Loyal Consumers or OneTime Deal Hunters: Repeat Buyer Prediction for ECommerce Merchants sometimes run big promotions (e.g., discounts or cash coupons) on particular dates (e.g., Boxingday Sales, Black Friday or Double 11 (Nov 11th), in order to attract a large number of new buyers. Unfortunately, many of the attracted buyers are onetime deal hunters, and these promotions may have little long lasting impact on sales. To alleviate this problem, it is important for merchants to identify who can be converted into repeated buyers. By targeting on these potential loyal customers, merchants can greatly reduce the promotion cost and enhance the return on investment (ROI). It is well known that in the field of online advertising, customer targeting is extremely challenging, especially for fresh buyers. With the longterm user behavior log accumulated by Tmall.com, we get a set of merchants and their corresponding new buyers acquired during the promotion on the Double 11 day. Our goal is to predict which new buyers for given merchants will become loyal customers in the future. In other words, we need to predict the probability that these new buyers would purchase items from the same merchants again within 6 months. A data set containing around 200k users is given for training, while the other of similar size for testing. We extracted as many features as possible and find the key features to train our models. We proposed merged model of different classification models and merged lightGBM model with different parameter sets. The experimental results show that our merged models can bring about great performance improvements comparing with the original models.

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.