Jinglong Dai, Hanwei Li, Weiming Zhu, Jianfeng Lin, Binqiang Huang
arXiv 10 Jun 2024 · Econometrics
arXiv:2406.05987 · PDF · DOI · OpenAlex · Extracted main text
Traditionally, firms have offered coupons to customer groups at predetermined discount rates. However, advancements in machine learning and the availability of abundant customer data now enable platforms to provide real-time customized coupons to individuals. In this study, we partner with Meituan, a leading shopping platform, to develop a real-time, end-to-end coupon allocation system that is fast and effective in stimulating demand while adhering to marketing budgets when faced with uncertain traffic from a diverse customer base. Leveraging comprehensive customer and product features, we estimate Conversion Rates (CVR) under various coupon values and employ isotonic regression to ensure the monotonicity of predicted CVRs with respect to coupon value. Using calibrated CVR predictions as input, we propose a Lagrangian Dual-based algorithm that efficiently determines optimal coupon values for each arriving customer within 50 milliseconds. We theoretically and numerically investigate the model performance under parameter misspecifications and apply a control loop to adapt to real-time updated information, thereby better adhering to the marketing budget. Finally, we demonstrate through large-scale field experiments and observational data that our proposed coupon allocation algorithm outperforms traditional approaches in terms of both higher conversion rates and increased revenue. As of May 2024, Meituan has implemented our framework to distribute coupons to over 100 million users across more than 110 major cities in China, resulting in an additional CNY 8 million in annual profit. We demonstrate how to integrate a machine learning prediction model for estimating customer CVR, a Lagrangian Dual-based coupon value optimizer, and a control system to achieve real-time coupon delivery while dynamically adapting to random customer arrival patterns.
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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 | Albert, J. and D. Goldenberg (2022) E-commerce promotions personalization via online multiple-choice knapsack with uplift modeling | 0.644 | 2 | 2 | 100% |
| 2 | Holthausen Jr, D. M. and G. Assmus (1982) Advertising budget allocation under uncertainty | 0.644 | 2 | 2 | 100% |
| 3 | Spiekermann, S., M. Rothensee, and M. Klafft (2011) Street marketing: how proximity and context drive coupon redemption | 0.644 | 2 | 2 | 100% |
| 4 | Kellerer, H., U. Pferschy, and D. Pisinger (2004) The Multiple-Choice Knapsack Problem, pp.\ 317–347 | 0.511 | 2 | 2 | 50% |
| 5 | Anderson, E. T. and D. I. Simester (2001) Are sale signs less effective when more products have them? | 0.511 | 2 | 1 | 100% |
| 6 | Abdi, H. et al (2007) Bonferroni and sidák corrections for multiple comparisons | 0.405 | 1 | 1 | 100% |
| 7 | Anderson, E. T. and D. I. Simester (2001) Price discrimination as an adverse signal: Why an offer to spread payments may hurt demand | 0.405 | 1 | 1 | 100% |
| 8 | Barlow, R. E. and H. D. Brunk (1972) The isotonic regression problem and its dual | 0.405 | 1 | 1 | 100% |
| 9 | Bawa, K. and R. W. Shoemaker (1989) Analyzing incremental sales from a direct mail coupon promotion | 0.405 | 1 | 1 | 100% |
| 10 | Chen, P., W. Ma, S. Mandalapu, C. Nagarjan, J. Shanmugasundaram, S.… (2012) Ad serving using a compact allocation plan | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 32 scored citations.