mercari AI


Mercari AI’s research paper—”Estimating the Effect of Timing on Coupon Effectiveness”— accepted to KDD2022 1st Workshop


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

  • Discount coupons are one of the marketing tools used by Mercari.
  • The effectiveness of the discount coupons has a strong effect on the business impact of our marketing campaigns.
  • The timing of a coupon can be a crucial factor in the effectiveness of the coupon.
  • We used uplift modeling to estimate the effect of timing on the effectiveness of the coupon without the need for a dedicated randomized control trial.


Mercari uses a number of coupons to motivate users to buy and sell items in the marketplace. The effectiveness of marketing campaigns (and therefore the improvement on the bottomline) is strongly dependent on how effective the coupons used in the campaigns are. While many factors can affect the effectiveness of a coupon, one can hypothesize it to depend on the timing the distribution of the coupon was scheduled. However, verifying such a hypothesis would typically require dedicated randomized control trials which costs time and effort.

Summary of paper

Estimating the Effect of Timing on Coupon Effectiveness

In this paper, we performed a natural experiment with uplift modeling to measure the effect of the timing of the distribution of the coupon on its effectiveness. This circumvented the need for a dedicated randomized control trial thereby saving on resources. Using uplift modeling, we found that the quicker we send coupons to users after they create an account, the greater the effectiveness of the coupon.

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.


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.