Strategies for Leveraging Customer Insight Data
There's been a lot of speculation that a certain class of data is losing its luster. Data that describes individuals and/or segments — often referred to as demographic, third-party or offline data — is increasingly being written off as commoditized and not particularly valuable for marketers, particularly in online realms. These views, while somewhat understandable for those who have only lived and breathed in online worlds, sell short the predictive power of this data category.
I use the term "multidimensional insight" to describe this data. This descriptive data includes addressable, individual elements along with his/her household characteristics, geographies and segments. These insights include descriptive variables such as whether members of a household are married, if children are present, income and wealth categorizations, lifestyle interests and past purchase behaviors, property characteristics, and segments which link to broader research surveys. In short, they're the types of data that have long been a staple of direct marketing and certain branded advertising.
Declaring this descriptive data category of multidimensional insight dead, dying or commoditized is wildly premature. I'll posit a countertheory to the prevailing wisdom that the explosion of media channels and the accompanying classes of data (e.g., search, open and clickthrough rates, clickstream, in-market, social sharing, online purchase behavior, first-party/self-reported/user-generated, etc.) will increase the value of and demand for high quality, accurate and relevant consumer insights. Why?
Marketing is both an art and a science of messaging plus math, but it's still based on imperfect information. Search offers perhaps the most pinpointed information, yet context still matters. For example, if I search "Lexus," what results and ads are most relevant for me? It depends on many things. The search term "Lexus" offers a clue. But that buying clue, when combined with insight about my income, age, whether I have kids, what vehicle(s) I drive currently and where I live, becomes much more actionable. The more a marketer knows about someone, the greater the likelihood that they can craft a product/offer/message combination, search result or advertisement that's relevant to that individual or audience.
Scale optimizes marketing ROI
At marketing conferences and in various trade publications, some form of the following is often expressed: "In the era of Facebook and Twitter, who needs third-party data? People share everything." It's true that people do share an amazing amount of information these days. Yet with all that sharing, there's still a paucity of relevant data for marketing scale. Huh?
Even with all the indicators, comments, explicit references to being in-market, clicks, and more, there's still not enough actionable insight being generated for marketers to achieve reasonable scale for targeted campaigns without using the necessary descriptive data for look-alike analysis. Back to the Lexus example: it's helpful for Lexus to see a tweet from one person indicating that he's in-market for a Lexus. Lexus can reach that person with all kinds of messaging. That's just one person, however. Lexus can find some look-alikes based on similar online behaviors, but that's still likely a small sampling of the audience it needs to reach. In order to scale effectively, look-alike modeling, selections and messaging powered by the insights generated from the right data types are mandatory to identify and target that particular, high-value audience Lexus wants to reach.
While marketing budgets are often siloed by channels, 70 percent of online consumers display multichannel behavior and are four times to five times more valuable than single-channel customers. Thus winning brands will be those that present a consistent, logical, multichannel experience for their audience. To that end, leading marketers are increasingly finding ways to synchronize messages, offers and even campaign selection across channels. Doing so based exclusively on exhibited behaviors in one channel or another may be completely off target when applied broadly in many channels, however.
Deep, meaningful consumer insight provides an important Rosetta Stone, enabling translation and consistency from one channel to another. If Lexus knows nothing else besides the fact that 60 percent of the individuals who buy its RX model have children and an annual income above $90,000, it can use that insight to target placement and messaging of its advertising intelligently on television, online, in email, outdoors and in print. Then the more information it can layer based on an individual's channel behaviors, purchasing life cycle, etc., the more Lexus can use that information to tweak channel budgets, individual offers and more. A key basis for optimizing messaging and coordinating customer engagement across channels is cultivating the right descriptive data to refine those deep, meaningful consumer insights as a starting point for reaching high-value, desired audiences and individuals with consistency.
In 1982, Jack Valenti, head of the Motion Picture Association of America, said the following to a Congressional panel exploring legal issues posed by the new technology of video cassette recorders: "I say to you that the VCR is to the American film producer and the American public as the Boston Strangler is to the woman home alone." Valenti turned out to be wrong. Movie producers quickly realized enormous new revenue streams from the seemingly substituting but actually complementary VCR technology. So it will be with consumer insight.
The question for marketers will not be "Should I use behavioral data or descriptive data to build this insight?" It's not an either/or proposition. Marketers will end up using all of the types of customer data for the context, scale and cross-channel consistency it provides. This type of data has been a marketing mainstay, underpinning direct marketing to individuals and households, fueling stimulus to response and broadcasting marketing to broader segments and audiences. The media channel explosion we're experiencing today, and all the various types of (often conflicting) data created by it, will actually enhance the value of that precious insight rendered by descriptive data, not render it obsolete.
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