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The Mercari AI team’s research “Strategic Coupon Allocation for Increasing Providers’ Sales Experiences in Two-sided Marketplaces” has been accepted at the KDD2024 TSMO 2024 Workshop

概要

We are pleased to announce that the paper "Strategic Coupon Allocation for Increasing Providers' Sales Experiences in Two-sided Marketplaces," authored by engineers Koya Ohashi, Sho Sekine, Deddy Jobson, and Jie Yang from the Mercari AI team, has been accepted and presented at the Workshop on Two-sided Marketplace Optimization (TSMO 2024) at KDD2024, an international conference in the field of data mining.

This research was conducted in collaboration with the University of Tsukuba, with support from "mercari R4D," the research and development organization of Mercari.

KDD is one of the most prestigious international conferences in the field of data mining, held annually by researchers from around the world. This year, it was held in Barcelona, Spain, from August 25 to 29, 2024.

Key points of presentation

Strategic Coupon Allocation for Increasing Providers' Sales Experiences in Two-sided Marketplaces

  • In this presentation, we proposed a coupon distribution strategy aimed at providing as many sellers as possible the experience of selling their products.
  • Through the satisfaction brought about by the selling experience, users are more likely to continue listing their items actively, which is expected to enhance the attractiveness of the platform as well as the quantity and diversity of the products listed.

Background

In a C2C marketplace like Mercari, where the platform provider does not have a direct supply chain, it is necessary to encourage individual sellers' voluntary listing activities to increase the quantity and variety of listed items. Since listing items involves certain efforts such as selecting products, taking photos, and packaging after sale, strong motivation is essential for sellers to act actively. Although the factors that influence listing motivation vary by individual, we believe that the most fundamental and important experience is the "selling experience" (the experience of having products sold). Therefore, it is crucial to provide as many users as possible with the selling experience.

However, in two-sided markets like Mercari, there is a risk that transactions might be skewed towards a certain group of users without appropriate intervention by the platform provider. To address this, our Data Science team aimed to use coupons, one of the most commonly used marketing tools, to alleviate the concentration of transactions and provide more sellers with the selling experience.

Summary of paper

In this paper, we propose a method for optimizing coupon distribution strategies with the number of users having selling experiences as the KPI. Generally, to optimize coupon distribution for a specific KPI, targeted interventions for the users are necessary. In this paper, we propose an efficient coupon distribution strategy using Uplift modeling and mathematical optimization.

By utilizing the proposed method, we confirmed with Mercari's actual data that more sellers could have selling experiences compared to conventional methods. The implementation of this method is expected to enhance the satisfaction of many sellers and promote their voluntary listing activities.

About Data Science Team

Mercari's Data Science team leverages techniques such as statistics, machine learning, and mathematical optimization to conduct customer insight analysis, enhance the efficiency of marketing activities, and develop product features.

About Mercari R4D

Mercari R4D was established in December 2017 as a research and development organization that aims to implement its findings practically, as part of the world at large. Under its mission of “pioneering the path toward undiscovered value,” R4D promotes a co-innovation approach to solving complex social issues by leveraging the power of science and technology, and also by going beyond the conventional boundaries of industry, academia, and government in order to realize a society where limited resources are circulated and all people can unleash their potential.

Author

ML Engineer

Koya Ohashi

  • Data Science
  • Personalization
  • Statistical Machine Learning

After working as an analytics consultant, Koya joined Mercari in 2022. As a marketing data scientist at Mercari, he analyzed marketing measures using machine learning and statistical models. He is currently working on measures related to improving customer satisfaction, and is responsible for a wide range of tasks from project design to implementation and evaluation.

ML Engineer

Deddy Jobson

  • Causal Inference
  • Marketing Strategy
  • Statistical Machine Learning

Deddy is a ML Engineer who joined Mercari as a new graduate. He is responsible for analyzing marketing campaigns using statistical models and mathematical optimization. On top of predicting which users respond positively or negatively to campaigns, Deddy also provides detailed explanations for those behaviors. He then shares those insights with stakeholders to discuss ways to improve future campaigns.

ML Engineer

Jie Yang

  • Causal Inference
  • Computer Vision
  • Marketing Strategy

2019年、ML Engineerとしてメルカリに入社。主な担当領域は、機械学習によってマーケティング戦略をサポートし、顧客満足度を満たし、マーケティング活動を効率化すること。現在は、Uplift modelingを使ったクーポン配布戦略の最適化に取り組んでいる。修士での研究分野はコンピュータービジョンで、CVPRとICCVに関する論文を発表している。 Google scholarページはこちら

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