From informaton to audiences a-winterberrygroup whitepaper with IAB

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From Information to Audiences: The Emerging Marketing Data Use Cases A Winterberry Group White Paper January 2012

© 2012 Winterberry Group LLC.

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Acknowledgements This white paper would not be possible without the significant contributions of more than 175 advertising and marketing thought leaders—representing virtually all corners of the commercial data and technology ecosystem. In particular, Winterberry Group is grateful to our research partner, the Interactive Advertising Bureau, as well as the following sponsors for their generous support of this important research initiative: Presenting Sponsors: Supporting Sponsors: To all those whose insights, time and other contributions helped in the development of this white paper, we thank you.

Notice This report contains brief, selected information pertaining to the commercial marketing data industry and has been prepared by Winterberry Group LLC with the support of Interactive Advertising Bureau. It does not purport to be all-inclusive or to contain all of the information that a prospective investor or lender may require. Projections and opinions in this report have been prepared based on information provided by third parties. Neither Winterberry Group, the Interactive Advertising Bureau nor their respective sponsors make any representations or assurances that this information is complete or completely accurate, as it relies on self-reported data from industry leaders—including advertisers, marketing service providers, technology developers and agencies. Neither Winterberry Group, the Interactive Advertising Bureau nor any of their officers, employees, representatives or controlling persons make any representation as to the accuracy or completeness of this report or any of its contents, nor shall any of the foregoing have any liability resulting from the use of the information contained herein or otherwise supplied.


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Executive Summary No matter what analogy you prefer, one truth is undeniably clear: Technology has fundamentally advanced the creation of what many call “big data.” Consider:

From the dawn of time through 2003, according to Google’s executive chairman, Eric Schmidt, human civilization generated approximately 5 exabytes of aggregate information. In 2009, that much data—captured in the equivalent of 25 quadrillion tweets—was generated every two days

Globally, businesses created 1.8 zettabytes of data in 2011, according to IDC. That output—enough to fill 57.5 billion 32-gigabyte Apple iPads—is growing approximately 62 percent annually (on a compounded basis)

In July 2011, Facebook’s 750 million worldwide users uploaded approximately 100 terabytes of data every day to the social media platform. Extrapolated against a full year, that’s enough data to manage the U.S. Library of Congress’ entire print collection—3,600 times over.

The world’s Twitter feeds, iPads and libraries may not stand a chance against this onslaught of information. But to the world’s marketers, the proliferation of data has given rise to what may prove to be the most substantial commercial opportunity since the emergence of the World Wide Web: the ability to better understand consumers, seamlessly match “right-time” offers to their needs and optimize the management of profitable, long-term customer relationships. Not surprisingly, many are working feverishly to capitalize on the new potential of marketing data, especially with respect to the torrent of highly insightful (but highly unstructured) information being generated online. The ongoing convergence of new data sources, targeting technologies and advertising delivery platforms is likewise shifting their focus—from the management of raw information to the optimization of granular consumer audiences across discrete advertising channels, product categories and geographies. The demands of real-time, rules-driven, audience-centered marketing represent a full-on paradigm shift in how marketing is done. But with the opportunity inherent in this approach comes a daunting challenge: to identify and deploy an actionable range of “use cases”—practical marketing applications that, fueled by data, may drive transformative improvements in both marketing effectiveness and efficiency. Today, even while some enjoy modest success in redeploying their existing resources to the new cross-channel task, most other marketers—saddled with legacy technology platforms, depleted of expertise by years of underinvestment and structured only to support “traditional” approaches to data usage—are finding they’re woefully unprepared for this transformation. For them, a growing data divide is taking shape, distinguishing those use cases to which data may now be profitably deployed from those which—though promising in their strategic potential—still represent nothing more than ideals of how automated, multichannel marketing may someday take shape.

The ongoing convergence of new data sources, targeting technologies and advertising delivery platforms is shifting focus—from the management of raw information to the optimization of granular consumer audiences.


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This white paper—produced in conjunction with the Interactive Advertising Bureau—will explore four data-driven use cases (audience optimization, channel optimization, advertising yield management and targeted media buying) that collectively represent the foundation of how many are now seeking to leverage the potential of “big” marketing data. In addition to that analysis, it will demonstrate that capitalizing on this opportunity will require:

Rules-driven integration of disparate data sets: The collection, analysis and segmentation of digital data demands the aggregation and anonymization of virtually all data, challenging marketers’ fundamental ability to draw distinct insights from consumers’ cross-channel interactions

Improved operating infrastructures: Though substantial process and data structure challenges also exist, a substantial barrier now inhibiting wider marketing data optimization resides within the marketing organization—characterized by rigid “silos” and the paucity of data-savvy marketing operations, IT and sales talent

A strong network of data-centric technology and service partners: The fastest and most efficient data aggregation, analysis and throughput solutions require a strong ecosystem of partners who understand and can integrate seamlessly with core data assets and supporting technologies

Marketing data governance: While organizations have long employed policy experts to advise on the regulatory ramifications of data utilization, many are coming to see marketing data governance—defining the “rules of the road” for assigning distinct data sources to different promotional tasks—as equally important.


