How can Predictive Analytics help E-Retailers?

Abstract

The rapid emergence of e-commerce sites has made the web-space an exciting and interactive business platform for producers, marketers and consumers. In their quest for increasing their share of customers’ wallets, online retailers are battling aggressively by offering better customer experiences through personalized recommendations, customized promotional offers, etc. And to enhance the customer experience and stay ahead of competition, online retailers are adopting advanced technologies and capturing information on their customers’ online activity on their site by various means resulting in a marked increase in the volume of data being collected. They are eager to understand how this big data can be used to increase customer’s click rate and to help prevent customer loss. The analysis of this huge volume of data presents a lot of opportunities as well as challenges. In this paper we discuss our experiences in working with this data for a Korean online retailer. We present our approaches/potential solutions on the following three topics which have helped the client enhance the customer experience. 1. Improving the performance of their recommendation engine which resulted in 62% increase in click-through rate. 2. Develop customer shopping pattern engine to score customers in each of the 34 client identified shopping patterns. These scores will in turn be used by the client for selective promotions through E-mail/SMS. 3. Identifying if context like time, weekday/weekend, weather, location, etc., affect the browsing behaviors of customers.

We are going to propose ways E-Retailers can make use of the big data they are collecting.