the predictive journey -...

IN ASSOCIATION WITH:

SURVEY ON PREDICTIVE MARKETING STRATEGIES2015

THE PREDICTIVE JOURNEY

Introduction—Marketing by the Numbers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2

Survey Overview and Methodology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Highlights of Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Predictive Marketing Enters the Mainstream . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Return on Investment and What Mature Enterprises Are Learning . . . . . . . . . 16

Skills and Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Lessons Learned and Recommendations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

CONTENTS

2 | THE PREDICTIVE JOURNEY

INTRODUCT ION—

MARKET ING BY THE NUMBERS

We live in a time of continued rapid change. We’ve reached a point in which data is now being generated from every corner of the en-terprise—from customer transactions to social media sentiment to inventory status. Channel sets—mobile phones, screen sizes, smart watches and other gear—are still rapidly evolving, and customers are becoming more demanding. At the same time, the digital revolution brings a plethora of new data sources of unforeseen mag-nitudes to bear.

This data, aggregated, analyzed and made action-able, has the potential to become a competitive force that can advance organizations well ahead of their competitors. But is this data being put to the best use? Are decision makers even aware of the value of the data now being stored within their enterprises? If so, are the people, processes and technology in place to make this happen?

It’s no longer enough to invest in marketing campaigns that are evaluated by gut-level con-clusions as to what customers are doing or what they prefer. The key is knowing what to do with the data companies have, but much of it still remains hidden or obscured from decision

makers. Enterprises need to be able to leverage the data they have on customers, operations and market conditions, as well as external data about prospective buyers, and turn this data into in-sights. True competitive advantage comes from the ability to employ this data to not only better understand the state of markets, but to be able to make connections, see what’s about to happen and especially, to take action on these insights.

This is the promise and potential of predictive marketing, which employs and analyzes data to forecast the most likely outcomes of market-ing campaigns. Predictive marketing is emerg-ing as the best strategy to embrace data analytics to guide decisions and increase the visibility of markets. While it is still the early stages for this strategy, it won’t be long until most organiza-tions will be investing in it. Predictive marketing is poised to enter the mainstream, and those or-ganizations that move forward with it will lead their markets.

Marketing leaders need to collect and explore patterns in their data that will help them pin-point potential opportunities across the revenue funnel. This often involves historical data about

INTRODUCT ION—

MARKET ING BY THE NUMBERS

Imagine being able to predict the buying habits of your customers before they even visit your site. Picture being a hero to your organi-zation, alerting department managers to sudden spikes in demand that weren’t even anticipated. Contemplate the power of being able to see around corners, and knowing where to invest resources and money well before trends become obvious. With today’s technology and data, these aren’t pipe dreams, they are real and achievable goals.

customer engagements, transactions and sales, and makes that learning available across the en-terprise for ongoing and future initiatives.

Such insights extend well beyond the bounds of marketing and are critical for all parts of the enterprise. Sales managers need to understand who the most likely prospects are for new prod-uct or service o!erings. Procurement managers need to know how much computing capacity, raw materials or supplies are needed to sus-tain the business in the months ahead. Human capital managers need to understand upcoming sta"ng requirements, and where skills demand will be. Operations managers need to be able to coordinate production schedules. Distribution managers require advance notice for shipping runs. Supply-chain partners need to prepare for potential surges in demand.

Predictive analytics and marketing cover a broad range of applications, says Ravi Dhar, professor of management and marketing as well as di-rector of the Center for Customer Insights at the Yale School of Management. The common denominator, he says, is to “be able to predict what it would take to encourage a desired cus-tomer behavior.” Such desired behavior includes a range of activities, from upselling, cross-selling or channel shifts, to customer migration from a bricks-and-mortar store to an online store. Ad-ditional predictive marketing approaches may include “optimizing prices, identifying custom-er needs more appropriately, machine learning, pricing analysis, unstructured data analysis, text analysis, social media and predicting what cus-tomers will end up buying,” Dhar adds.

The following trends are driving this shift to-ward predictive marketing:

of data from devices, computers, communi-cations and transactions that provide clues about their intentions.

