Using online user-generated reviews to predict offline box-office sales and online DVD store sales in the O2O era

Abstract

With the rapid growth of e-commerce and social media, customers post online reviews on various online shopping websites and social media after their consumption experience, which generated the electronic word of mouth effect. In the online-to-offline era, companies are using multi-channels to increase customer demand, and the Effect generated by online users’ reviews plays an important role in customer demand both online and offline. Few previous studies focused on using online user-generated reviews to forecast the demand of hyper-differentiated products online and offline, which is particularly hard to predict due to the various preferences of customers and complex relationship between factors. Via predictive global sensitivity analysis, this study uses online user-generated reviews posted on social media to predict customers’ demand for hyper-differentiated products both online and offline, with an example in the film industry. We generate forecasting equations, which can successfully predict movie box office and online DVD store sales. The effectiveness and reliability of our approach are proved by the numerical studies of 22 randomly selected movies. Managers can use our method to access and analyze online users’ review data and forecast future online and offline product sales.