Managing Customers' Satisfaction: Evidence from Mining User Generated Content on the Internet

Abstract

Establishing the relation between online ratings and reviews provides a potentially inexpensive and effective way for karaoke managers to capture satisfaction and quality improvement information from customers. To this end, this study proposes an integrated approach that leverages text mining and empirical modeling to quantitatively correlate user generated content on the internet. From Dianping.com (a Chinese crowd-sourced online review community), 47,069 pairs of karaoke rating and review were examined, with major topics identified. Subsequently, multilinear regression was employed to screen out the most impactful factors that influence overall, environment, sound effect, and service ratings. Managerially, the idea of triggering the synergistic benefit from customer ratings and reviews is referential for market practitioners both within and beyond the karaoke industry.