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Mercari AI’s research paper —”Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions”—accepted to KDD2022 Workshop

Overview

The 1st Workshop on End–to-End Customer Journey Optimization was co-located with the KDD 2022 conference. The theme of the workshop was to discuss ways to improve the user experience across the entire customer journey.

Key points of presentation

[Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions](Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions)

We summarize our major contributions as follows:

  • We propose a framework of promotion decision making by targeting customer direct and enduring effect. Our framework consists of direct and enduring response predictions, incentive allocation decision making and business impact evaluation.
  • We propose a customer direct and enduring effect (CDEE) model, which provides predictions of the direct purchase propensity and the enduring purchase amount of each customer.
  • We compare the CDEE model against benchmarks on two promotional campaigns in our company and achieve a significantly better performance.

Background

In the e-commerce marketplace, marketers run various types of promotions as a part of the customer relationship management (CRM) strategy in order to maintain customer relationships and guide consumers towards desired actions. In Mercari, multiple treatment promotions are also a common marketing strategy. To better cater to each customer’s needs, personalized promotion decision making is necessary and has garnered the attention of the research community. Some research optimizes promotion decision making by applying purchase prediction. While the modeling and optimization on the direct purchase probability brings profits to the business, there is still room for improvement.

First, different customers contribute differently to the revenue even when they complete a purchase. For promotions which aim to maximize revenue, we should consider not only the purchase probability but also the purchase amount in the decision making process.

Second, promotions can induce purchases but these often only turn out to be one-shot deals. On top of that, promotions might cause purchase acceleration or stockpiling. Therefore a post promotion dip in purchases is widely observed in marketing. To create promotions that aim to build long-term customer engagement and boost customer loyalty, the post-promotion period effect should be taken into account.

Summary of paper

To target the efficient customers from the perspective of revenue during and post promotion, we propose a framework of promotion decision making by modeling customer direct and enduring response. The direct response corresponds to the customer’s re- sponse during the promotion period, while the enduring response corresponds to the customer’s response during and after the promotional period. Our framework consists of both direct and enduring response predictions, incentive allocation decision making and business impact evaluation.

We propose a customer direct and enduring effect (CDEE) model which provides predictions of the direct purchase propensity and the enduring purchase amount of each customer.

We formalize the incentive allocation problem. Our optimization goal is to pick the best incentive types for customers maximizing the total enduring purchase amount while keeping the cost under budget.

To estimate the effect of decision making, we apply an unbiased evaluation approach on the business metrics with randomized control trial (RCT) data.We compare our method with benchmarks using two promotions in Mercari and achieve significantly better results on both model evaluation and business metrics.

About MDS Team

In the Marketing Data Science (MDS) team, we use statistics, machine learning, and mathematical optimization to improve the efficiency of marketing campaigns.

Author

ML Engineer

Jie Yang

  • Causal Inference
  • Computer Vision
  • Marketing Strategy

Jie joined Mercari as a ML Engineer in 2019. Her work is to support marketing strategies by Machine Learning to meet customer satisfaction and make marketing efforts efficient. Currently she is working on optimizing coupon distribution strategies by applying uplift modeling. Her research domain was computer vision at her master stage and she published papers on CVPR and ICCV. Check out her google scholar page here.

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