Uplift Modeling for Cost Effective Coupon Marketing in CtoC ECommerce Ecommerce companies often provide marketing incentives such as price discount coupons to motivate new customers to make their first purchase. However, many customers make purchases only when coupons are distributed to them; they stop making purchases after using the coupons. Thus, for costeffective marketing, it is desirable for companies to distribute marketing coupons to new customers that have the highest potential to make future purchases without continued coupon incentives. However, it is difficult for ecommerce companies to identify the new customers to be targeted within 30 hours of registration. In this study, we address this problem using uplift modeling for costeffective marketing. Uplift modeling can be used to identify the time when there is a causal relationship between coupon distribution and future noncoupon purchases. The ability to identify these causal relationships can allow a company to distribute coupons to the most promising customers and improve its business. Several studies have explained the benefits of uplift modeling in realworld ecommerce businesses. In this study, we demonstrate the results of uplift modeling for coupon distribution in a realworld CustomertoCustomer (CtoC) ecommerce platform. We show that uplift modeling decreases marketing costs by 39.0% with only a negligible reduction in the number of acquired customers who make noncoupon purchases. However, it is difficult for Ecommerce companies to identify the new customers to be targeted within 30 hours of registration. In this study, we address this problem using uplift modeling for costeffective marketing. Uplift modeling can be used to identify the time when there is a causal relationship between coupon distribution and future noncoupon purchases. The ability to identify these causal relationships can allow a company to distribute coupons to the most promising customers and improve its business. Several studies have explained the benefits of uplift modeling in realworld Ecommerce businesses. In this study, we demonstrate the results of uplift modeling for coupon distribution in a realworld CustomertoCustomer (CtoC) Ecommerce platform. We show that uplift modeling decreases marketing costs by 38.6% with only a negligible reduction in the number of acquired customers who make noncoupon purchases.

Uplift Modeling for Multiple Treatments with Cost Optimization Uplift modeling is an emerging machine learning approach for estimating the treatment effect at an individual or subgroup level. It can be used for optimizing the performance of interventions such as marketing campaigns and product designs. Uplift modeling can be used to estimate which users are likely to benefit from a treatment and then prioritize delivering or promoting the preferred experience to those users. An important but so far neglected use case for uplift modeling is an experiment with multiple treatment groups that have different costs, such as for example when different communication channels and promotion types are tested simultaneously. In this paper, we extend standard uplift models to support multiple treatment groups with different costs. We evaluate the performance of the proposed models using both synthetic and real data. We also describe a production implementation of the approach.