User-Generated Content on Social Media : Predicting Market Success with Online Word-of-Mouth

Enabled by Web 2.0 technologies, online social media in the forms of discussion forums, message boards, and blogs has become a prevalent channe lof communication for consumers and businesses. Online social media allows consumers to share their product opinions and experience at an unprecedented pace and scale. This user generated content, or online word of mouth (WOM), has the potential to influence product sales and firm strategy. Consequently, as Web-mining and opinion-mining tools and technology continue to proliferate, it is critical to examine how WOM information can be measured and used to improve managerial decisions. In this article, we explore the predictive validity of various text and sentiment measures of online WOM for the market success of new products. From the firms’ perspective, it is important to effectively predict the sales of new products in the product development process. The earlier such a forecast can be made, the more useful it will be, since marketing strategies can then be adjusted accordingly. We thus examine online WOM that appears at different stages of the new-product life cycle, such as before production, before introduction, and after introduction. New-product development is a highly risky process, and it is useful to examine different aspects of its success. In addition to examining product sales directly, we also study product evaluation by third-party professionals and how the product would receive marketing support from the firm, both of which could influence sales. The context of our study is the Hollywood movie industry. The forecast of movie sales is highly challenging and has started to incorporate online WOM. We collected online WOM information from the message board of Yahoo Movies for a total of 257 movies released from 2005 to 2006. We used Senti Word Net and Opinion Finder, two lexical packages of computational linguistics, to construct the sentiment measures for the WOM data. We will first examine the evolution patterns of online WOM over time, followed by a correlation analysis of how various sentiment measures relate to the metrics of new product success