Abstract
E-coupons (electronic coupons) have been a mainstay of online marketing to attract consumers and promote them to repeat purchase, distributing right e-coupons to right consumers is of critical importance. In big data era, analyzing consumers preferences for e-coupons by their online behavior and the impact of data imbalance caused by low active consumers are rarely studied. Thus, we propose a two-stage hybrid model. Firstly, consumer segmentation is implemented to analyze behavioral characteristics for each segment and distinguish low active consumers, then models are constructed for different consumer segments. The proposed model is applied to a real online consumption data. Consumers are aggregated into four segments: potential e-coupons user, low discount sensitive user, high discount sensitive user (including discount preference and fixed preference). The first one is defined as low active consumer segment and others are high active consumer segments. Isolation forest model and logistic regression model are respectively constructed for them. Result shows that data imbalance is effectively relieved, prediction performance is also significantly better than the traditional approaches. Finally, e-coupons’ usage characteristics for each consumer segment are summarized, according to that, companies can increase sales and improve consumer satisfaction as well.