-diences.

delivery of goods and services—marketers need to help their organizations anticipate future demand.

to changing circumstances and customer preferences.

While the goals of predictive analytics take it well beyond marketing, it ultimately always ties back to marketing in one form or another, says Paul Sallomi, vice chairman and the Global and US Technology Sector leader for Deloitte LLP. “At the enterprise level, not all insights are di-rectly a!ecting the marketing channel,” he says.

sensors that deliver streaming data that helps pre-dict repair needs in advance of having a problem while in #ight. This is not a direct marketing concern, but it reshapes the level of engagement between the manufacturer and the purchasers of such products. While not under the purview of the marketing department, predictive “changes the way the provider thinks about the product, and the way the consumer thinks about the val-ue from the product,” he points out. “It changes the sales relationship, and it changes how the product is marketed.”

COPYRIGHT © 2015 FORBES INSIGHTS | 3


Forbes Insights and Lattice Engines would like to thank the following individuals for their time and expertise:

Ravi Dhar, Professor of Management and Marketing and Director of the Center for Customer Insights at the Yale School of Management

Paul Sallomi, Vice Chairman and Global and US Technology Sector Leader, Deloitte LLP

John Smits, Data Science Operations and Chief Data O"cer, EMC

Shashi Upadhyay, CEO and co-founder, Lattice Engines

Andrea Ward, Vice President of Marketing for the Oracle Marketing Cloud

This report is based on a survey of 308 executives based in North America, con-

ducted by Forbes Insights in April 2015. All respondents represented companies

with $20 million in annual revenues or greater—30% report revenues exceeding

$1 billion. The majority are chief marketing officers of enterprises, or vice presi-

dents or directors of business units. Executives surveyed represent a range of

industries, led by industrial goods manufacturing, media, technology software

and systems, and financial services.

Forbes Insights also conducted one-on-one interviews with industry leaders—

marketing executives with leading organizations and analysts/thought leaders—

to add context to the survey findings.

SURVEY OVERV IEW AND METHODOLOGY

ACKNOWLEDGMENTS


The vast majority of marketing leaders (84%) intend to ramp up spending on marketing tech-

nologies and initiatives.

An overwhelming majority of executives with experience in predictive analytics (86%) indicate

the technology has already delivered a positive return on investment.

In terms of skills sought, analytics/predictive

analytics are in high demand at more than two-thirds (68%) of organizations.

Close to six in 10 organizations (58%) report accurately measuring marketing

Overall, customer value metrics are used by 36% of

organizations. Predictive analytics also plays a key role in sales and revenue forecasts, as well as

risk assessments.

HIGHL IGHTS OF KEY F INDINGS


Predictive analytics is on most marketing agendas. However, the journey has only begun. The vast majority of organizations intend to increase the role of predictive analytics in their marketing processes over the next 12 months.

Predictive marketing has caught the interest of most enterprises, but executives are only start-ing to explore the possibilities of this emerg-ing technology. Interest in predictive marketing is developing in tandem with the opportunities big data analytics is opening up to enterprises. Greater data-driven decision making helps iden-tify future customers, current customers likely to increase their engagements with the business, and the propensity of customers to buy certain products or services.

Predictive marketing is becoming a core part of enterprise marketing initiatives.

increase the role of predictive marketing over the coming year (Fig. 1). Accordingly, invest-ments in predictive analytics tools and method-

they will increase spending over the next 12

Predictive marketing is possible today because there is a wealth of data now available to organi-zations from internal and external sources, com-bined with the power of increasingly sophisti-

cated data management systems and analytics software that can bring this data to life. Fueling this trend even further is the emergence of em-powered, online customers who are constantly feeding data through their devices and comput-ers to enterprises in the form of intent data.

“Whenever customers or prospects are search-ing online, posting comments in communities or social media, or reading blogs or articles, they are showing intent or future interest. Intent data can be incredibly helpful at $nding buyers who have a need now, especially when paired with $t and behavior data,” Shashi Upadhyay, CEO of Lattice, says. “B2B selling is committee driven. The person who has speci$cally expressed in-tent—downloaded a white paper, done a search, etc., for a solution—may not be the economic buyer, such as the VP or C-level decision maker. It may be the intern doing research on behalf of the decision maker. Would you rather get the ad in front of the intern or would you rather get the ad in front of the VP or C-level decision maker? Your marketing budget is better spent when targeting the decision maker.”

