#5019. Targeting customers under response-dependent costs

July 2026publication date
Proposal available till 24-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Modeling and Simulation;
Management Science and Operations Research;
Industrial and Manufacturing Engineering;
Computer Science (all);
Information Systems and Management;
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Abstract:
This study provides a formal analysis of the customer targeting problem when the cost for a marketing action depends on the customer response and proposes a framework to estimate the decision variables for campaign profit optimization. This study makes two contributions to the literature, which are evaluated on an e-commerce coupon targeting campaign. 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 an estimate of the average or customer-level treatment effect. We propose a framework to jointly estimate the treatment effect and the response probability by combining methods for causal inference with a hurdle mixture model. The proposed causal hurdle model achieves competitive campaign profit while streamlining model building.
Keywords:
Causal ML; Customer targeting; Data science; Decision analysis; OR In marketing

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