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Marketers today use multiple marketing channels that generate impression-level data that can be linked to unique individual, or household, customers. While most of these channels are digital, some are not, such as direct mail, call-center records or catalog mailings. A more appropriate and widely-accepted term is “addressable”. Done right, digital attribution is more than just a scheme to give credit to the addressable channels that precede a conversion (such as a purchase, request for information, or sign-up). It’s a way to assess performance, measure return on investment and, ultimately, guide marketing budget allocations to the most effective channels. The ultimate goal of marketing is to change consumer behavior. Thus, the true measure of
marketing effectiveness is not how likely a customer is to buy, but whether and by how much marketing increases a customer’s likelihood to buy. For that reason, any method used for digital attribution must be based on estimates of incremental effects. In addition, the incremental effects must express causality, not just correlation. In this world of targeting, customers are typically exposed to marketing because they are already considered more likely to make a purchase. But that doesn’t necessarily mean the marketing is precipitating a change in behavior. Similarly, executing a search or clicking on a display ad is a strong indicator of purchase intent, but that doesn’t mean the customer is influenced by the display ad or the landing page.
The distinction between a propensity to buy between different customers and the incremental persuasion power of marketing to an individual is at the crux of a viable digital attribution model, a fact that is often missed in the midst of fancy buzzwords. (Particular “buzzwords” to be skeptical of include Shapley value, game theory, and post-hoc control group.)