Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions

TL;DR

This paper introduces a personalized promotion decision framework that models both immediate and lasting customer responses to optimize incentives, improving ROI and customer loyalty in e-commerce.

Contribution

It presents a novel customer direct and enduring effect (CDEE) model for personalized promotion decisions, incorporating post-promotion effects for better ROI optimization.

Findings

Significantly improved promotion ROI in Mercari case studies.

Effective modeling of both direct and enduring customer responses.

Enhanced customer retention through personalized incentive allocation.

Abstract

Promotions have been trending in the e-commerce marketplace to build up customer relationships and guide customers towards the desired actions. Since incentives are effective to engage customers and customers have different preferences for different types of incentives, the demand for personalized promotion decision making is increasing over time. However, research on promotion decision making has focused specifically on purchase conversion during the promotion period (the direct effect), while generally disregarding the enduring effect in the post promotion period. To achieve a better lift return on investment (lift ROI) on the enduring effect of the promotion and improve customer retention and loyalty, we propose a framework of multiple treatment promotion decision making by modeling each customer's direct and enduring response. First, we propose a customer direct and enduring effect (CDEE) model which predicts the customer direct and enduring response. With the help of the predictions of the CDEE, we personalize incentive allocation to optimize the enduring effect while keeping the cost under the budget. To estimate the effect of decision making, we apply an unbiased evaluation approach of business metrics with randomized control trial (RCT) data. We compare our method with benchmarks using two promotions in Mercari and achieve significantly better results.