An Effectiveness Verification Model for Coupon Distribution Measures Based on Machine Learning Considered Users' Purchase Intention

Abstract

Online coupon distribution is a significant marketing measure that leads to increased sales. However, distributing coupons blindly risks lowering a company's profit ratio. It is, therefore, essential to estimate the coupon effect. In addition, users' potential purchase intention is thought to make a difference in the coupon effect. For example, users with low purchase intentions are likely to increase their gross profit through coupons. In contrast, users with high purchase intentions will likely decrease their gross profit through coupons. Therefore, it is possible to conduct highly effective targeting by analyzing the relationship between potential purchase intention and the coupon effect. In this study, we propose a framework containing an experimental design and a verification method based on machine learning to analyze the relationship between the coupon effect and the user's potential purchase intention. Finally, we demonstrate the effectiveness of the proposed framework by applying it to real-world data.