that look like that based on those traits.” Fleisch said.

But predictive analytics isn’t just about validating segments; it can also be used to invalidate segments or data sets upon which segments have historically been built. Oz Lang, director of product marketing for Adobe’s Advertising Cloud TV, said using predictive analytics and machine learning to analyze TV segments often

helps to spot-check decisions about targeting specific demographics or networks. By integrating first-party data with viewership data and all the other customer signals a company is collecting, they can turn to predictive analytics to assess the true value of reaching a target segment in a particular channel.

“You might assume moms watch these particular shows or networks, but those placements might not really be the most effective places to reach this group,” Lang said.

Lang went on to note that this isn’t just about selecting the right shows or networks to target. In fact, by marrying viewership data with all the other segment data, companies can even work backward to understand the incrementality of adding specific data sets to their segments.

You can line up particular demographic, psychographic or customer behavior data with viewership patterns to understand how that segment really indexes,” he said. “And there have been some situations where we’ve identified third-party data that someone’s bought from an external vendor for X amount of money that is undifferentiated. There may be no distinction in performance between Soda Drinker A and Soda Drinker B segments.”

Carolyn Corda, CMO at Adara, a travel data co-op, echoed the importance of using predictive analytics to better prioritize the value of individual data sets and signals. She offered the example of an airline looking to promote various offers around upgrades, their club lounge, rewards programs or other added benefits. To segment audiences into groups, the airline took into account thousands of signals and variables, one of which was pet ownership. In the model, pet ownership had little to do with predicting outcomes; however, it was a data set the marketing team initially latched onto as a meaningful indicator.

“Of those thousands of variables, pet ownership was way down on the list, but people initially gravitated toward it and wanted to tell themselves that it made sense that cat owners wanted to go into the airport lounge, because they were more into comfort and prestige, whereas dog owners were more about upgrades, because dogs are loyal,” Corda said. “But it was such a marginal element in the grand scheme of things. The machine might know it’s number 1005 out of 1010 different characteristics, but it’s the human that picks it out and makes a story up.”


Many interviewed for this report noted the value in also pulling in attribution data to better understand incrementality, specifically with regard to segmenting and targeting audiences across various channels and touchpoints. Neustar’s DeBlasio said he sees clients leveraging both to understand the right combination of media exposure and channel propensity for existing customer segments.

“We can see the incrementality of the actual journey and the peaks and valleys within that journey to understand which combination of channels and messaging is going to increase incrementality for the user,” DeBlasio said. “And for the tactics that are doing nothing for your business, let’s not buy those impressions.”

But like any new automated tactic, marketers must closely monitor these efforts and take the time to ensure the models and algorithms are driving added value.

“If you’re introducing AI, you might have a holdout group that you’re not using it on,” Selligent’s Mullen said. “And then you can compare LTV and customer satisfaction and feedback to the control group vs. the people treated with AI to make sure the AI is actually hitting the mark and delivering what you expect.”

For more on how companies can get started using machine learning and AI for their marketing efforts, read our January 2019 report, “Getting Smart About Artificial Intelligence: Five Best Practices for Diving in”.

PUTTING IT TOGETHER: FIRST- VS. THIRD-PARTY DATA

As marketers look to build their audience segments,

a common question is whether to start with first- or

third-party data.

Historically, marketers have relied heavily on third-party cookies to identify audiences within digital. But as consumers spend greater amounts of time—and advertisers, more ad dollars—in cookieless environments such as in-app and even connected TV, limitations to using third-party cookies have grown. But this is one of the very reasons identity graphs have become more commonplace—to marry cookies with device IDs and even offline data sets.

As noted, the vast majority of marketers use identity graphs today, but they increasingly also layer in their own deterministic, first-party data sets to identify their known customers across the addressable media landscape.

“Owning your first-party data and being able to activate it is an advantage when it comes to advertising moving forward,” The Shipyard’s Grzelak said. “There isn’t just one big idea anymore. It’s about microsegments and understanding audiences even down to the individual.”

FIRST-PARTY DATA “Segments of one” and microsegments were common phrases mentioned by experts interviewed for this report. It’s clear more individual-focused segmentation practices are on the rise as marketers look to take all of their first-, second- and- third-party data sets into account not just for targeting individuals, but more importantly, to personalize communications for them.

