The Myth of Targeting
Why too much data has led to missed opportunities for brands to grow
In the course of an average day, an individual consumes some form of media for more than half of their day. While preliminary for 2021, recent research reveals that the average American consumes about 13.4 hours of media a day; nearly 8 of those hours classified as digital media.1
Those 8 hours are filled with data harvesting opportunities. With every flutter of fingers on a keyboard or a strolling stroke of a finger on a screen, oodles of data are generated. The data might be demographic (gender, age, location) or behavioral (visited a news source or purchased an Instapot). When combined with each other, psychographics can be inferred (guys 25 to 54 from New Orleans visiting ESPN.com like sports and cooking). For years, advertising technology companies have worked to render human activity into machine readable form and turn the resulting output into more information about audiences to target with advertising. That targeting has ostensibly become increasingly precise, with more data points brought to bear on each individual contact with media. These data points number in the hundreds, even thousands (It’s been reported that Facebook collects around 52,000 data points on any one user).2. All in the service of learning more about customers and then using that knowledge to find more customers, which is itself in the quest to eliminate the always lurking threat of “waste.”
Loath to contribute to the body of 'you're doing it wrong' admonishment content that reached its peak explicitly in 2015 but still shows up in other forms of guidance and advice articles (Slate has 204 items dedicated to the category dating back to 2015 and earlier)3, but this approach we’ve taken to audience targeting over the decades has been wrong.
Over the decades, as greater amounts of data became available on existing customers, the more advertiser focus turned to audiences that looked like the existing customer as that customer was reflected in that data.
With the advent of digital advertising and its ability to tie the advertising activity to the data, the focus on the “look-alike” grew more intense.
Digital advertising also affected closing the loop between advertising event, the data that advertising used for targeting, and the actions and interactions taken in response to that advertising. The data generated from these interactions put the advertising being done right at the feet of actions taken and where those actions occur.
Over time the movement of advertising closer and closer to the actions taken led to targeting methods that preference the locations of the action rather than the people advertisers want to take that action.
This has led to the selection effect in targeting decisions. What’s the selection effect? Known also as selection bias, it is the introduction of factors that can skew conclusions drawn from observable phenomenon and associated data based on how those, and which, factors are chosen. While there are multiple types of selection bias, the two that have impact in advertising are sample and time interval. Stripped to its underwear, sample bias is the result of a non-random selection of an observed population. In the case of look-alike targeting, its the use of data related only to existing customers. Decisions based only on data gathered from purchasers means the data is based solely on the act of buying. This is akin to putting ads for Roundtable Pizza outside Roundtable Pizza parlors and then concluding that the ads led to pizza purchases. Time interval bias is artificially selecting a time frame for observation that may not cohere with a natural timeframe within which certain behaviors might occur. This is like gathering data about turkey buyers by looking only at the week prior to Thanksgiving. Given both examples: sure, advertising pizza outside of pizza parlors gives you a better than random chance at reaching people who buy pizza; but if I want to grow share and prove the advertising contributes positively to that goal, shouldn’t the advertiser be reaching people who aren’t patrons of Roundtable, too? And if I want to sell more turkey, shouldn’t I want to know about ALL customers who buy turkey at any time, not just the one occasion a year when I know turkey is purchased even by those who might not buy turkey the rest of the year? (Not to get into the weeds of statistics, but this also screws with any useful negative binomial distribution reading).
Advertisers need to start looking at audiences again and not just countable actions. You aren’t growing share if all you do is advertise to the people who have your product in their hands, standing in line at the check out.
Does this mean no targeting data should be used? No, but more rigorous metrics planning should be; and more rigorous testing protocols should be deployed. There’s been too much reliance on the magical thinking of conventional wisdom. While not everything meaningful is countable and not everything countable is meaningful, some things meaningful are countable, and even empty spaces are worthy of study.
What’s needed is not more data, or even better data — though there is an awful lot of chaff that is passed along as wheat — but better metrics planning.
What does that look like? We'll cover off on that next week.