p2pi.org

As seen in

he ability to compare mar-keting activity across plat-forms has been a highly

desired but unattainable goal for a generation of marketers. Today, however, the Holy Grail the indus-try seeks is on the horizon – if not within immediate reach.

So what specifically is this Holy Grail? According to the Coalition for Innovative Media Measurement (CIMM), “Single source data for passive media measurement is the Holy Grail for cross-platform adver-tising effectiveness.” This is a fairly technical statement that deserves to be explained in detail.

First of all, the term single source refers to tracking a single shopper, recording all of her advertising ex-posures and all of her purchases, for

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Best Practices in

ShoPPer Marketing MeaSureMentBest Practices in

ShoPPer Marketing MeaSureMent

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n Comparing marketing efforts across platforms has been the Holy Grail for a generation of marketers. “Single source data” has finally brought this goal into view.

n The term “single source” refers to tracking a specific shop-per by recording all of her advertising exposures and all of her purchases over a given period of time.

n The ideal single source data stream would provide a true 360-degree picture of a shopper’s day from a media-expo-sure standpoint and include all purchase behavior. So far, the best models track exposure from three channels: online, television and in-store; data streams from mobile devices may be added in the foreseeable future.

n Single source data offers five major benefits to shopper marketers:1. Enhanced ability to rationalize and optimize

investments 2. Accurate picture of in-market responsiveness by

shopper segment3. Assessment of the opportunity costs of media plans4. Ability to better optimize media plans5. Better understanding of the role each medium plays

in driving salesn When consumers use the Internet, they leave behind trails

of data that serve as a record of their behavior. There are lots of trails generated by both man and machine that, taken cumulatively, constitute “big data.”

n Since the turn of the millennium, shopper marketers have called for function-centric silos to be eliminated. Big data may ultimately be the catalyst.

Executive Summary

a defined period of time. The ideal single source data stream would provide a true 360-degree picture of a shopper’s day from a media-ex-posure standpoint and also would include all of her purchase behavior.

So far, the best available models can track a shopper’s exposure in three channels: online, television and in-store. These areas constitute the term cross-platform in CIMM’s statement. While this new ap-proach is a great leap forward, it still excludes numerous other channels of media exposure such as radio, outdoor and mobile.

Gathering the data works rough-ly this way: Purchase behavior is tracked using scanner data derived from credit /debit card transac-tions. Each shopper is assigned an

By Liz Crawford, Senior Industry Analyst

The following is the final installment in a six-part series examining best practices for the measurement of shopper marketing. This article explores directions for the future. To read the first five articles in the series, visit www.p2pi.org.

Directionsfor the

Future©iStockphoto.com/ryasick

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anonymous tag, which is then matched to on-line activity connecting that shopper to a spe-cific online surfer. So the single source becomes a specific shopper. In a final step, certain house-holds that have their TV viewing monitored are matched to the anonymous tags, completing a single source, in-store/out-of-store tracking system. (See Venn diagram, above.)

An enormous advantage to this approach is that it can report recorded behavior, which is captured digitally as the passive observations noted in CIMM’s statement. The individual isn’t a “respondent,” so there are no ques-tionnaires to fill out or incentives to dangle. Instead, this technique is one of the first to take advantage of “big data.”

Big data is the huge set of “exhaust data” that is emerging. When consumers use the Internet, they leave behind trails of data that serve as a record of their behavior. There are a lot of trails generated by both man and ma-chine. Like consumers, machines also gener-ate exhaust data, which includes monitoring records of every stripe, as well as machine-to-machine communi-cation trails like electronic data transfer messages. Because these data include passively observed records of consumer behavior, they are more accurate than ask-ing respondents to remember and report their activity.

While this new methodology is exciting, there are issues with the approach. First, as previously noted, many media platforms are missing. Notably absent at this point are magazines, newspa-

pers, outdoor media and radio. Further, the industry is still working to link TV and re-tail consumption to digital platforms such as mobile applications. At this stage, the most interesting addition to the current database would be smartphone user behavior (which may not be too far away).

Until all these links are established, media consumption is ascertained using old-fashioned surveys to fill in the gaps on a per-person basis.

