![Sample our Engineering & Technology journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5933/TOC-Subject-Sample-2021-Engineering-Tech.jpg"})
Abstract
In recent years, web marketing has thrived, and online coupon distribution has become a significant marketing measure that leads to increased sales. However, randomly distributing coupons risks lowering the profit ratio of companies. Therefore, it is important to estimate the effect of coupons and analyze the causal relationship between coupons and results. The potential purchase intention (PPI) of users is believed to influence the effect of coupons. For example, distributing coupons to users with a low PPI is likely to increase the gross profit of companies, whereas distributing coupons to users with a high PPI is likely to decrease the gross profit. Therefore, by analyzing the relationship between PPI and the effect of coupons, highly effective targeting can be conducted based on the PPI. In this paper, we propose an experimental design based on machine learning to analyze the effect of coupons, which varies depending on the PPI. We propose a method to predict users’ PPI based on their purchase history data using machine learning and analyze the relationship between PPI and the effect of coupons. Finally, we demonstrate the effectiveness of the proposed framework by applying it to real-world data.
Acknowledgment
The authors express their gratitude to Hokuto Sasaki and Tomoaki Ozawa for their helpful discussions and reviews of the contents of our paper.
Disclosure statement
No potential conflict of interest was reported by the author(s).