PREDICT I VE MARKET ING

ENTERS THE MA INSTREAM

Predictive marketing employs and analyzes data to forecast the most likely outcomes of marketing campaigns, and is considered the best strategy to embrace data analytics to guide decisions and increase the visibility of markets.

Note: Does not add to 100% due to rounding.


Organizational support Will increase 10%-20%Will decrease

Will remain the same

Will increase less than 1%

Will increase 1%-5%

Will increase 6%-10%

Will increase 20%-25%

Will increase more than 25%

Don’t know/unsure

2%5%5%

5%

16%

27%

28%

10%

3%

MARKETING TECHNOLOGY PURCHASING PLANS OVER THE COMING YEAR

2FIG.

Yes, significantly

In some cases

No

Helpful partner/consultant

Don’t know/unsure

Data analytics will be lessened

45%38%

12%

0%

2%

1FIG.

INTEND TO INCREASE ROLE OF PREDICTIVE ANALYTICS IN MARKETING PROCESSES OVER NEXT 12 MONTHS?


Ultimately, there is a direct connection to the bottom line. The ability to determine which customers will respond to a promotion or new product enables companies to directly apply their resources to those segments. “We have so many products that we bring to market. Having our reps knock on every door of every custom-er and promoting every one of our technolo-gies is not viable,” says John Smits, data science operations and chief data o"cer at EMC. “We developed algorithms to identify which of our customers is more ready to buy a certain tech-nology platform or not. There are also non-cus-tomers or prospects in the market. The trick is knowing which of those prospects have a busi-

ness environment that could bene$t from our technologies, or maybe are reaching a critical growth point in their internal structure that re-quires additional IT development. We use ana-lytics to target those.”

The targeting and segmentation opportunities enabled through predictive marketing may be powerful in many ways. “There are so many op-portunities to engage di!erently with custom-ers,” says Andrea Ward, vice president of mar-keting for the Oracle Marketing Cloud. “All of it comes back to great targeting, segmentation, making sure you get the right message to the right person at the right time. All of that can be helped with great predictive tools.”

3WHEN PREDICTIVE MARKETING TECHNOLOGIES WERE ADOPTED

FIG.

Haven’t used yet

6-12 months ago

5 or more years ago

Haven’t used yet, but plan to within the next 6-12 months

1-2 years ago

Don’t know/ unsure

Less than 6 months ago

3-4 years ago

8%

13%

22%

15%23%

8%

7%

3%


Ultimately, predictive marketing helps deliver better returns on marketing investments. “You’ll be assured that you’re investing in the right things, the right market and the right messages,” says Ward. “Predictive marketing can help with all of those conversations.”

In the process, the role of marketing will be el-evated within enterprises, says Ward. “Technol-ogy enables marketers to measure outcomes in a much better way, and also demonstrate how marketing impacts the rest of the organization.” As a result, predictive marketing helps connect marketing with other units across organizations. “We’re able to show how marketing, service, commerce, social, all these things play together

and how to contribute and improve the cus-tomer experience—all of that’s now measurable and more connected,” says Ward. “Technology is giving marketing a seat at the table.”

While predictive analytics is on most marketing

-sider their organizations to be highly advanced

Organizational support Highly advanced—predictive marketing is a core function within most or all marketing initiatives

Just starting out, no initiatives yet

Piloting/experimenting

Advancing—e!orts have been completed and have

delivered results

Don’t know/unsure

13%

42%

1% 13%

31%

CURRENT STATE OF PREDICTIVE MARKETING

4FIG.


HOW PREDICTIVE MARKETING TECHNOLOGY IS USED

5FIG.