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Methodology This white paper explores a series of “use cases” that define how marketers are commonly deploying multichannel data to improve their advertising and marketing effectiveness and efficiency. It further highlights a series of trends that are defining how data is now being used to drive broader advertising and marketing performance for companies based in the United States. Developed in research partnership with the Interactive Advertising Bureau—and with the sponsorship of Acxiom Corporation, IBM, BlueKai, eXelate, Janrain, ShareThis and V12 Group—the paper’s findings are based on the results of an intensive research effort that included in-person, phone and online surveys of more than 175 marketers, agency executives, data compilers, technology developers and other industry thought leaders around the globe. Where possible, contributors have been cited by name so as to provide transparency into the research process and supporting panel. In some cases, contributors have asked that we omit their name and company information so as to allow them the freedom to speak with maximum candor.


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The Emerging Marketing Data Use Cases The span of today’s data use cases is broad, reflecting the relative immaturity of the “digital data” enterprise and the array of pilot solutions that marketers and publishers are deploying to make use of the growing information resources at their disposal. For some, a data use case may be as simple as demographic-driven customer acquisition (as enabled by a rented mailing list); for others, the span of what’s actionable may include a host of sophisticated display advertising targeting solutions. Interest in these applications is being piqued by the realization that information may be used to drive transformative value that spans “demand” and “supply” sides of the advertising and marketing value chain. Data availability is now allowing advertisers, agencies and publishers to optimize ad delivery, evaluate campaign results, improve site selection and retarget ads to other sites. It’s also improving the value of media to brands by delivering their advertising to better-qualified prospects—making the ad more efficient, more valuable and providing a more compelling user experience. Grounded in years of direct response, data use by those marketers that predominantly leverage offline channels is proving to be just as sophisticated as those applications that dominate in the online sphere. Ironically, best practices developed in this “traditional” DR marketing world are often used to establish parameters for the deployment of digital data, even in those cases where data are being used to enable a shift in strategic emphasis from direct response to brand engagement. “The industry has spent a lot of time and money at the bottom of the funnel,” said Jeff Liebl, chief marketing officer at TruSignal. “Advertising is supposed to be about generating intent, but the bottom of the funnel is mostly about looking for people who have already shown interest. I think we’ll see ad dollars shift and a greater focus placed on earlier, upper-funnel brand awareness activity, targeting people that haven’t necessarily demonstrated online behavior yet that shouts ‘I’m in market.’” What follows is a discussion of four selected marketing data use cases—audience optimization, channel optimization, targeted media buying and advertising yield management—along with an assessment of fundamental benefits, current maturity levels, core beneficiaries and long-term potential.

Use Case Fundamental Advertising Benefit

Maturity Level

Core Beneficiaries Long-Term Potential

Audience Optimization Effectiveness Low E-commerce Marketers, Digital Advertisers, Lead Generation Portals, Publishers (for traffic acquisition)

High

Channel Optimization Effectiveness/Efficiency Low E-commerce Marketers, Publishers, Lead Generation Portals

High

Advertising Yield Optimization

Efficiency Low Publishers High

Targeted Media Buying Efficiency/Effectiveness Intermediate Marketers (via Demand-Side Platforms), Digital Agencies / Trading Desks

High


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Audience Optimization Identifying customers and likely prospects through the integration of rich (though disparate) first- and third-party data sources; managing cross-channel marketing execution with the goal of engaging those audiences strategically—and in accordance with consumers’ preferred advertising media.

Fundamental Advertising Benefit

Maturity Level

Core Beneficiaries

Long-Term Potential

Effectiveness: Identifying the “right” target consumers is the foundation of targeted advertising, and may be used to improve performance across branding, engagement and direct response functions

Low: Though the technology now exists to capture and deploy large quantities of information (in the necessary “real-time” windows), consensus has yet to coalesce around the optimal approach to structured integration of this data—especially when its sources span traditional (“PII”) and digital (“non-PII”) channels

E-commerce Marketers, Digital Advertisers, Lead Generation Portals, Publishers (for traffic acquisition)

High: More so than any other use case, the ability to define high-potential audiences from disparate indicators—and then communicate with them across a range of media—represents a fundamentally new approach to managing addressable customer markets

