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Modelling delayed redemption with importance sampling and pre-redemption engagement

By Samik Datta, Anshuman Mourya, Anirban Majumder, Vineet Chaoji
2023
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Rewards-based programs are popular within e-commerce online stores, with the goal of providing serendipitous incentives to delight customers. These rewards (or incentives) could be in the form of cashback, free-shipping or discount coupons on purchases within specific categories. The success of such programs relies on their ability to identify relevant rewards for customers, from a wide variety of incentives available on the online store. Estimating the likelihood of a customer redeeming an incentive is challenging due to 1) data sparsity: relatively rare occurrence of coupon redemptions as compared to issuances, and 2) delayed feedback: customers taking time to redeem, resulting in inaccurate model refresh, compounded by data drift due to new customers and coupons.

To overcome these challenges, we present a novel framework, Dress (Delayed Redemption Entire Space Sampling), that jointly models the effect of data sparsity and delayed feedback on redemptions. Our solution entails an architecture based on the recently proposed Entire Space Model ([12]), where we leverage pre-redemption engagement of customers (e.g. clipping of coupon) to overcome the sparsity challenge. The effect of delayed feedback is mitigated via a novel importance sampling mechanism, whose efficacy we formally analyze via a novel application of Influence Function ([10]). Experimental evaluation suggests that Dress achieves significant lift in offline metric in comparison to state-of-the-art alternatives. Additionally, a live A/B test with Dress resulted in a lift of 10 basis points in the redemption rate.
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