Predictive marketing is possible due to the abun-dance of data coming in from a range of internal and external sources. Online web services, along with internally generated data, mean organiza-tions have large stores of data to sort through to pinpoint what is of value. Typically, predictive

marketing is being employed across a range of marketing functions, especially for measuring the e!ectiveness of marketing programs, as well as sales support. Other areas employed include segmentation, $nding new prospects and recom-mendations (Fig. 5).

Marketing measurement

Find new prospects

Churn prediction

Sales support

Recommendations

Lead scoring

Don’t know/ unsure

Segmentation

Risk and fraud detection

60%

46%

39%

33%

30%

37%

22%

14%

1%


Website data 47%Demographics 44%Online transactions 41%Social 39%Mobile app data 37%Behavioral data 32%Customer/subscription or CRM data 29%Social metrics 28%Share of customer base/market 27%Products/services previously purchased 26%Search 26%Proprietary corporate data (i.e., call center logs, brand surveys, etc.) 26%Campaign metrics 25%Website content consumption 25%Physical store transactions 24%Call center 23%Data aggregators 21%Third-party data from sources like Experian, LexisNexis, Dun & Bradstreet, etc. 19%Cookies 17%Catalog transactions 16%Clickstream 16%Last-touch/multi-channel/multi-touch attribution 15%

TYPES OF PREDICTIVE MARKETING DATA

6FIG.

Predictive marketing draws on an array of key data sources. Currently, most executives rely on data from website visits for analysis, along with demographic data and analysis of online trans-

actions. Other sources frequently employed in predictive marketing e!orts include social me-dia–based data, mobile app data and customer behavioral data (Fig. 6).


Predictive analytics needs to be an enterprise ef-fort, gathering and sharing data from across the organization, to better support downstream op-erations such as production, shipping and cus-tomer service. However, most of the data that would form the foundation of predictive mar-

-

information is now readily accessible to decision makers in a single, highly integrated presentation

less of their total enterprise data is ready and available (Fig. 7).

While enterprises are still having di"culty bringing all these data sources together for anal-ysis, executives are ready and willing to begin to share the results of their predictive marketing

that insights that are generated through predic-tive marketing are made widely available across their enterprise, to be shared with other parts of

“Generally, the most accurate data models are leveraging multiple data sources,” says Smits. “There’s a complexity in bringing together var-

ious disparate data sets. What we learned pretty early on was the need for a master data manage-ment (MDM) strategy that starts to standardize and normalize these data sets, before you build true predictive insights. To bene$t from true in-sights and accuracy, you need to bring togeth-er multiple data sets. To do that, you need this MDM piece, to be able to structure these data sets so they can connect to each other. And you need to process this information very quickly.”

Much of the data that may be available for predictive analysis tends to “reside on many di!erent computers and systems that don’t speak with each other,” says Dhar. “It’s very hard

For example, in a bank, the data on the bank account is in one place, the mortgage is anoth-er, the equity loan is in a third place, and these systems are often not very well integrated. Func-tions such as accounting and sales often are in separate domains as well. Nobody can access, or

integrate those,” he adds.

This is a problem seen across all types of busi-nesses—even the most advanced web compa-

51%-75%Little to none

1%-25%

25%-50%

76%-100%

Don’t know/unsure

40%

18%

3%10% 4%

25%

PERCENTAGE OF ENTERPRISE INFORMATION READILY ACCESSIBLE IN SINGLE FORMAT

7FIG.


nies, Dhar adds. Often, partnerships are required to complete the view of the customer. “Any one business often only takes a sliver of a view,” he says. “Even companies considered to be better in terms of using data often don’t have the whole view. If Google, for example, wants predictive analytics around how its searches leads to pur-chases, it doesn’t have a good way to connect those dots. It only knows the searches, and it doesn’t have a good way to know how long it took people to buy, or how many purchases they made. But if you combine Google with Visa or

American Express or MasterCard, now you have a very powerful combination of not only what people are searching for, but what did the searches lead to.”

Executives were also asked about the factors that

most cases, the survey $nds, two factors come together to help deliver successful outcomes: ef-fective technology choices and organizational

E!ective technology choices

Organizational support

Top management buy-in

Helpful partner/consultant

Don’t know/unsure

Other

63%

57%

47%

28%

2%

1%

9SUCCESS FACTORS FOR CURRENT PREDICTIVE ANALYTICS PROJECTS

FIG.

