How Does Queueing Information in Pre-Sales Call Centers Affect Customer Repurchase Behavior

Abstract

With the growth of mobile e-business, it has become common for customers to experience an explosion of instant desire to consume in a short period of time. Companies are harnessing this momentum, hoping to better capture, retain and convert these consumer desires into actual sales orders. Consequently, companies also need to use pre-sales call centers to provide the satisfied service and motivate more customers to repurchase, thereby increasing the revenue. The function of the call centers is updated from providing post-sales service to providing pre-sales consultation. In this regard, this paper examines the impact of delayed announcements consisting of queue information on customer repurchase behavior in pre-sales call centers. We classify the queuing information into no-information, part-information and full-information according to the level of information by constructing a simulation model. In the simulation experiments, some application scenarios are set up to describe the load of the call center. In all the given scenarios, we find out the full-information, which provides the delay time, is always the best. Moreover, the different methods for estimating the delay time to provide the full-information, namely LES, EA, EA2 and WA–LES, are compared. We find optimal announcements for the application scenarios set out in the time-varying scenario as well. Consequently, considering there exists certain cost for the company to make WA–LES announcements, while some call centers can only provide part-information instead, this paper investigates the impact of estimate bias on customer repurchase behavior and company revenue. Subsequently, the company revenue is further investigated in two extended experiments, where repurchase influence power and repurchase number are limited. The analysis ideas and simulation logic of this paper have good reference significance for e-commerce pre-sales call centers, and the suggestions for setting information levels in practice while considering customer repurchase behavior are also clearly given in the conclusion of the paper.