mlsanityData to 7 Oct 2026

Estimating the Effect of Timing on Coupon Effectiveness

arXiv 2606.30664 ↗First posted 17 Jun 2026stat.AP cs.AI cs.LGcited by 0

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Abstract (arXiv)

The coupon incentive is one of the most common tools marketers use to court users to engage with a business at various stages of the customer life cycle. A variety of factors can affect the effectiveness of a coupon incentive on users, timing being one of them. We hypothesize that coupons can be more effective when delivered at critical times in the customer journey, right when a user is engaging with the platform. Verifying such a hypothesis would typically require real time event-triggered coupon distribution software that may be too expensive to implement. In this paper, we propose a framework in which we apply causal inference on "natural randomized control trial experiments" to measure the effectiveness of sending coupons at the right time to users without requiring a dedicated AB test. We demonstrate the usefulness of our framework in the case of a user onboarding coupon campaign held in our company and show how the results can lead to correct data-driven decisions for the business. Furthermore, in order to test the generalizability of our framework, and to make our research more reproducible, we apply our framework on a user retention campaign with a publicly available dataset.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 3 of its 23 references that are in the map. First posted on arXiv 17 Jun 2026.

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References · 3

  1. 2017Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learningcited by 475
  2. 2019On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternativescited by 20
  3. 2021A framework for massive scale personalized promotioncited by 4

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