There is another big issue with this nascent approach. While these three databases are robust enough to find and track individuals, sample sizes sometimes inadequately represent populations. Weighting and other statistical techniques can be used to compensate for sam-ple sizes until more observations are collected, but this is a bit of a drawback in the short term.

Beyond these two issues, there is the social (some might even say “moral”) issue of pri-vacy. Research companies engaged in tagging and tracking are quick to assert that they are scrupulous in their use of anonymous tags.

Additionally, more than one company is always involved in matching the data, so a specific individual’s identity can’t be revealed, even from a logistical standpoint. The tags and data hand-offs are important, therefore, because they are crucial to preserving anonymity of in-dividuals; otherwise, this kind of analysis could be perceived as surveillance.

Marketing Mix AnalysisMarketing mix models have been used since the 1980s to understand the impact of various marketing tactics on sales. Until now, mar-keters haven’t had access to this enormous exhaust data, which lets them observe media consumption in conjunction with purchase behavior on a mass scale. So older mix mod-els used statistical techniques to examine the correlation between sales and various inde-pendent marketing variables. This was a foren-sic exercise in which analysts would infer the causes of sales increases based on advertising impressions, spending and other factors. The “shoppers” were a gross, unified force behind the statistical relationships.

The picture changes when marketing mix models get the high-octane input of single source data. Models are still run, but they are used to analyze the relationship between real “opportunity to see” ad exposure and an actual purchase. How likely is a shopper to buy Brand X after exposure to Online Ad Y? After exposure to Online Ad Y and TV Ad Z? These kinds of ques-tions get very accurate, reality-based answers due to the extensive records available today.

How We’ll Roll: New OutputsSingle source data will revolutionize how companies go to market and gauge success by delivering five prominent new benefits to shopper marketers.1. An Enhanced Ability to Rationalize and Op-timize Investments: Without even tapping into modeling or analytics, it is possible to simply

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“ The data itself will break the silos. You can’t have data that speaks to everything and people who don’t talk to each other.”

Bill Pink, senior partner, Millward Brown

SiNglE SOuRcE SHOPPER DATA

Retail Buyer Online Surfers

TV Viewers

examine tracked shoppers to reveal the per-centage of households that were exposed to various media and their actual contribution to revenue in the same time period. This is not modeled behavior but observed, in-market behavior. Additionally, it is possible to model the incremental household penetration gener-ated by each marketing activity.

Armed with this information, a marketer can optimize investments across platforms because the technique reveals the relationship between marketing activities and sales response much more accurately than models of the past. This is a thought-provoking advantage, because it inches marketing closer to an era of “perfect information” – not quite grasping the Grail, but nearing it. 2. An Accurate Picture of In-Market Responsiveness by Shop-per Segment: Because shopper behaviors are observed and not generated through statistics, it is possible to separate groups of shoppers into multicultural, gen-erational or other segments. Mar-keting responsiveness then can be read and compared.

Such in-market, passive obser-vation is likely to be more revealing of real-world responsiveness than

other methodologies such as central location testing, a technique that ultimately may be relegated to prelaunch diagnostics and com-munications refinement.3. An Assessment of Media Opportunity Costs: Interestingly, this technique allows marketers to look at the buying rates among shoppers who were exposed to advertising and those who were not. It also can show the buying rates of those not reached through online or television advertising. For example, category buyers who were not exposed may constitute an opportunity cost (lost sales) that can be pursued in subsequent media plans – when tracked consumption patterns could identify

optimal placement. That is really different. 4. The Ability to Better Optimize Media Plans: The “rule” of three exposures as the optimal frequency has been accepted for years. Single source data inputs may not completely debunk that, but they can create a more fact-based discussion of optimal frequency, as seen in the sample chart above.

While this kind of chart has been available in the past, it was harder to gather the data, so only the biggest brands could produce them. Alternatively, this graph could have been gen-erated through modeling, which theoretically at least makes it less accurate. As cross-plat-form analysis becomes more prevalent, the

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In season

Out of season

% of HHsBuyingBrands

Cumulative TV Exposures Per Household

Example of Brand Penetration by Frequency

Source: Millward Brown

“ The new team leads will manage a

joint venture between brands and

retailers, going after pockets of

demand and allocating resources to

maximize returns very specifically.”

