The aim of this research is to theorize and demonstrate that analyzing consumers’ text product reviews using text mining can enhance the explanatory power of a product sales model, particularly for hedonic products, which tend to generate emotional and subjective product evaluations. Previous research in this area has been more focused on utilitarian products.
Our text clustering-based procedure segments text reviews into multiple clusters in association with consumers’ numeric ratings to address consumer heterogeneity in taste preferences and quality valuations and the J-distribution of numeric product ratings. This approach is novel in terms of combining text clustering with numeric product ratings to address consumers’ subjective product evaluations.
Using the movie industry as our empirical application, we find that our approach of making use of product text reviews can improve the explanatory power and predictive validity of the box-office sales model.
Marketing scholars have actively investigated the impact of consumers’ online product reviews on product sales, primarily focusing on consumers’ numeric product ratings. Recently, studies have also examined user-generated content. Similarly, this study looks into users’ textual product reviews to explain product sales. It remains to be seen how generalizable our empirical results are beyond our movie application.
Whereas numeric ratings can indicate how much viewers liked products, consumers’ reviews can convey why viewers liked or disliked them. Therefore, our review analysis can help marketers understand what factors make new products succeed or fail.
Primarily our approach is suitable to products subjectively evaluated, mostly, hedonic products. In doing so, we consider consumer heterogeneity contained in reviews through our review clusters based on their divergent impacts on sales.
Emerald Group Publishing Limited
Copyright © 2014, Emerald Group Publishing Limited