76%-100%

Don’t know/unsure

Insights are used only within our department

Yes, insights are made widely available across our organization

Insights are shared with some business units Don’t know/unsure

6% 1%

41% 52%

PREDICTIVE MARKETING INSIGHTS MADE AVAILABLE TO ENTERPRISE?

8FIG.


Predictive marketing delivers greater intelligence and insights to marketing campaigns, but as with any promising technology, it’s not without its challenges. The ability to measure the results generated by the data is a top challenge. There is also the challenge related to the fact that a great deal of enterprise data is siloed or unavailable to

di"culty identifying and capturing key data and analytics. Even when the data is made available, it often may sit in reports without further atten-

to convert the data they generate into actionable formats. Securing needed budgets and funding also is a constant issue (Fig. 10).

EXECUTIVES WITH MORE EXTENSIVE EXPERIENCE WITH

PREDICTIVE MARKETING WERE ASKED WHAT THEIR

MOST SUCCESSFUL EFFORT HAS BEEN TO DATE. SOME

PROGRAMS CITED INCLUDE THE FOLLOWING:


The challenge is to also bring outside data into the analysis picture, along with existing internal information. “If you’re looking at doing better targeting, or better segmentation or messaging, there might be pieces of data that you within your own company aren’t collecting that could help you get a better view of your ideal custom-er,” Ward points out. “Frankly, it might be other things you wouldn’t even think about. There might be a lot of attributes that you or your company don’t have access to or collect that can be provided to give you better predictors of what your customer looks like.”

With the rise of big data—particularly in its va-riety of formats and structures—predictive mar-keting becomes “both a challenge and an oppor-tunity,” says Sallomi. “Pattern recognition started with text. Now it’s starting to move into pictures and a lot of other areas. As we begin to analyze pattern recognition in new ways, then we start to weave together a pretty wide variety of sources of new data. It’s going to be a while before true standards emerge across many of these dimen-sions, but the market is going to move ahead anyway.”

Accurately measuring marketing campaign/initiative results

Securing needed budgets and funding

Finding the right skills for current and future needs

Identifying and capturing key data and analytics

Identifying and managing appropriate marketing technology

Training and nurturing the right skills

Making data actionable for business results

Raising marketing’s visibility with senior leadership

Aligning marketing goals with corporate goals

58%

52%

51%

46%51%

46%

44%

42%

39%

10MARKETING CHALLENGES(Percentage rating strategy as “4” or “5” on a scale of 1 to 5, with 1 meaning the factor is not a challenge, to 5 meaning the factor is a “significant challenge”)

FIG.


about signi$cant growth and opportunities for organizations. As with any promising transfor-mation, it requires a learning curve to develop, in terms of both organizational learning as well as individual skill levels. Those companies fur-ther along with predictive marketing—that are highly advanced or with several years of experi-ence—are already demonstrating results. Predic-tive marketing e!orts have quickly delivered re-sults for the investments made, delivering more targeted and smarter marketing campaigns. Any successful predictive marketing initiative brings together many parts of the enterprise. Organi-zational support and business involvement is key.

As mentioned in the previous section, the study identi$ed distinct stages of growth in the pre-

-nizations high on the maturity curve have had predictive marketing initiatives under way for $ve or more years (Fig. 12). The three stages of development among organizations that are cur-rently working with the technology include the following:

Early-stage organizations, which are piloting, experimenting or launching their $rst e!orts,

percent of this segment plan implementations

had e!orts under way for up to a year.

Advancing organizations, -vey respondents, are those who report that their predictive marketing e!orts have been completed in parts of their businesses, and these programs have delivered results. Of this

under way for a year or more.

Highly advanced organizations,of executives, consist of those who con$rm that predictive marketing is a core function within most or all of their marketing initia-tives. They are the most experienced segment

-tives under way for $ve or more years.

