Measuring the Value of Social Dynamics in Online Product Ratings Forums
“The common underlying assumption of studies which investigate the impact of consumer reviews on product sales is that posted product ratings reflect the customers’ experience with the product, independent from the ratings of others. However, researchers have shown that posted product ratings are subject to a number of influences unrelated to a consumer’s objective assessment of the product. For example, Schlosser (2005) showed that posted product ratings are influenced by social dynamics. Specifically, the rating an individual posts for a product is affected by previously posted ratings. Additionally, Godes and Silva (2009) demonstrate ratings dynamics that result in a negative trend in posted product ratings as the volume of postings.
The consequence of the ratings dynamics described above is that user-provided product ratings do not always accurately reflect product performance, yet they still have the potential to significantly influence product sales. This can be quite disconcerting for product marketers, and as a result, many marketers are investing in activities intended to create a more favorable ratings environment for their products with the intention of boosting sales (Dellarocas 2006).
Our objective in this paper is to measure the value of the social dynamics found in online customer rating environments. We do this by explicitly modeling the arrival of posted product ratings and separate the effects of social dynamics on ratings from the underlying baseline ratings behavior (which we argue reflects the consumers’ “socially unbiased”1 product evaluations). In many ratings forums, consumers evaluate products along a five-star scale. Therefore, we capture the ratings process by modeling the arrival of ratings within each star level as five separate (but interrelated) hazard processes. This allows us to capture the timing and the valence of posted ratings simultaneously. Additionally, we include time-varying hazard covariates to capture the effect of social influence on ratings behavior. The resulting model estimates allow us to compute a set of ratings metrics that represent the expected ratings behavior both with and without the effect of social dynamics. We then decompose observed ratings into a baseline ratings component, a component representing the impact of social dynamics and an idiosyncratic error component. To capture the sale impact of social dynamics, we model sales as a function of these component ratings metrics.
Our model results show that there are substantial social dynamics in the ratings environment. We further study these dynamics by examining both their direct and indirect effects on product sales. Specifically, we show that dynamics observed in ratings valence (or average rating) can have direct and immediate effects on sales. We also show that these dynamics can have additional indirect effects on future sales through its influence on future ratings. These indirect effects can mitigate the long-term impact of ratings dynamics on sales. This is particularly true in cases where the level of opinion variance is increased.”