Sara M. Watson: The Uncanny Valley of Targeted Marketing

Scott Howe of Acxiom spoke at Harvard last week as part of the Topics in Privacy series. I’ve been really interested to follow the steps Acxiom is taking to set an example in the advertising industry to engage more directly with consumers through aboutthedata.com . Howe talked at length about the philosophy behind the site, its success in the first couple months, and addressed some of the early criticisms. I’m encouraged by what Acxiom is doing, but I walked away from the talk with more questions than answers. Howe shared some statistics on the site after it first opened in September—they had 500K visitors in the first month, and only 2% of visitors have opted out as a result of logging in (I have to wonder, if that’s a[n intentional] design flaw, and that the option is buried or hard to find). He also shared that 11% of people made corrections to the data, most often addressing political party, income, education, marital status, and occupation. Howe also shared that the site has very low return rates so far, i.e. that once logged in, people aren’t coming back. He acknowledges that individuals won’t have reason to come back until the value of updating and maintaining a relationship with Acxiom is more clear to consumers. Acxiom, like other data brokers,  is in the business of collecting, cleaning, analyzing, and segmenting consumer data from all kinds of sources. Aboutthedata only serves to show the demographic data, and some of the inferred insights about demographic data perhaps based on behaviorally tracked data. For example in my profile I am “inferred married” and it gives me the option to declare that I’m married. But Acxiom doesn’t expose the 70+ proprietary market segmentations* it has developed to describe me to marketers. I don’t get to object to being called a “Rolling Stone” or a “Midtown Minivanner” because these segmentations or “clusters” are fixed based on demographic details like household age, marital status, income, and “urbanicity.” Acxiom doesn’t expose this life stage segmentation to you, but I imagine mine is likely incorrect, given that Acxiom thought I owned a truck (I own no vehicle, let alone a truck). But aside from these broad generalizations about people types based on demographic variables, it seems like there isn’t more customer segmentation happening based on more behavioral data (or at least proprietary segmentation like that is being kept under wraps). Market segmentation is still almost entirely demand driven , that is marketers come to Acxiom looking for specific parameters to define their customer segmentations, and hasn’t yet evolved to take advantage of the promise of big data to drive segmentation from correlative discoveries in the data. For example, Howe’s described Porsche looking for the set of consumers who are likely to purchase a luxury vehicle in the next two weeks. The marketers, in this case Porsche, come to Acxiom with the set of parameters and models that will give them that set of customers to market to. Some of Acxiom’s customers are more sophisticated than others as to what parameters they are interested in (i.e. they have data scientists on their teams). So these segmentation parameters are generated by the demands of the marketer. They are essentially hypothesis driven, to match the product to the desired consumer behavior and interest data. The marketer says, “I’m looking for these people, Acxiom, show me where they are.” Acxiom will run those parameters, get rid of the twelve-year-olds who are ogling cars on the Porsche website, and deliver Porsche the men who are looking to buy their next midlife crisis fix so that Porsche can better target advertisement to those ready and willing customers. We haven’t gotten anywhere closer to letting the data tell us about what kinds of segmentations might be interesting to market to, or even more advanced, to let the data define new market opportunities . This would be the supply-defined segmentation model of the data broker. And it seems like an underdeveloped opportunity for brokers to take on a role in defining markets, with a supply-driven market segmentation derived from the correlations. But it’s also reassuring that the big data promises of correlative discovery hasn’t yet resulted in the creation of new markets. The marketers, for the most part, are still defining the segmentation. But it’s only a matter of time before defining markets with correlative methods becomes the value-adding, differentiating business of data brokers. Right now, segmentation for marketing purposes is only as useful as the market you think you are targeting. But given what we’ve seen in dragnet surveillance techniques for flagging behavioral patterns, I imagine we will begin to see the industry shift to include both demand (of marketers) segmentation, and supply (of the data brokers) proprietary insights gleaned from amassing and analyzing these huge datasets of both demographic and behavioral details. Right now there’s not enough finesse with correlations to handle false positives and false negatives to differentiate signal from noise.