The plethora of first-party data now being amassed and analyzed by both publishers and advertisers is being used to build rich audience profiles that, marketers say, can enhance advertising effectiveness by enabling improved targeting and message relevancy. Today’s dominant approach calls for the development of unique customer/prospect profiles, which are then segmented and modeled as the basis for identifying what are commonly called “lookalike audiences” for follow-up marketing across channels. For publishers, third-party data overlays and data exchanges—providing access to a wealth of additional information generated through online sources—are providing the opportunity to enhance first-party data with demographic and interest-based indicators, as well as first-party data from other online publishers. “Companies usually own very rich first-party data,” said Travis May, head of strategy and operations at Rapleaf. “Third-party data is especially helpful when there are new customers or early-lifecycle customers and the data need to be enhanced to be segmented more quickly.” In one example: Catalina Marketing, which claims to collect and analyze in-store purchase data covering 80 percent of the U.S. population, is now combining offline and online sales data to help its consumer goods clients make more intelligent, audience-centric predictions for in-store promotions. According to Eric Williams, Catalina’s chief information officer, this approach is generating 8-10 percent coupon redemption rates (versus 0.5 percent rates for comparable mass-market couponing programs). “By linking this data, we are creating a total purchase history that will allow us to categorize and stratify consumers into purchase category buckets and infer what will be of interest to them before they actually buy,” said Williams.


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Channel Optimization Enabling “right message, at the right time, via the right media” targeting; expanding the role of consumers in choosing optimal/preferred communications media.


Maturity Level


Effectiveness/Efficiency: Allows for the strategic utilization of media in alignment with the inherent strength of those channels, as well as consumer preferences; engages audiences at a richer level and minimizes investment in wasted/suboptimal channel efforts

Low: Traditional advertising and marketing efforts have been structured around the deployment of individual channels through distinct campaigns, and migration to true “media-agnostic” models that seek to match audiences to optimal/preferred output levers requires process, technology and data source alignment that most marketers have not yet undertaken

E-commerce Marketers, Publishers, Lead Generation Portals

High: Migration to media-agnostic communication strategies stand to enhance consumer engagement, promote a robust dialogue and reinforce both single-purchase behavior as well as lifetime customer value

The rapid introduction of new addressable marketing channels over the past two decades—starting with the emergence of foundational digital media such as email, search and display advertising, and hallmarked today by the maturation of tablets, smartphones, addressable television and other media—has reinforced consumers’ technological sophistication, and provided them with a new span of control over marketing content. At the same time, the diversity of promotional options has introduced a new challenge to both publishers and advertisers: maintain a marketing dialogue that matches strategic intent to optimal delivery channel, but honors consumers’ choice with respect to messaging cadence and medium. Brands that are able to integrate multichannel data across channels—effectively becoming “agnostic” to the deployment of any single medium—hold the prospect of creating holistic, near-360-degree views of customer preferences and intent regardless of channel. The result is more relevant advertising—delivered at the optimal time, via the consumer’s preferred channels. Executives across the marketing ecosystem agree that data owners are sitting on mountains of valuable information that can be used to drive these kinds of media-agnostic efforts, but say much of the potential of that data is being undermined by efforts to deploy messages through “sexy” channels, such as social media platforms. “Marketers are anxious to jump ahead into social and other burgeoning areas of digital marketing, yet they shouldn’t overlook that they have a tremendous asset right on their own website that can be used to make these efforts more effective,” said Marc Kiven, founder of BrightTag. “Imagine being able to walk behind every customer in your store and see where they go, what they look at and what they touch. This data already exist… *marketers+ just need permission to use it and the technology to unlock it.”


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Advertising Yield Optimization Maximizing the value of available advertising inventory by identifying and “selling” high-value audiences across individual publisher properties and delivery media.


Maturity Level

Core Beneficiaries

Long-Term Potential

Efficiency: Allows advertisers to avoid investing in media on the basis of simple demographic characteristics—where impressions generally reach a large number of suboptimal target consumers as a means of capturing good prospects from a larger universe. (To publishers, the benefit is all about effectiveness—as optimizing yield generates higher advertising revenues)

Low: Though technological advances are rapidly allowing audiences to be “sold” across distinct online media platforms, the potential of the approach demands true cross-channel yield optimization; most publishers are very early in their efforts to integrate traditional ad inventory (where it exists) into a holistic optimization effort

Publishers High: For a publisher community struggling to effectively monetize content (both “premium” and among “long tail” sites that generate less Web traffic), the identification and optimization of audience-centric inventory has the potential to deliver substantial revenue opportunities, possibly even supplanting existing approached to advertising packaging and sales