For customized clothing retailer Bow and Drape, pairing first-party data with purchase data is a common starting point for audience segmentation, according to co-founder and CEO Aubrie Pagano. But it’s not a fool-proof means of identifying all the potential segments customers could belong to over their lifetime. For example, past purchase data for a 30-year-old female who bought a customized dog sweater would be unable to tell Bow and Drape whether the same customer might also be interested in customized bridal garb.

Here, data gathered from social sites such as Facebook on recent engagements, submissions to their bridal sweepstakes (a promotion they use to grow their first-party understandings of potential customers who fit the segment but may not have bought) and even added third-party data on unidentified site visitors Bow and Drape has recently retargeted may also be used to better understand which customers fit into which segments.

Edwin Miranda, CEO of performance marketing agency KOI IXS, noted that for a telecommunications client, they started with first-party data to understand which products a customer already used. To broaden the segments, they looked at competitor data to identify added products that the customer took advantage of, but with another provider.

https://prolink.emarketer.com/content/getting-smart-about-artificial-intelligence



From there, the segments were further refined based on other characteristics such as existing vs. potential customer, length of contract time (to identify people who frequently change providers), geographical proximity to their stores and competitor’s stores.

“By the time you mix all this first- and third-party data, you end up with many segments,” Miranda said. “The type of media we use for each segment is different. The website experience is different. The images, colors and call-to-action on the website is different for each of them.”

While first-party data may be in high demand, many companies are still struggling to make the most of it. A September 2018 survey of US marketing decision-makers conducted by Advertiser Perceptions and consultancy MightyHive found 44.5% of respondents felt they were using less than 40% of their company’s first-party data potential. And just 4.4% thought they were using 81% to 100% of their potential.

% of respondents

Percent of Their Company's First-Party Data Potentialthat US Digital Marketers Think They Are Using, Sep 2018

0%-20% 14.1%

21%-40% 30.4%

41%-60% 29.6%

61%-80% 21.5%

81%-100% 4.4%

Source: Advertiser Perceptions and MightyHive, "The Data-ConfidentMarketer," March 26, 2019246093 www.eMarketer.com

Getting down to the segment of one sounds good in theory, but it can be difficult to achieve, particularly in the absence of sound data hygiene and statistically significant data sets.

“You need a large enough sample size to understand what is noise and which true traits and dimensions are impactful,” Adobe’s Fleisch said.

Fleisch’s colleague Lang said this is particularly true when pursuing individual-level segments in linear TV. “You have your first-party data, but a lot of times the first-party data represents a small portion of the overall TV viewership audience,” he said.

He said one workaround is employing lookalike audiences in these environments and testing the results to understand whether such audiences behave similarly to customers. “That helps us get more confidence and

assurance that the buys will have the impact we expect them to,” he said.

THIRD-PARTY DATA First-party data may be powerful, but there are some instances where it might not be an ideal first choice for building segments. This is especially true for brands whose first-party data sets are limited, such as CPGs; it is even truer for those whose data sets are stale or inaccurate.

“If it doesn’t give us enough to go off of and feel confident in, then we would look primarily to leverage research in other third-party tools and data,” said Emily Blair, vice president of strategy at buy-side digital partner Goodway Group.

Brands still largely use third-party data for upper-funnel campaigns, relying heavily on syndicated research tools to first identify targets and then translate them to media channels. But it’s not just age and gender demographics that companies use. A host of other third-party data sets can speak to audience behaviors, feelings and attributes beyond the traditional third-party data sets used for brand planning.

Digital advertising agency Neo leverages third-party data sets as well as what they can access via programmatic partners to identify standard audience attributes. They also incorporate behavioral data from partners to identify individual-level interests, for example, turning to Add This or Share This to understand what types of content users are sharing.

“We might identify what their passion points are within tech, news and sports so we can build programs around that to reach audiences,” said Jen Parbus, director and partner at Neo. “There’s a halo effect with the broader audience, but you’re still able to reach these individuals.”

Performance advertisers may also use third-party insights as a starting point for building and identifying audience segments in addressable media. DEG Digital’s Stauffer referred to this as the top-down approach to segmentation, where segments or personas are handed down from on high, be it from a brand team or an in-house research team. He went on to note that in many cases, this might include four or five broad categories built on a mix of demographic and behavioral data.