Patrick Fitzmaurice, principal, The Capré Group

data should become more accurate and less expensive, allowing even smaller brands to get better decision-making tools.5. An Understanding of the Role Each Vehicle Plays in Driving Sales: Because opportunity-to-see data is tied to sales figures, it is possible to understand the role each marketing element has had on the business. Now, marketers can look at the real impact of TV or in-store, for instance. These vehicles play different roles that will be tracked – not merely modeled. (See chart, above.)

This is great news for shopper marketers, who are looking to create effective messages at different points along the path to purchase. Strategy and creative teams can leverage this information to drive specific behaviors at dis-tinct touchpoints.

Big Data, Big implicationsSince the turn of the millennium, shopper mar-

keters have been calling for their companies to “break down the silos” within entrenched legacy departments, which were developed in response to the marketplace needs of 20 years ago. What was an efficient practice in 1995 is hampering growth in 2012.

“We need to break down the silos. These conversations will not be easy because of the mindset that trade is trade, brand dollars are in one bucket and shopper dollars [are] in an-other,” Jim Fuqua, Supervalu’s then-director of shopper marketing, told Shopper Marketing in early 2011. Booz & Co. has similarly called for the change: “To maintain its growth and fulfill its promise, shopper marketing must evolve beyond a siloed, tactical practice and become a strategic capability that is better integrated with other major investments and across the marketing and media ecosystem.” (Shopper Marketing 3.0: Unleashing the Next Wave of Value. Booz & Co./GMA, 2010)

Repeated calls like those have met with a slow – even obstinate – industry response. While intentions are often sincere, reorganizing can be painful and, therefore, is often resisted. But big data will force the change. According to Bill Pink, senior partner at Millward Brown, New York, “The data itself will break the silos. You can’t have data that speaks to everything and people who don’t talk to each other.”

Patrick Fitzmaurice, principal of The Capré Group consultancy in Atlanta, envisions a new role for customer-facing talent that “will be very different from the rates-and-dates mental-ity of the past. The new team leads will manage a joint venture between brands and retailers, going after pockets of demand and allocating resources to maximize returns very specifically.”

The effective use of big data promises to an-swer the need to rationalize marketing invest-ments by tracking individual shoppers as they are exposed to multiple marketing elements. Further, it potentially can erode silo walls be-

cause the data will “speak to everything,” forc-ing heretofore discipline-focused marketers to do the same.

It also may level the playing field somewhat between large and small brands – and large and small marketing efforts. If the cost of digi-tal data output declines (as it has consistently in the past), the cost of measuring campaigns may become affordable for most marketers. The implication is that smaller, savvy players may better be able to give big brands a run for their money.

While these data techniques are still na-scent, one thing is certain: Big data is coming. It’s time for shopper marketers to prepare.

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Series Schedule

Part 1: Rationalizing the Investment

Part 2: Measurement of Shopper Behavior

Part 3: Measurement of Brand Impact

Part 4: Effective Integration Practices

Part 5: Retail Collaboration

Part 6: Directions for the Future

Liz Crawford has more than 20 years of brand management and consulting experience with a concentration in strategic innovation. Over the last few years, Crawford has focused on developing integrated shopper marketing strategies for Fortune 500 clients. Currently, Crawford is an analyst and contributing writer for the Path to Purchase Institute. McGraw-Hill released her book, “The Shopper Economy,” in March.

About the Author

JWT/OgilvyAction inc., conducting busi-ness under the OgilvyAction and JWT Ac-tion brands, is a fully integrated, end-to-end shopper marketing and experiential marketing agency with main offices in New York, Chicago and Akron, Ohio. It is part of the WPP Group.

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Tracked Vehicles and Proven Impact on Fast-Moving Packaged Goods

VEHiclE PROVEN ROlE

Trade promotion Bring in frequent shoppers

TV Bring in new users

Coupons Drive loyalty and usage

Internet Bring in new users

Source: Millward Brown