On the supply side, publishers are moving fast to deploy third-party data overlays (sourced largely through exchanges) and the services of data management platforms in an effort to create richer audience profiles designed to maximize their yield (the rates they may charge for advertising inventory) and improve the value of that ad inventory for which traffic doesn’t warrant a “premium” sales approach or pricing. With multiple data streams, typically, feeding internal systems in rapid succession, publishers said data control, accuracy and processing speed are critical prerequisites for identifying high-yield audiences across disparate media platforms. “We have two big relationships with publishers and both recognize the need to control their data ecosystem in a very robust way,” said David Soloff, chief executive officer of Metamarkets. “They are carefully overseeing first- and third-party data and usage logs and trying to uncover tremendous pockets of inventory that may be mispriced or ignored. It’s great for building ROI.” One publisher said that the benefits of yield optimization ultimately won’t stop with more informed pricing of inventory. “Creative versioning,” he said, will allow advertisers to provide variable, tailored content to different audiences across all of the publisher’s properties—enhancing the effectiveness of each ad unit (while driving the publishers’ ability to extract value from that inventory). “We can execute this idea now on any given property, but we’re working on a way to be able to roll this out across all of our sites,” the publisher said. One major challenge, he added, has already surfaced as a barrier to capitalizing on this potential: the ability and willingness of advertising sales teams to understand, embrace and communicate the role of these complex ad units.


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Targeted Media Buying Enabling the economical, value-oriented purchase of advertising media; delivering targeted messages to audiences across a diverse, actionable range of channels.


Maturity Level


Efficiency/ Effectiveness: The use of automated, real-time media buying tools allows for access to audiences at “true” market pricing—eliminating the need to invest in “eyeballs” that are not likely to value a message or offer; likewise provides a deeper platform for customizing marketing offers or content in a move to expand relevance of those underlying messages

Intermediate: “Real-time bidding” (RTB) tools have matured substantially over the past few years, and are in common use by enterprise marketers across verticals

Marketers (via Demand-Side Platforms), Digital Agencies / Trading Desks

High: Meaningful media-buying efficiencies are already accruing to sophisticated users; deeper value will come through the coordinated use of these applications and the targeted messaging/offer tools that allow for optimization of message content, timing and cross-channel integration

Demand from advertisers for the efficiencies inherent in real-time bidding and the improved effectiveness that comes through improving brand messaging relevance is driving more sophisticated data use across both ad targeting and media buying practices. Demand-side platforms (DSPs) and digital agencies (many empowered, over the last few years, by the addition of automated trading desk capabilities) are leading the market in this respect by enabling marketers to identify, “purchase” and target high-value customers across channels, in rapid timeframes. In particular, search and display retargeting programs—targeting site visitors who have abandoned a shopping cart or left a site without otherwise converting—can provide specific offers based on the visitors’ on-site behavior. “By way of example, one of our retail clients… wanted to establish dynamic targeting rules as its customers came onto its site,” said BrightTag’s Kiven. “By splitting its audience into control and test groups, the retailer was able to understand the differences in behavior of users who saw a retargeted message versus those who did not.” The results of this more flexible, rules-driven approach to data collection and integration lets the company shift attention from top-of-funnel branding efforts and work more closely with its DSP partner to better manage retargeting bids. Multichannel data integration is a critical component of improved media-buying capabilities. According to one agency executive, integrating on- and offline data for one of the agency’s advertising clients resulted in a nearly 30 percent increase in online display performance.


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The Opportunity Ahead: Trends in Marketing Data Utilization

Rules-driven integration of disparate data sets: While traditional data management practices were built largely around centerpiece “personally identifiable information” (PII) elements—usually consumer names and postal addresses—the collection, analysis and segmentation of online data demands the aggregation and anonymization of virtually all data sets, challenging marketers’ fundamental ability to draw distinct insights from consumers’ cross-channel interactions.

Marketers and publishers continue to be wary of using personally-identifiable (PII) data in the digital realm due to concerns about consumer privacy and data accuracy. “You can’t make a flawless data background when moving data between multiple devices because there are too many unknowns when it comes to privacy,” says the CEO of one DMP company. “With addressable TV, for example, the cable distributors have PII that they could easily match up with computer background and deliver a custom broadcast based on a customer’s search history, but no one is willing to bridge that gap yet *in fear of running afoul of privacy best practices+.” As a result, data collection, analysis and segmentation processes are being driven (or constrained, depending on your perspective) largely by the need to aggregate and then anonymize—remove any “PII” elements—wide swaths of both first- and third-party data. In response to this inherent complexity, many are taking a cue from the data co-op models that emerged in the 1990s (largely for use by catalog marketers) and turning to data exchanges, where participating digital publisher data is blended, segmented by interests and made available to all contributors to augment their own audience insights. First-party, browser-based data—collected primarily through cookies—is being widely supplemented with this third-party data to scale data sets and identify large, “lookalike” audiences of high-value customers. Available sources span a wide range—from social media registration data (including, at times, insight into income, age and gender), to transaction-based data that includes activity on shopping behaviors, to general-interest data indicating news and other areas of consumer interest. A debate is raging, though, about the value of third-party data. Some executives warn that it is becoming increasingly generic and, therefore, less valuable. “Third-party data has become over-commoditized,” said an executive at one media application developer. “We are actually seeing a shift to first-party data.” Not so, said an executive of one data technology company. “Accurate third-party data remains valuable because it provides context, scale and cross-channel consistency. It gives advertisers useful context for messaging to know the demo- and psychographic elements associated with a person interested in ‘Product X.’ It provides a level of insight-driven scale that, even in online environments, still isn’t available to advertisers using first-party data alone. And it is the key mechanism for reaching target audiences across channels with consistent messages.”

