Mercari AI’s research “Estimating the impact of coupon non-usage” accepted to ECML/PKDD 2022’s workshop
We are pleased to announce that the "Estimating the impact of coupon non-usage" by Engineer Deddy Jobson of the Mercaril AI team has been accepted for the Uplift Modeling Tutorial & Workshop collocated with the international conference "ECML PKDD 2022".
ECML PKDD (“European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases”) is one of the top conferences in the field of data mining and machine learning, and is held annually by researchers from around the world.
The accepted papers were presented at the workshop of "ECML PKDD 2022" held at Grenoble, France in September 2022.
■Key points of presentation
We look into the phenomenon of users not redeeming discount coupons.
We use causal analysis to quantify the negative business impact of the lack of usability of marketing coupons.
■Background / 研究の背景
Mercari runs many marketing campaigns in the form of coupons which can be redeemed by users when making purchases.
While the marketing campaigns are very successful, we observe that a substantial number of users who have access to the coupon fail to make use of it.
While we can measure the coupon redemption rate and find the number of users who bought items without coupons, we can’t directly estimate the counterfactual number of users who didn’t buy a coupon but would have been able to use the coupon they were given.
■Summary of paper / 研究概要
We created a causal diagram and used a bayesian model to estimate the business impact of the lack of usage of the coupon.
We show through our experiments that campaigns can greatly benefit from an increase in usability of the coupons we distribute.
■About MDS Team
The Marketing Data Science Team makes use of statistics, machine learning, and mathematical optimization to improve the efficiency of marketing campaigns. We actively learn and adopt the state of the art methods used in the literature to solve technical problems and publish papers along the way.