Sleeping customer detection using support vector machine

Abstract

Customers are difficult to find and sometimes even more difficult to keep. If a company doesn’t pay attention to customer relationship management, it will spend lots of money to acquire them and then let them sleep. The goal of this study was to develop a sleeping customer detection system that collected users’ social network information and shopping behavior, and then classified them into 3 categories (sleeping customer, napping customer, and general customer). We collected the user information from January 01, 2015 to December 31, 2016. Support vector machine based classification was used. In this study, the overall accuracy was 81.7%. The results can help companies to reactivate sleeping customers as soon as possible.