Data collection, analysis, modeling and segmentation processes are being driven (or constrained, depending on your perspective) largely by the need to aggregate and then anonymize wide swaths of both first- and third-party data.


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Actions Speak Louder Than Names Despite the challenges inherent in the PII/non-PII divide, some data executives downplay the importance of knowing a prospect’s name and address, arguing that pixel-driven data—insight into what an individual browser does on a website or a platform like Facebook—often brings the sought-after targeting capabilities, even without a consumer name. “A cookie is just as good as an individual ID,” argued an executive at one large media-buying platform. “Knowing what people do through trackable cookies can be very sophisticated and pinpoint those who engage or convert at higher levels by following their behaviors—whether through display, social, a website or viral video.” These strategies are being enabled by large, sophisticated machine networks and algorithms that identify useful signals and patterns of behavior that can’t be found in PII data alone. Ultimately, many said, the consumer’s name and address isn’t as important in raw behavioral data to determine propensity to respond. Said TruSignal’s Liebl: “We take first- and third-party data, put it in our modeling engine and let the algorithm decide the attributes and segmentations that identify the person as a high-value customer.”


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Improved Operating Infrastructures: The primary barrier to widespread marketing data optimization resides within the marketing organization, rather than the data itself. Specifically, legacy operating infrastructures—characterized by rigid organizational “silos” and the paucity of data-savvy marketing operations, IT and sales talent—are substantially hindering the maximization of data, processes and systems.

“A big hurdle is how companies are organized and structured. Traditional marketing data is managed in marketing operations, behavioral data is probably with a VP of digital marketing and then digital data will be fragmented across those groups or have its own VP of social media.” – John Zell, VP, Global CRM Solutions, Razorfish Many enterprises suffer from an embedded culture of “traditional” media management; even though they may be deploying new digital channels (as led by distinct planning, creative and delivery teams), they are often managing those distinct efforts in organizational silos, separate and distinct from the company’s other marketing channels and data sources. Panelists reported that it’s uncommon for online and offline channel managers to share data, and typical for different managers to oversee digital execution channels such as email, social and search. Moreover, many organizations still rely on their corporate IT function—which commonly has neither the budget nor the decision-making authority to steer marketing programs—to manage granular marketing data applications. In addition, installed legacy systems and architectures (frequently built by different contractors with the intent of making integration with other platforms difficult) can’t accommodate the number of channels and volume of data now available, much less the need to integrate complex, real-time data feeds. The answer that many forward-thinking companies have developed is to invest in the development of a data accessibility culture–led by a chief data officer. “Ultimately there’s going to be a chief data officer (CDO) that exposes the data to you and wrestles away some of the technology needs from the internal groups,” said Christian Ward, senior vice president at Infogroup. Advertising Sales Reps Lack Critical Technology Expertise “It’s a rare breed of person who can understand what’s going on technology-wise and tie it to the marketing world.” – Ari Buchalter, COO, MediaMath The second organizational challenge that has limited the monetization opportunities linked to marketing data is a lack of sales expertise when it comes to data-driven advertising. Media sales reps, for example, are historically trained to sell inventory by way of traditionally volume- and demographic-driven variables—estimated magazine circulation, say, or television ratings. But to successfully sell “audiences” (as defined by disparate data sources) across channels, reps today must be technology savvy as well as media savvy. “Reps have to get in the dirt more to understand this new ad

“It’s a rare breed of person who can understand what’s going on technology-wise and tie it to the marketing world.” Ari Buchalter, COO MediaMath


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technology, and most don’t have a technical background,” explained Dwight Green, Nielsen’s vice president of digital product leadership. According to some industry executives, the most effective data-driven sales reps are coming from the analytics sector because they know how to sell software as a service. Additionally, staff with digital agency or DSP experience (reflecting an understanding of trading desks and technology-driven data-use models) will be valuable, as they understand the specialized buying of data-driven audiences. Publishers built on traditional advertising sales are using education and training to better prepare their sales forces to monetize data value through media. “The first thing we’re doing is building a ski slope of analytics tools that include beginner, intermediate and expert proficiency levels,” says one broadcast and online publisher. “We’ll build out the simple tools first and invest in training and education to make it successful.”


