November 23, 2005

Psyching Out the Voters If demographics are based on the notion that “birds of a feather flock together” then psychographics works on the premise that on selected topics birds of multiple flocks care about the same thing in similar ways. Mike Bloomberg used psychographic models and segmentation to get beyond “Soccer Moms” in his re-election campaign for Mayor of New York. Some observers are saying this changes the political calculus, though most of us think a mastery of psychographics is the secret genius Karl Rove has traded on for years. Demographics are generally good predictors of the gross segments. Age, education and income pretty much dictate your taste in consumer goods, real estate and politics. When you overlay purchase data, be it anything from cars, clothes to magazine subscriptions the picture gets more nuanced, though the conclusions are clearly inferences not facts. For many marketing purposes this level of specfication is good enough. Psychographics take this data and mix it with attitudinal data to produce a richer profile. It’s a simple formula. The more data you mix the more nuanced the profile you get. The more nuanced the profile, the higher the cost. Most marketers can’t or won’t justify the incremental cost of psychographic profiling because the value of a sale is too low or the need for such precision targeting isn’t as great. Generally only the highest value items with long or complex selling cycles are willing to invest in a psychographic approach. For years big research firms have been creating segmented profiles which combine demographic data with purchase history or purchase intention to yield discrete groups which are likely to be targeted by marketers. That’s how Volvo and Bill Clinton discovered “Soccer Moms” as a distinct subset of the population worthy of specific messages and a bit of romancing in the first place. Psychographics approach the compilation of a group by zeroing on a common need, attitude or behavior. For Bloomberg, “Fearful or Anxious New Yorkers” (FANS) are lower income people heavily dependent on the City and its social services both to provide income, income support and basic social services. The group cuts across zip codes, age and ethnicity lines. His message to them reinforced the idea of security. The City will thrive. They will keep their jobs. The City will keep services open and flowing. The proof points were his record on fighting crime and terrorism and his track record on job creation and heath care. Compare this appeal with that to “Cultural Liberals” those higher income New Yorkers concerned that the arts, music and culture scene be maintained both for their own enjoyment, the status of the City and as a lure for tourists. The pitch here was his background as a businessman and his strong fiscal management which allows the City to afford these things and “do more with less.” Why did Bloomberg go to this extent when he was paired against a has-been politician from the Bronx? I’m guessing because he can. Will this create a new paradigm for political campaigns, as suggested by Jim Ruttenberg in the New York Times? Don’t bet on it. Bloomberg spent 10 million dollars on what his campagned called "list development". That's a number that would scare even a national campaign. But look for a clever contender, with a good database marketer advising her, to take existing psychographic data sets from commercial vendors and enhance them with voting records, data on political or charitable donations and real-time polling data to yield the same insight for targeting at a shade of the price.
Better Behavioral Marketing Behavioral targeting is emerging as a standard expectation for ad targeting and dynamic content serving on the Web. Behavioral targeting promises improved media efficiency and the ability to identify and zero-in on those with a higher propensity to buy. Yet in this evolving field, there are no clear understandings about what inferences can properly or accurately be drawn from demonstrated behavior. Other than the individual who clicks through to a completed sale, most behavioral targeting at this point is guess work. Consider several approaches for observing, evaluating or scoring behavior. Repetitive Behavior Logically someone who does the same thing again and again or visits the same place repeatedly is probably more interested than the average Joe. Assuming that most people only make one or two clicks in error, it is reasonable to guess that someone returning for a 3rd click is probably interested, if not a real buyer. My wife is a prime example. She likes to visit future purchases frequently before buying. To her, multiple trips to the shoe store, the furniture store or the big box retailer to hover over her intended item are no big deal. In fact she enjoys the process of visiting and revisiting. With each incremental step she learns more, increases her desire and adds layers of nuance to her buying rationale. Repetition confirms her interest or ratchets up her intent and her commitment to the purchase. The same holds true on the Web. She will click and click again on an item. Perhaps she’ll visit it at multiple sites in search of greater product detail, to compare prices or to discover a deal on shipping or a favorable return policy. If we tracked her behavior with a cookie or some other technology, the vital questions would be -- how many visits signal her intent and on which visit should we prompt her to buy? Should we dynamically serve her content or intervene by popping up an offer on her third or fourth visit or on her sixth?? How do we know how much repetition is sufficient to encourage her to convert or at what point she might be freaked out by a big brother intervention and abandon interest? Sequential Behavior Perhaps if we watched where she went before and after visiting the product, we might get a better idea. If she visits the same product at a competitor site, does that signal intensity of interest or intent? If she looks at a similar product or a product that normally goes together and sells together with the first product can we infer a pending purchase? If she puts the cherished item in the shopping cart and abandons it can we assume “No” is not really no? And it is a fair expectation that if you abandon a shopping cart, someone may follow-up with a question or an offer? What about if she makes a beeline for something? Would that be enough evidence to treat her differently from the great mass of web surfers? Say she responded to an email and clicked on a designated landing page or navigated from the home page to a particular product page in the shortest possible sequence (3 clicks?), would that mark her as an A prospect and separate her from the herd? Response Devices How about if she fills in a form, signs up for an e-mail newsletter, downloads a whitepaper, prints out a PDF, uses a zoom feature, puts data into a calculator or clicks a “contact me” button? Assuming that only X percent respond frivolously, would using the provided response device qualify her as a hot prospect or merely mark her as either a well trained consumer or as a tire kicker? If she fills in only part of the form, what can we infer? Is a newsletter subscriber more interested than a downloader? Can we distinguish between serial subscribers and sequential downloaders, who could be anyone from your next best customer to a high school kid working on a project? Direct marketers will tell you that even among those who answer the call to action and utilize the provided response mechanisms; most responders are generally interested but not ready-to-buy. So the act of responding, while rarely more than 2 percent of those exposed to an offer, still doesn’t turn you into a qualified, hot lead or give us any indication that for a little extra effort or TLC we can get you to buy. So what’s a marketer to do? Charged with generating demand, we...

Danny Flamberg

I am a veteran marketing consultant working with leading and emerging brands

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