Predictive marketing initiatives that have been under way for some time are delivering impres-

-tives that have been overseeing predictive mar-

increased return on investment (ROI) as a result of their predictive marketing. Only a handful,

-creased return (Fig. 12).

RETURN ON INVESTMENT AND

WHAT MATURE ENTERPR ISES ARE LEARNING

An overwhelming majority of executives with experience in predictive analytics indicate the technology has already delivered a positive return on investment. More highly experienced enterprises report greater sharing of data and higher levels of automation.

Note: Does not add to 100% due to rounding.


Organizational support Increased returns 10%-25%Decreased returns

No impact on returns

Increased returns 1%-5%



Increased returns by more than 50%

Don’t know/unsure

1%10%

4%

10%

16%

25%

4%

31%

PREDICTIVE MARKETING RETURN ON INVESTMENT

12FIG.

PILOTING ADVANCING HIGHLY ADVANCED

Not implemented yet 11% 3% 0%Implementing in 6 months 28% 4% 0%

21% 27% 10%6-12 months 26% 26% 15%1-4 years 11% 36% 25%5 or more years 1% 3% 45%

11PREDICTIVE MARKETING EXPERIENCE

FIG.


Mature users of predictive analytics technol-ogy are more likely to be experiencing tangible gains to their bottom lines. Close to half of these

-

One of the primary bene$ts of predictive mar-keting is that it enables a much greater degree of focus, executives report. Upadhyay explains, “This includes the ability to better identify mar-

ket opportunities, better ad targeting, improved nurture programs and more targeted accounts.” The primary value organizations are seeing is being able to better identify market opportuni-

marketing resources also leads as a bene$t—bet-

also enhanced by predictive marketing, cited by


No impact/decreased returns 11% 4% 0%Increased returns 1%-10% 39% 67% 34%Increased returns 10%-25% 24% 12% 17%Increased returns by more than 25% 8% 5% 48%

PREDICTIVE MARKETING RETURN ON INVESTMENT—BY MATURITY STAGE

13FIG.

BUSINESS VALUE GAINED FROM PREDICTIVE MARKETING

14FIG.

Better identify market opportunities

Better assess customer attitudes

Achieve higher click-through rates

Better ad targeting

More e!ectively target content

Better funnel conversions

More targeted accounts

Increase lift from marketing campaigns

Higher close rates

46%

40%

39%

37%38%

34%

29%

26%

26%

1% Don’t know/unsure


As observed in the previous section, organiza-tional support and technology choices are the key factors behind existing success stories. These factors increase as predictive marketing e!orts mature into more highly advanced initiatives. As these e!orts move forward and demonstrate their value, the advantages of predictive market-ing are recognized and provided with greater support (Fig. 15).

Having access to plentiful data sources both inside organizations as well as externally is key to predictive marketing. As organizations gain more experience and advance in their predic-tive marketing e!orts, the scope of enterprise

data also widens. More than two-$fths of highly advanced enterprises say that most of the enter-prise data across their infrastructure is available for analysis; this expands from about one-fourth to one-$fth in earlier stages of the evolution (Fig. 16).

The insights that are ultimately generated through predictive analytics engines tend to be widely distributed in enterprises leading the way with analytics. Close to three-fourths of executives with highly advanced organizations report they are sharing this information across their enterprise, with downstream operations such as design or production (Fig. 17).


68% 59% 74%Organizational support 55% 52% 71%Top management buy-in 58% 36% 71%

18% 32% 37%


(>50% of enterprise data available) 19% 26% 43%

PREDICTIVE MARKETING SUCCESS FACTORS—BY MATURITY STAGE

ENTERPRISE DATA VIEWS—BY MATURITY STAGE

15

16

FIG.

FIG.

Piloting

Advancing

Highly advanced

42%

47%

74%

PREDICTIVE MARKETING INSIGHTS SHARED WIDELY ACROSS ORGANIZATIONS— BY MATURITY STAGE

17FIG.