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A strong network of data-centric technology and service partners: The fastest and most efficient “big data” aggregation, analysis and throughput solutions require a strong ecosystem of service- and technology-enabled partners. The burgeoning data supply chain is proving to be extremely agile in streamlining and processing data for marketing performance—supported by a new class of open-source tools (such as Cassandra and Hadoop) as well as maturing data optimization providers that offer new solutions for managing and redeploying large quantities of disparate data sources.

“When you’re in a fast-moving environment and you want to create smaller segments, that’s a machine-solved problem rather than a human-solved problem, and that’s the problem we’re trying to solve.” – David Soloff, CEO, Metamarkets “As these machine-learning processes become bigger and more important they will need to be outsourced. More sophisticated data processing requires more exotic software that is hard to master independently.” – Stewart Allen, CTO, Clearspring There’s widespread industry agreement that achieving optimal data collection, analysis and throughput performance requires a strong ecosystem of technology-enabled partners, particularly as digital data—which is growing increasingly temporal (or time-sensitive)—requires faster processing and integration. The data ecosystem is proving extremely agile in the streamlining and processing of data for marketing outputs, particularly on the demand side. Most DSPs, for example, have built trading desks that drive speed and efficiency in ad bidding and buying. A corps of analytics-focused marketing agencies—grounded in data segmentation, but often tasked with the execution of those strategies, as well—has emerged to drive sophisticated audience modeling. And not to be left behind, email service providers (ESPs) are adding more analytical services, including A/B testing, to improve their clients’ targeting efficiency. Other service-driven vendors, including agencies and data management platforms (DMPs), have focused on analytics and segmentation to make data more usable for client marketing and advertising. As it matures, for example, the DMP market is progressively splintering into a number of primary specialty disciplines—focused respectively on the aggregation of third-party data and intersection of ad network technology, as well as “pure-play” models focused around the integration of customer data, with a variety of views into the underlying data. What providers in all these groups share is a focus on integrating multichannel data streams to plug into CRM and other systems to provide data owners with a “360-degree” view of their customers (and customer interactions). This “DMP approach” is grounded deeply in a service-driven supply model, distinguished by the overlay of data access, analytics and media optimization capabilities (but also, some criticize, by a lack of core data management capacity).

The “DMP approach” is grounded deeply in a service-driven supply model, distinguished by the overlay of data access, analytics and media optimization capabilities (but also, some criticize, by a lack of core data management capacity).


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Technology, meanwhile, is rapidly growing to meet the critical execution requirements—including rapid throughput, low latency and high-capacity processing power—that real-time marketing execution demands. New open-source tools such as Cassandra and Hadoop, for example, provide “virtual” platforms for managing unwieldy data sets.

Marketing data governance: Data governance has emerged as a critical priority for virtually every player in the data ecosystem. But whereas organizations have long employed policy experts to advise on the regulatory ramifications of data utilization, many are coming to see marketing data governance—defining the “rules of the road” for assigning distinct data sources to different promotional tasks—as an equally critical go-forward priority.

Realizing potential value from a vast new array of data sources presents a series of challenges wholly separate from those associated with process management, technology or marketing strategy. By comparison, the basic governance questions associated with data usage— dictating who may access a given data set, and what rules or rights to data usage, data privacy and data security may be associated with its deployment—are just as thorny, and present an even costlier potential array of risks.


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When it comes to umbrella data governance strategy, panelists were united on one best practice: Unless associated with one’s own customers or other first-party data sources, PII data is essentially off-limits for online targeting purposes. The potential cost of doing otherwise—of running afoul of privacy regulation, or violating the consumer’s inherent right to choice in managing his or her marketing communications—are simply too great for most marketers to bear. “We check with our privacy counsel before we do anything,” said Nielsen’s Green. “The penalties are tough and you must have the right internal teams and know the laws and acceptable standards that are in place.” For many data owners, the solution is to house data internally—“behind the corporate firewall”—even when third-party solutions are used for the purpose of managing data processing, analytics or optimization. For its part, the marketing data industry is moving to develop, publish and promote a series of universal data-use guidelines in an effort to provide self regulatory solutions that may assuage consumer or regulatory concerns. The new principles—like the Digital Advertising Alliance’s recent Self-Regulatory Principles for Multi-Site Data—build upon FTC recommendations regarding the collection of Web viewing data and establish a clear framework governing the collection of online data that also provides consumer choice for the collection of such data. Data transparency is a critical component of the solution. Industry executives agree that consumers need to understand how their data is being used before they will begin to trust brand use of that data. The preferred response for most marketers is to allow consumers to opt out of some data use practices. Individual vertical industries, too, are moving to balance their own unique marketing concerns with the lucrative potential of new data sets and potential consumer concerns about the use of that information. In the auto industry, for example—where “data,” for example, could conceivably include detailed information on everyday consumer whereabouts—the importance of maintaining best practices in all regards is incredibly important. “The connected car will have a huge impact on our industry,” said Paula Skier, senior product marketing manager for digital products at Polk. “Through the combination of in-vehicle technology and smartphones, cars can be the conduit for creating unbelievable amounts of data—driver and passenger attributes, driving patterns, location, speed, media consumption, communication with other consumers