From there, DEG Digital then looks to pull in first-party customer data (when available) to see if there are logical ways to subsegment based on LTV or transaction patterns. The agency also applies machine learning or predictive analytics to develop propensity scores to further divide audiences into microsegments or all the way down to the individual level.

“We can then go to clients and say, ‘You started with three or four high-level segments, but this tiny segment over here that makes up 2% of your customer base is actually representing 25% of your revenue,” Stauffer said. “That smaller pocket might not have been well-represented in the initial segment, but we can uncover it by doing some advanced analytics.”

Are Third-Party Data Targeting and Tracking Practices in Trouble?

In a word: yes.

When the General Data Protection Regulation (GDPR) went into effect on May 25, 2018, many marketers initially pulled out of open-market buying and eschewed the use of third-party data from resellers and partners they were unsure had collected proper consent.

While EU-facing marketers have gotten their bearings since then, the impending arrival of the California Consumer Privacy Act (CCPA) in July 2020 is likely to resurface many of these initial concerns.

“The reality is, there are proposed bills on a state-by-state level in 15 to 20 states right now,” said Jason Grunberg, vice president of marketing at email, web and mobile platform Sailthru. “And we’re getting to the point where this is going to be a national agenda. If you’re an organization—whether you’re on the brand or vendor side—you have to be thinking about the most progressive states and looking at consent and data management from that perspective.”

In addition to GDPR and CCPA, a host of other factors are already limiting the collection and use of many of the third-party data sets listed above. For one, we predict a quarter of US internet users block ads. Apple’s Intelligent Tracking Prevention (ITP) 2.1 and 2.2 are other roadblocks. Under ITP 2.2, all Safari browsers block third-party cookies and limit first-party cookie windows to 24 hours. And Google’s May 2019 announcement that it will start to require developers to specify for Chrome the types of cookies they use—and allow consumers to opt out of their use—will undoubtedly continue to upset the balance of third-party data usage.

Many see the consented use of first-party data as one workaround to the third-party data woes. But such challenges also have the industry discussing the viability of “zero-party data,” which is data owned directly by the consumer that could then be shared with advertisers and marketers.

As talks of educating consumers and empowering them with their own data management rights increases, it will be interesting to see if empowering consumers to control and share their own data can be a scaled reality—and not just a limited endeavor.

KEY TAKEAWAYS ■ Understanding ones’ customers is a necessity for digital marketers and their companies today. And in order to craft an exceptional customer experience—or any customer experience at all—marketers must first know who their audiences are.

■ To segment audiences within digital and addressable media, many start the process with first- or third-party data. Each approach offers its own advantages and challenges. Growing concerns around the use of third-party data, as well as greater access to first-party data, has many companies looking to take a more first-party-data-forward approach.

■ Within the broad categories of first- and third-party data, there are many types of data to choose from. From demographics and psychographics to sentiment and external data, marketers have no shortage of signals to choose from. When deciding which to choose, marketers should ask two key questions: Who is my audience? And how engaged is my audience with my brand today?

■ Savvier marketers are also pairing those data sets with predictive analytics and machine learning to understand how those segments could or should be engaging with their brand. While it’s still early in the process for many companies, the benefits and insights can be substantial.

EMARKETER INTERVIEWS

Dennis Becker CEO

Mobivity Interviewed on April 23, 2019

Brad Birnbaum Founder, CEO

Kustomer Interviewed on April 25, 2019


Emily Blair Vice President, Strategy

Goodway Group Interviewed on April 25, 2019

Jane Clarke CEO, Managing Director Coalition for Innovative Media Measurement (CIMM)