“Success requires participation from all disci-plines—from IT, from sales, from marketing, from $nance and from business unit leaders,” says Sallomi. “And the data points that come in—whether from call centers, customer demo-graphics, customer preferences or propensity to buy—need to be mashed up in a platform that provides a holistic picture.”

the most common type of metric employed to measure the success of predictive marketing

targeted and new customers to determine their programs’ success. Additional metrics used as

ROI, cost per lead and total conversions. Cus-tomer acquisition costs are another determinant

Customer retention rate

Projected return on investment

Marketing influenced customer percentage

Customer value

Total conversions

Customer acquisition cost (CAC)

Channel-specific tra"c

Cost per lead

Marketing percentage of customer acquisition cost

Ratio of customer lifetime value to customer acquisition cost

Lead to close ratio

Time to payback customer acquisition cost

Bounce rate

41%

36%

32%

32%32%

31%

31%

28%

25%

28%

25%

23%

17%

26%Total visits

PREDICTIVE MARKETING SUCCESS METRICS

18FIG.


As predictive marketing e!orts move forward and gain greater scale, the need to apply greater automation increases. Highly advanced predic-tive marketing organizations are more likely to have automated these processes than the earlier-


Sales and revenue forecasts 53% 57% 77%Risk assessments 53% 58% 69%Upsell/cross-sell opportunities 45% 49% 51%

42% 45% 46%Customer value 37% 39% 63%Likelihood of customer responses 39% 37% 46%

AUTOMATION LEVELS—BY MATURITY STAGE

19FIG.


The key to successful predictive marketing is the ability to pull together many parts of the enter-prise. Ultimately, predictive marketing extends well beyond the marketing department—infor-mation moves both upstream and downstream, providing guidance not only to top-level execu-tives, but also to production, procurement and sales departments, as well as supply-chain part-ners and vendors. This calls for not only techni-cal and data analysis skills, but also business com-munications to help all parts of the enterprise participate in and bene$t from a predictive mar-keting strategy.

The rise of predictive marketing means a grow-ing integration between marketing executives and technology specialists. Marketing teams need to better understand the implications of data science and move their organizations to-ward competing on analytics. This calls for more analysis tools and skills. Organizations that de-velop and leverage these capabilities and embed them into their full range of marketing activities will have the advantage in their markets.

Organizations need highly skilled professionals, versed in everything from data science to con-tent development, to make their analytics e!orts

out internal training programs that will help de-velop such skills.

However, more important than acquiring the right skills is leadership. “Any sort of change $rst requires leadership,” says Deloitte’s Sallomi. “It requires creative minds. It requires ideas. It requires a clear management platform and infra-structure, partnering together a solution that has a clear measurable outcome. It certainly requires IT and analytics and software-based skills.”

Executives report there is an intense need for the skills that can deliver predictive marketing capabilities. Analytics/predictive analytics skills

operations skills are the most valuable commod-ity (Fig. 20).

SK ILLS AND TR A INING

Predictive analytics skills are in great demand. In terms of skills sought, analytics/predictive analytics are in high demand at more than two-thirds of organizations.


68%Digital content development and management 63%Marketing operations 61%Digital channel management 60%Demand generation 60%Social media 56%

55%Mobile 55%Industry sector specialization 55%Communications/PR 54%SEO/content distribution 52%

MARKETING SKILLS IN DEMAND(Percentage rating strategy as “4” or “5” on a scale of 1 to 5, with 1 meaning the skill is not important, to 5 meaning the skill is “extremely important”)

20FIG.


Organizations are undertaking a variety of training and support approaches to build their analytics and predictive marketing skills bases. There is wide adoption of on-site training and education for employees, as well as informal but ongoing approaches such as coaching and men-toring (Fig. 21). At EMC, the talent gap led the

organization to develop its own custom train-ing program, along with its own certi$cations. “It’s a team sport,” says Smits. “When you cou-ple together complexity of data, complexity of business problems you’re trying to solve, you need a team of people that have deep expertise in each one.”

On-site training and education

O!-site training and education

Don’t know/unsure

Coaching and mentoring

Support for outside courses at local universities/colleges/technical schools

None at this time

Access to training videos and websites

Other percentage of customer acquisition cost

71%

61%

49%

35%40%

2%

1%

2%

PREDICTIVE MARKETING TRAINING AND SUPPORT OFFERED TO EMPLOYEES

21FIG.



