Shopper presence, as well as their behavior, on media platforms is driven by their intrinsic nature and preferences. Many forms of media, especially digital forms, are delivered to maximize exposure. As a result, the measured effects of media, delivered on the focal platform or channel, may be over or under estimated.
Let’s consider an example. Sometimes digital marketers buy up the cheapest impressions to maximize exposure. These impressions are leftover as a result of other advertisers intentionally targeting high value shoppers, increasing exposure to the less attractive shoppers that remain. The implication is that an analysis of the effectiveness of digital display ads would underestimate the impact of the focal digital advertising channel, and subsequently the marketers would overly reduce digital display ad pressure in error, rather than optimally target the most responsive shoppers.
No matter which statistical methods or machine-learning algorithms are applied, if exposure propensity is unequal and non-random, biased media measurement is likely. Fortunately there is a long lineage of scientific research that proposes remedies for the probable bias. In essence this research reveals that if you weigh the observed dependent variable (click, conversion, etc.) by the probability the shopper is exposed to the media, you can carry out your analysis on the transformed dependent variable. The real challenge occurs when exposure is due to aspects that are not directly observed in the available data. If you can recover these unobserved aspects from the data, such as a the effectiveness of banner ads on a particular shopper, then you can jointly recover the dependency between exposure and the effectiveness of banner ads, and eliminate the bias due to unintentional banner ad targeting. I can’t promise this is an easy thing to do, and unfortunately it cannot be achieved with standard statistical analysis packages.
Next time you are presented with digital media attribution results, or you are pitched a digital attribution solution be sure to ask yourself whether some unintentional form of targeting could be contaminating the results. Then ask what the attribution solution does to control for the bias that this unintentional targeting creates.