Johannes Haupt, Stefan Lessmann
arXiv 13 Mar 2020 · Econometrics · publishedEuropean Journal of Operational Research (2021) · 24 citations (OpenAlex)
arXiv:2003.06271 · PDF · DOI · OpenAlex · Extracted main text
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. Targeting a customer is profitable if the impact and associated profit of the marketing treatment are higher than its cost. Despite the growing literature on uplift models to identify the strongest treatment-responders, no research has investigated optimal targeting when the costs of the treatment are unknown at the time of the targeting decision. Stochastic costs are ubiquitous in direct marketing and customer retention campaigns because marketing incentives are conditioned on a positive customer response. This study makes two contributions to the literature, which are evaluated on an e-commerce coupon targeting campaign. First, we 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 an estimate of the average or customer-level treatment effect. Second, 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. Code is available at https://github.com/Humboldt-WI/response-dependent-costs.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Aurelie Lemmens and Sunil Gupta (2020) Managing churn to maximize profits | 1.000 | 5 | 3 | 100% |
| 2 | Michael C. Knaus, Michael Lechner, and Anthony Strittmatter (2021) Machine learning estimation of heterogeneous causal effects: Empirical monte carlo evidence | 0.950 | 7 | 6 | 86% |
| 3 | Eva Ascarza (2018) Retention futility: Targeting high risk customers might be ineffective | 0.950 | 7 | 4 | 86% |
| 4 | Floris Devriendt, Jeroen Berrevoets, and Wouter Verbeke (2021) Why you should stop predicting customer churn and start using uplift models | 0.950 | 7 | 4 | 86% |
| 5 | Günter J. Hitsch and Sanjog Misra (2018) Heterogeneous Treatment Effects and Optimal Targeting Policy Evaluation | 0.935 | 11 | 6 | 82% |
| 6 | Sören R Künzel, Jasjeet S Sekhon, Peter J Bickel, and Bin Yu (2019) Metalearners for estimating heterogeneous treatment effects using machine learning | 0.928 | 4 | 4 | 100% |
| 7 | Floris Devriendt, Darie Moldovan, and Wouter Verbeke (2018) A literature survey and experimental evaluation of the state-of-the-art in uplift modeling: A stepping stone toward the developm… | 0.928 | 4 | 3 | 100% |
| 8 | Robin M. Gubela, Artem Bequé, Fabian Gebert, and Stefan Lessmann (2019) Conversion uplift in e-commerce: A systematic benchmark of modeling strategies self | 0.928 | 4 | 3 | 100% |
| 9 | Behram Hansotia and Brad Rukstales (2002) Incremental value modeling | 0.811 | 4 | 2 | 100% |
| 10 | J.R. Bult and Tom Wansbeek (1995) Optimal selection for direct mail | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 38 scored citations.