Coaching and mentoring 63% 59% 63%58% 48% 54%

On-site training and education 76% 72% 69%39% 42% 40%

Support for outside courses at local universities/colleges/technical schools 47% 42% 46%

PREDICTIVE MARKETING TRAINING AND SUPPORT OFFERED TO EMPLOYEES— BY MATURITY STAGE

22FIG.

In terms of predictive marketing maturity, those organizations that are early in the process are

-parts to be encouraging and supporting ongo-ing training and education. Organizations still

in the early stages of their predictive marketing e!orts recognize even in these early stages that employee training in new techniques is essential (Fig. 22).


Customers are not going to wait for organiza-tions they are buying from to understand their needs, and competitors are certainly not going to sit it out until the organizations $nally under-stand what they’ve been missing in their com-pany and beyond. Predictive marketing is fast becoming a key element of data-driven orga-nizations. With predictive marketing, enterprises can employ machine learning, combined with a wealth of data, to cut through the noise and clutter of today’s markets to identify well-target-ed insights on customer trends and preferences. Valuable insights about potential shifts in mar-kets or customer groups is all contained within the big data coming out of transactional sys-tems, social media, customer data and external resources. Upadhyay explains that “the results are high levels of personal engagement with customers, and more cost-e!ective and targeted lead generation, campaign optimization, market segment analysis and prospect quali$cations.” Businesses now have the ability to successfully embed and integrate these insights into business processes, so they are an automatic feature that doesn’t require additional manual intervention or scripting.

The challenge is to reduce the gaps among sales, marketing, operations and other parts of the business that are exacerbated by siloed data and systems. “Predictive marketing is an enterprise-scale endeavor, so better integration of data from across all these silos is a key step for enterprises moving towards predictive,” adds Upadhyay.

predictive analytics. As the solution expands within organizations, it will be a critical part of the evolution to data-driven organizations.

-rience in predictive marketing are seeing signi$cant results. They report greater orga-nizational and training support, which have moved their e!orts forward.

is a key challenge. More work needs to be done to bring in and integrate data from internal enterprise as well as external sources.

predictive analytics success in organizations.

LESSONS LEARNED AND RECOMMENDAT IONS

As this new survey shows, the time for organizations to embrace predictive marketing is now.


Data needs to be not only integrated but more aligned with the customer life cycle. In the In-ternet economy, customers tend to be several steps ahead of the organizations with which they are dealing. “Much of the engagement with customers happens before they ever talk with a salesperson,” Ward says. As a result, she contin-ues, “if you leave the customer data to the sales organization, it’s too late. You’re already through

needs to play a lot more of a role in how data is managed and shared.” This consists of being aware of how customers are impacted through

Along with better understanding the customer life cycle, business leaders “need to have a good sense of how predictive analytics ties into com-petitive advantage and strategy and pro$tability,” says Dhar. “That ensures a lot of commitment to invest.” He adds that the predictive analyt-ics $eld “is confusing to executives right now.” Predictive marketing proponents need to spell out the advantages: “if you did one, two and three, this is going to be impactful in terms of your top-line or bottom-line advantage,” Dhar relates. “The industry will serve itself better if it ties itself to business solutions more, as opposed to getting enamored by the technology.”

Communication and cross-enterprise collabora-tion is the key to making predictive marketing e!orts e!ective. Sallomi advises proponents to

highly impacted people representing di!erent disciplines.” High-level perspective is needed. The $rst task this group needs to undertake is to “spend a little time thinking about how your business might be disrupted if you do nothing,” he adds. “Then think about how you might apply some of these technologies and some of these opportunities to your own business.”

Success with predictive analytics in marketing requires an “MDM mindset,” says Smits. “Partic-ipants need to have an appreciation that there’s work needed to bridge these data sets together. It requires people knowledgeable about systems as well as data management and data steward-ship.” Having the right talent is also critical, he

you need talent that is very capable and data en-gineering speci$c, as well as having a really good sense of business problem solving.”

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