arXiv 12 Apr 2022 · Econometrics · 4 citations (OpenAlex)
arXiv:2204.05793 · PDF · DOI · OpenAlex · Extracted main text
With advances in estimating heterogeneous treatment effects, firms can personalize and target individuals at a granular level. However, feasibility constraints limit full personalization. In practice, firms choose segments of individuals and assign a treatment to each segment to maximize profits: We call this the coarse personalization problem. We propose a two-step solution that simultaneously makes segmentation and targeting decisions. First, the firm personalizes by estimating conditional average treatment effects. Second, the firm discretizes using treatment effects to choose which treatments to offer and their segments. We show that a combination of available machine learning tools for estimating heterogeneous treatment effects and a novel application of optimal transport methods provides a viable and efficient solution. With data from a large-scale field experiment in promotions management, we find our methodology outperforms extant approaches that segment on consumer characteristics, consumer preferences, or those that only search over a prespecified grid. Using our procedure, the firm recoups over $99.5%$ of its expected incremental profits under full personalization while offering only five segments. We conclude by discussing how coarse personalization arises in other domains.
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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 | Hitsch, G. J., S. Misra, and W. W. Zhang (2024) Heterogeneous treatment effects and optimal targeting policy evaluation | 1.000 | 5 | 4 | 100% |
| 2 | Peyré, G. and M. Cuturi (2019) Computational Optimal Transport | 0.843 | 3 | 3 | 100% |
| 3 | Dubé, J.-P. and S. Misra (2022) Personalized Pricing and Consumer Welfare | 0.737 | 4 | 3 | 50% |
| 4 | Bergemann, D. and A. Bonatti (2011) Targeting in advertising markets: Implications for offline versus online media | 0.737 | 3 | 3 | 67% |
| 5 | Wager, S. and S. Athey (2018) Estimation and Inference of Heterogeneous Treatment Effects using Random Forests | 0.737 | 3 | 2 | 100% |
| 6 | Galichon, A (2016) Optimal Transport Methods in Economics | 0.693 | 6 | 4 | 33% |
| 7 | Chiong, K. X., A. Galichon, and M. Shum (2016) Duality in dynamic discrete-choice models | 0.644 | 2 | 2 | 100% |
| 8 | Farrell, M. H., T. Liang, and S. Misra (2020) Deep Learning for Individual Heterogeneity | 0.644 | 2 | 2 | 100% |
| 9 | Farrell, M. H., T. Liang, and S. Misra (2021) Deep Neural Networks for Estimation and Inference | 0.644 | 2 | 2 | 100% |
| 10 | Gupta, S. and P. K. Chintagunta (1994) On Using Demographic Variables to Determine Segment Membership in Logit Mixture Models | 0.644 | 2 | 2 | 100% |
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