Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2020 005

Targeting Cutsomers Under Response-Dependent Costs

Johannes Haupt
Stefan Lessmann

This study provides a formal analysis of the customer targeting decision problem
in settings where the cost for marketing action is stochastic and proposes a
framework to efficiently estimate the decision variables for campaign profit
optimization. Targeting a customer is profitable if the positive impact of the
marketing treatment on the customer and the associated profit to the company is
higher than the cost of the treatment. While there is a growing literature on
developing causal or uplift models to identify the customers who are impacted
most strongly by the marketing action, no research has investigated optimal
targeting when the costs of the action are uncertain at the time of the
targeting decision. Because marketing incentives are routinely conditioned on a
positive response by the customer, e.g. a purchase or contract renewal,
stochastic costs are ubiquitous in direct marketing and customer retention
campaigns. This study makes two contributions to the literature, which are
evaluated on a coupon targeting campaign in an e-commerce setting. First, the
authors formally analyze the targeting decision problem under response-dependent
costs. Profit-optimal targeting requires an estimate of the treatment effect on
the customer and an estimate of the customer response probability under
treatment. The empirical results demonstrate that the consideration of treatment
cost substantially increases campaign profit when used for customer targeting in
combination with the estimation of the average or customer- level treatment
effect. Second, the authors propose a framework to jointly estimate the
treatment effect and the response probability combining methods for causal
inference with a hurdle mixture model. The proposed causal hurdle model achieves
competitive campaign profit while streamlining model building. The code for the
empirical analysis is available on Github.

Heterogeneous Treatment Effect, Uplift Modeling, Coupon Targeting,
Churn/Retention, Campaign Profit

JEL Classification: