Virtual Quant Marketing Seminar - Walter Zhang
Optimal Comprehensible Targeting
Dear All,
Our next Virtual Quant Marketing Seminar for the semester is on Monday, October 14th at Noon ET. The speaker will be Walter Zhang (UPenn).
The zoom link for all the talks is below, note that this is a new link: https://upenn.zoom.us/j/93394346729?pwd=SoVXVesbltpYbQxo9tbbPF6YgopDl3.1
Title: Optimal Comprehensible Targeting
Abstract: 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 laws. First, I introduce a new method called Policy DNN, which combines policy learning and deep neural networks, to form a profit-maximizing black box benchmark and provide theoretical guarantees on its performance. In contrast to prior approaches that use a two-step method of estimating treatment effects before assigning individuals their treatment group, Policy DNN directly estimates treatment assignment, which improves efficiency. Second, I construct a class of comprehensible targeting policies that is represented by a sentence. Third, I show how to optimize over this class of policies to find the profit-maximizing comprehensible policy. I 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. Finally, I apply my framework empirically in the context of price promotions for a durable goods retailer using data from a field experiment. I quantify the cost of explanation, which I define as the difference in expected profits between the optimal black box and comprehensible targeting policies. The comprehensible targeting policy reduces profits by 7% or 22 cents per customer when compared to the black box benchmark.
The schedule for the rest of the semester is:
Monday, October 28, Noon ET - George Gui (Columbia)
Monday, November 4, Noon ET - Justin Grimmer (Stanford)
Monday, November 11, Noon ET - Soheil Ghili (Yale)
Monday, December 2, Noon ET - Davide Proserpio (USC)
We encourage you to use our google calendar to keep track of the talks