Exploring the value of online product reviews in forecasting sales: The case of motion pictures

Abstract

The growing popularity of online product review forums invites the development of models and metrics that allow firms to harness these new sources of information for decision support. Our work contributes in this direction by proposing a novel family of diffusion models that capture some of the unique aspects of the entertainment industry and testing their performance in the context of very early postrelease motion picture revenue forecasting. We show that the addition of online product review metrics to a benchmark model that includes prerelease marketing, theater availability and professional critic reviews substantially increases its forecasting accuracy; the forecasting accuracy of our best model outperforms that of several previously published models. In addition to its contributions in diffusion theory, our study reconciles some inconsistencies among previous studies with respect to what online review metrics are statistically significant in forecasting entertainment good sales.

Dynamic Pricing in the Presence of Social Learning and Strategic Consumers

“…This case reflects situations in which SL is either (i) irrelevant, because there is no ex ante quality uncertainty (i.e., σ p → 0) and therefore nothing to be learned from product reviews, 4 Since the firm and consumers hold the same prior belief, firm actions in our model cannot convey any additional information on product quality to the consumers (i.e., there is no scope for signalling); this informational structure is commonly assumed in the SL literature to focus attention on the peer-to-peer learning process (e.g., Bergemann and Välimäki 1997, Bose et al 2006, Bose et al 2008, YU et al 2013b). Furthermore, although we do not model expert/critic reviews explicitly, these may take part in forming the public prior belief; Dellarocas et al (2007) find that there is generally little overlap between the informational content of critic reviews and that of consumer reviews.…”

“…For the consumers, learning from reviews allows for better-informed purchasing decisions, which in turn reduces the likelihood of ex post negative experiences. For the firm, the SL process can also be beneficial, for instance, by allowing for increased accuracy in forecasting future demand (e.g., Dellarocas et al 2007). However, the ease with which the modern-day consumer can gain access to buyer reviews also gives rise to a new dimension of strategic consumer behavior: rather than experimenting with a new product themselves, consumers may be enticed to delay their purchasing decisions in anticipation of peer reviews.…”

What makes a useful online review? Implication for travel product websites

“…Chevalier and Mayzlin (2006) found a positive relationship between consumer reviews on retailing websites and book sales (e.g., Barnes & Nobel and Amazon.com). They also identified that the valence (average numerical rating) (Dellarocas, Zhang & Awad, 2007) and number of online consumer reviews (Duan et al, 2008) are vital predictors of box office sales. Clemons, Gao, and Hitt (2006) showed that not only the variance of ratings but also the strength of the most positive quartile of reviews has a significant impact on the growth of craft beers.…”

The Customer is Always Right: Sentiment Analysis for Bank Service Quality

“…Due to many challenging research problems and a wide variety of practical applications, it has been a very active research area in recent years. In fact, it has spread from computer science to management science [e.g., 2,11,17,32,37,58,74]. This chapter first presented an abstract model of sentiment analysis, which formulates the problem and provides a common framework to unify different research directions.…”