Requirements of the DAA Self-Regulatory Principles for Multi-Site Data:

Organizations that collect multi-site data for purposes other than online behavioral advertising must provide transparency and control regarding Internet surfing across unrelated websites

The collection, use or transfer of Internet surfing data across websites for determination of a consumer’s eligibility for employment, credit standing, healthcare treatment and insurance are prohibited

Organizations must comply with the Children’s Online Privacy Protection Act (COPPA) regarding the collection and use of children’s data

The Multi-Site Data Principles are subject to enforcement through strong accountability mechanisms.


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and even social networking. But with access to this information, [the industry] has to demonstrate a benefit to consumers so they are comfortable with and want to participate in programs leveraging that data.” Global Marketers Face Restrictive Data Use Environment Abroad With the expansion of the global marketplace, U.S.-based companies with international operations face greater privacy and data governance challenges. For example, each European Union country has its own set of data regulations that, individually and collectively, are more restrictive than their counterparts in the U.S. For example: “An IP address is PII in every country except the U.S,” argued Catalina’s Williams. The result is that execution of data-driven marketing abroad is even more difficult. Vigilant awareness and compliance with data regulations within each country will be critical as the industry continues to evolve. Such interactions could be self regulated as U.S.-based data or governed under safe-harbor rules.


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Conclusion The use of marketing data is evolving as rapidly as the technology driving it. Today’s immature use cases will become tomorrow’s standard marketing practices. Strategy will follow technology, as new suppliers push the marketing envelope to identify and integrate offline and online data streams into broader data assets that can be analyzed, segmented and modeled—creating audience profiles that cut across channels. Core direct marketing skills and practices in data analysis, segmentation and modeling will continue to provide a solid foundation for emerging digital data use cases—but must be augmented to account for new techniques and skills required to collect, analyze, integrate and derive value in the face of these new applications. Meanwhile, marketers and publishers will continue to grapple with a number of challenges posed by big data: storage capacity and accessibility; machine-generated insights (i.e. modeling and algorithms) versus human intuition and skill; consumer choice; and the role of PII in digital marketing. But there will be more growth opportunities, as well, as the relationship between top-of-funnel branding and bottom-of-funnel conversion programs become better defined in the online world. There’s widespread agreement among marketing industry executives that consolidation is coming—and that it will encourage more brand marketers and publishers to mature and grow their deployment of data use cases (and maybe acronyms, too). “Within the digital ecosystem we’ll start to see consolidation and horizontal integration,” said Caribou Honig, a partner with QED Investors. “Point solutions focused on a single channel will fall to the wayside unless they’re highly superior, and even then they’ll be integrated with a platform somewhere. Ultimately, your online display DSP, online video DSP, social DSP and PPC platforms will all reside on a single platform.”


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Appendix A: Our Research Panelists Tell Us What “Data Developments” They Expect to See as 2012 Unfolds “Attribution and cross-channel performance aggregation will continue to expand and become utilized to greater marketing benefit.” “More advanced machine-learning techniques that incorporate meaningful data points to predict outcomes. The algorithms are out there, we just need to plug in all the disparate data sources—context, first- and third- party data, site, geography—to a stable online ID pool in real time to deliver the right creative to the right person and the right place for a brand to pinpoint the best prospect.” “We’ll continue advancing in connecting multichannel marketing and personalization via a host of services and technologies. In a short period of time, consumers will more strongly voice preferences on how they choose to accept marketing messages. Marketers quick to adapt to these preferences will pull away from the pack.” “The metrics for display advertising need to change. Basic clickstream metrics provide little to no insight into success/failure. Additionally, a large percentage of online targeting through multiple platforms will be driven by data on the front end.” “The convergence, with sufficient anonymization, of large offline data segments into online platforms. It is an untapped resource and data companies and CRM marketers are becoming savvier about the opportunities.”


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“Offline data is valuable and will bring the tried-and-true maturity of offline market research and advertising lessons learned to the digital space. It will bring consistency and scale back into the multichannel advertising equation.” “The willingness to rework frameworks—especially in the area of offer management—to reach customers and potential customers effectively and efficiently. Most advertising agencies are unwilling to recommend this to their clients because it will ultimately result in loss of revenue.” “The combination of qualitative and quantitative measurement. Large companies aren’t yet driven to ask how positive or negative sentiment reflects upon other numbers. Where are the dependencies? How can this be measured and acted on?” “The sheer volume of data available will require marketers and their respective channels and vendors to be able to digest and deal with “big data.” Those who have access to larger data pools will be exponentially better equipped and can significantly build out market share.” “With more robust offline data able to be connected at a sub-zip code level as a geo-targeting technique, the use of single-threaded cookie attributes as the definitive targeting methodology will fade. Inferred interest as a sole metric will fade and so could the privacy issues associated with tracking users.”