Interviewed on April 22, 2019

Karen Cohen Director, Product Marketing

AppsFlyer Interviewed on April 24, 2019

Carolyn Corda CMO

Adara Interviewed on April 25, 2019

Devon DeBlasio Senior Product Marketing Manager

Neustar Interviewed on April 23, 2019

Ric Elert President

Conversant Interviewed on April 22, 2019

Michael Fisher, Ed.D. President

Fluent Dialog Interviewed on April 4, 2019

Ryan Fleisch Head of Product Marketing, Advertising Cloud DSP

Adobe


Jason Grunberg Vice President, Marketing

Sailthru Interviewed on April 25, 2019

Dave Grzelak Chief Strategy Officer

The Shipyard Interviewed on April 24, 2019

Steve Gutentag Co-Founder, CEO

ThirtyMadison Interviewed on April 29, 2019

Tony Hudson Media Solutions Lead

Civis Analytics Interviewed on April 16, 2019

Dan Jaye Co-Founder, CEO

aqfer Interviewed on May 20, 2019

Nick Jordan Founder, CEO

Narrative I/O Interviewed on April 25, 2019

Oz Lang Director, Product Management, Advertising Cloud TV

Adobe


Katie Malone Director, Data Science Research and Development

Civis Analytics


Shane McAndrew Chief Data Strategy Officer

Mindshare US Interviewed on April 24, 2019

Edwin Miranda CEO

KOI IXS Interviewed on April 24, 2019

Jackie Mockridge Mattina Senior Vice President, Consumer Insights and Analysis

360i


April Mullen Director, Consumer-First Marketing Adoption

Selligent Marketing Cloud Interviewed on April 25, 2019


John Nardone CEO

Flashtalking Interviewed on April 24, 2019

Doug Novack Managing Director, Business and Industrial Markets

Google

Interviewed on May 3, 2019

Chris O’Hara Vice President. Product Marketing, Marketing Cloud

Salesforce


Aubrie Pagano Co-Founder, CEO

Bow and Drape Interviewed on April 25, 2019

Jen Parbus Partner, Director

Neo Interviewed on April 25, 2019

Alexis Perlmutter Lead Product Manager


Fabian Seelbach CMO

Curology Interviewed on April 30, 2019

John Stauffer Managing Director, Strategic Planning and Channel Strategy

DEG Digital


Todd Thompson Senior Vice President of Data, Insights and Customer Engagement

RRD Marketing Solutions


Mark Weinstein Senior Vice President and Global Head of Customer Engagement, Loyalty and Partnerships

Hilton

Interviewed on May 30, 2018

Jay Tandan US Digital Marketing Manager

Ben & Jerry’s Interviewed on April 22, 2019

Jonathan Williams Applied Data Science Lead


Ziggy Zografakis Director, Marketplace Partnerships

dataxu Interviewed on April 23, 2019

READ NEXT

Creating and Distributing Content for the Customer Journey: How B2Bs Can Influence Audience Behavior Through Strategic Content

Customer Experience 2018: Personalization Still Elusive as Marketers Seek to Answer a Single View of Customer

Getting Smart About Artificial Intelligence: Five Best Practices for Diving in

Loyalty Marketing: Data Fuels Personalization Strategies and Better User Experiences

Seven Marketing Tech Trends for 2019: Customer Data Is Still at the Center

Understanding Customer Engagement: How to Map and Make Sense of the Metrics that Matter

Digital Display Advertising 2019: Nine Trends to Know for This Year’s Media Plan




https://prolink.emarketer.com/content/customer-experience-2018





https://prolink.emarketer.com/content/loyalty-marketing

https://prolink.emarketer.com/content/loyalty-marketing

https://prolink.emarketer.com/content/seven-marketing-tech-trends-for-2019

https://prolink.emarketer.com/content/seven-marketing-tech-trends-for-2019



http://prolink.emarketer.com/pro/content/digital-display-advertising-2019

http://prolink.emarketer.com/pro/content/digital-display-advertising-2019


SOURCES

Advertiser Perceptions

Clicktale

Cognetik

CX Network

Effie

The Harris Poll

Interactive Advertising Bureau (IAB)

LoopMe

MightyHive

RedPoint Global

Sapio Research

SharpSpring

Social Media Today

UserTesting

Winterberry Group

EDITORIAL AND PRODUCTION CONTRIBUTORS

Anam Baig Senior Editor Joanne DiCamillo Senior Production Artist Katie Hamblin Chart Editorial ManagerDana Hill Director of ProductionErika Huber Copy EditorAnn Marie Kerwin Executive Editor, Content StrategyStephanie Meyer Senior Production ArtistHeather Price Managing Editor, ContentMagenta Ranero Senior Chart EditorAmanda Silvestri Senior Copy Editor

http://Advertiserperceptions.com

http://Clicktale.com

http://Cognetik.com

http://CXNetwork.com

http://Effie.org

http://Theharrispoll.com

http://Iab.com

http://Loopme.com

http://mightyhive.com

http://Redpointglobal.com

http://Sapioresearch.com

http://Sharpspring.com

http://Socialmediatoday.com

http://Usertesting.com

http://Winterberrygroup.com

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