Optimal Comprehensible Targeting

Developments in machine learning and big data allow firms to fully personalize and target their marketing mix. However, data and privacy regulations, such as those in the European Union (GDPR), incorporate a "right to explanation," which is fulfilled when targeting policies are comprehensible to customers. This paper provides a framework for firms to navigate right-to-explanation legislation. First, I construct a class of comprehensible targeting policies that is represented by a sentence. Second, I show how to optimize over this class of policies to find the profit-maximizing comprehensible policy. I further demonstrate that it is optimal to estimate the comprehensible policy directly from the data, rather than projecting down the black box policy into a comprehensible policy. Third, I find the optimal black box targeting policy and compare it to the optimal comprehensible policy. I then empirically apply my framework using data from a price promotion field experiment from a durable goods retailer. I quantify the cost of explanation, which I define as the difference in expected profits between the optimal black box and comprehensible targeting policies. Compared to the black box benchmark, the comprehensible targeting policy reduces profits by 7.5% or 23 cents per customer.