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Appendix B: A Marketing Data Lexicon Evolving technology, data and marketing process are ushering in an entirely new language to define how advertisers, publishers and intermediaries do their work. The below lexicon—developed predominantly by the IAB Networks and Exchanges Committee—represent a small selection of the terms appear that appear in this paper and may be new to various constituencies of the advertising ecosystem:

Term Definition

Ad Network Provide an outsourced sales capability for publishers and a means to aggregate inventory and audiences from numerous sources in a single buying opportunity for media buyers. Ad networks may provide specific technologies to enhance value to both publishers and advertisers, including unique targeting capabilities, creative generation and optimization. Ad networks’ business models and practices may include features that are similar to those offered by ad exchanges

Cookie A small text file sent by a website’s server to be stored on the user’s Web-enabled device that is returned unchanged by the user’s device to the server on subsequent interactions. The cookie enables the website domain to associate data with that device and distinguish requests from different devices. Cookies often store behavioral information

Data Management Platform (DMP)*

A technology-enabled infrastructure for managing the aggregation, integration, analysis and redeployment of multiple first- and third-party data sources, particularly online

Demand Side Platform (DSP)

Provide centralized (aggregated) media buying from multiple sources including ad exchanges, ad networks and sell-side platforms, often leveraging real-time bidding capabilities of said sources. While there is some similarity between a DSP and an ad network, DSPs are differentiated in that they do not provide campaign management services, publisher services nor direct publisher relationships

First-Party Data* That which is sourced by, owned and managed by an entity (or its direct affiliates on its behalf) independently

Personally Identifiable Information (PII)

User data that could be used to uniquely identify the consumer. Examples include name, social security number, postal address and email address

Pixel (or Beacon) An HTML object or code that transmits information to a third-party server, where the user is the first party and the site they are interacting with is the second party. Pixels are used to track online user activity, such as viewing a particular Web page or completing a conversion process

Segment A set of users who share one or more similar attributes

Third-Party Data Data that did not originate from either the publisher or advertiser. Typically this is used to enhance ad targeting. For example, demographic data from a third party might be used to help determine which auto ad (make/model) to display on an auto site

* Defined by Winterberry Group


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A global leader in interactive marketing services, Acxiom Corporation connects clients with their customers through deep consumer insight that enables profitable business decisions. We incorporate decades of experience in consumer data and analytics, information technology, data integration and consulting solutions for effective marketing across digital, Internet, email, mobile and direct mail channels. Headquartered in Little Rock, Ark., Acxiom serves clients around the world from locations in the United States, Europe, Latin America and the Asia-Pacific.

For more information, please visit www.acxiom.com.

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The Interactive Advertising Bureau (IAB) is comprised of more than 500 leading media and technology companies that are responsible for selling 86 percent of online advertising in the United States. On behalf of its members, the IAB is dedicated to the growth of the interactive advertising marketplace, of interactive’s share of total marketing spend, and of its members’ share of total marketing spend. The IAB educates marketers, agencies, media companies and the wider business community about the value of interactive advertising. Working with its member companies, the IAB evaluates and recommends standards and practices and fields critical research on interactive advertising. Founded in 1996, the IAB is headquartered in New York City with a Public Policy office in Washington, D.C.

For more information, please visit www.iab.net.

Winterberry Group is a unique strategic consulting firm that helps advertising, marketing, media and information companies build value. Our services include:

Corporate Strategy: The Opportunity Mapping strategic development process prioritizes customer, channel and capabilities growth options available to advertising and marketing industry firms, informed by a synthesis of market insights and intensive internal analysis. Market Intelligence: Comprehensive industry trend, vertical market and value chain research provides in-depth analysis of customers, market developments and potential opportunities as a precursor to any growth or transaction strategy. Marketing System Architecture: Process mapping, marketplace benchmarking and holistic system engineering efforts are grounded in deep supply chain insights and “real-world” understandings—with a focus on helping marketers and publishers better leverage their core assets. Mergers & Acquisitions Due Diligence Support Services: Company assessments and industry landscape reports provide insight into trends, forecasts and comparative transaction data needed for reliable financial model inputs, supporting the needs of strategic and financial acquirers to make informed investment decisions and lay the foundation for value-focused ownership. For more information, please visit www.winterberrygroup.com.

http://www.iab.net/

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