Shyam Gopinath is an Assistant Professor in the department of Marketing in the Kelley School of Business, Indiana University. His areas of expertise include online word of mouth, customer relationship management, entertainment industry, econometric models of consumer dynamics.
Industry Expertise (1)
Areas of Expertise (4)
Econometric Modesl of Consumer Dynamics
Customer Relationship Management
Online Word-of-Mouth Marketing
Northwestern University,: Ph.D., Marketing 2011
University of Virginia: M.S., Statistics 2005
IIT Madras, India: M.Tech, Industrial Management 2002
University of Kerala: B.Tech, Industrial Engineering 1999
Media Appearances (1)
'Top-ranked' reviewers aren't the top influencers when it comes to online sales: study
PHYS Org online
Shyam Gopinath, assistant professor of marketing at Kelley, and two co-authors of a paper accepted for publication by Marketing Science found that the influence of top-rated reviewers was limited to instances such as brand-new products or products with a high variance in existing reviews...
2018 The main objective in this paper is to study the effect of reviews by top- and bottom-ranked reviewers on product sales. We use designated market area sales data for 182 new music albums released over an approximately three-month period along with user review data from Amazon.com. Our estimation accounts for confounding factors in the effects of online word-of-mouth measures via the use of instrumental variables. There are several key insights. Overall, we find that bottom-ranked reviewers have a greater effect on sales than top-ranked reviewers. Top-ranked reviewers can be opinion leaders, but their influence is largely limited to special cases like very new products or products with high variance in existing reviews. Additional analysis reveals that the differences in the influence of top- and bottom-ranked reviewers is driven by both what they write (content) and who they are (identity). The results are robust across multiple product categories (music and cameras) and multiple dependent variables (sales and sales rank).
2016 Customers come and go, and some return. Is their behavior the same, better or worse when they return? To answer this we first define revived customers in a non-contractual setting. Next, we compare the purchasing behavior of revived customers before and after the period of inactivity. The Customer Lifetime Value (CLV) models in the targeting literature make the implicit assumption that the buying behavior of revived customers is the same as the buying behavior of active customers (Equality Assumption). The third objective of our study is to investigate whether this assumption holds or not. For our analyses, we use an extended Pareto/NBD model. We have three main findings from our study. First, we find that revived customers exist in non-contractual settings. Second, we show that higher past purchase frequency (strongly habitual) customers have greater consistency in purchasing behavior than lower past purchase frequency (weakly habitual) customers. The final key finding is that the equality assumption is not valid for, i.e., revived customers have different purchasing behavior than active customers.
2014 We study the relative importance of online word of mouth and advertising on firm performance over time since product introduction. The current research separates the volume of consumer-generated online word of mouth (OWOM) from its valence, which has three dimensions—attribute, emotion, and recommendation oriented. Firm-initiated advertising content is also classified as attribute or emotion advertising. We also shed light on the role played by advertising content on generating the different types of OWOM conversations. We use a dynamic hierarchical linear model (DHLM) for our analysis. The proposed model is compared with a dynamic linear model, vector autoregressive/system of equations model, and a generalized Bass model.
2013 We measure the effects of pre- and postrelease blog volume, blog valence, and advertising on the performance of 75 movies in 208 geographic markets in the United States. We attribute the variation in blog effects across markets to differences in demographic characteristics of markets combined with differences across demographic groups in their access and exposure to blogs as well as their responsiveness conditional on access. We study the effects of prerelease factors on opening day box office performance and of pre- and postrelease factors on box office performance one month after release. Our estimation accounts for confounding factors in the measurement of these effects via the use of instrumental variables. We find considerable heterogeneity in the effects across consumer- and firm-generated media and across geographic markets, with gender, income, race, and age driving across-market differences. Release day performance is impacted most by prerelease blog volume and advertising, whereas postrelease performance is influenced by postrelease blog valence and advertising. Across markets, there is more variance in advertising and blog valence (postrelease) elasticities than there is in blog volume (prerelease) elasticities. We identify the top 20 markets in terms of their elasticities to each of these three instruments. Further, we classify markets in terms of their sensitivities across these three instruments to identify the most sensitive markets that studios can target with their limited release strategies. Finally, we characterize the extent to which studios could have improved their limited release strategies by identifying the overlap between the actual release markets and the most responsive ones. We find that at the time of first-release studios cover only 53% of the most responsive advertising markets and 44% of the most responsive markets to prerelease blog volume in their limited release strategies, implying considerable room for improvement if these were the only metrics to assess those strategies.
2010 Our objective in this paper is to measure the impact (valence, volume, and variance) of national online user reviews on designated market area (DMA)-level local geographic box office performance of movies. We account for three complications with analyses that use national-level aggregate box office data: (i) aggregation across heterogeneous markets (spatial aggregation), (ii) serial correlation as a result of sequential release of movies (endogenous rollout), and (iii) serial correlation as a result of other unobserved components that could affect inferences regarding the impact of user reviews. We use daily box office ticket sales data for 148 movies released in the United States during a 16-month period (out of the 874 movies released) along with user review data from the Yahoo! Movies website. The analysis also controls for other possible box office drivers. Our identification strategy rests on our ability to identify plausible instruments for user ratings by exploiting the sequential release of movies across markets—because user reviews can only come from markets where the movie has previously been released, exogenous variables from previous markets would be appropriate instruments in subsequent markets. In contrast with previous studies that have found that the main driver of box office performance is the volume of reviews, we find that it is the valence that seems to matter and not the volume. Furthermore, ignoring the endogenous rollout decision does not seem to have a big impact on the results from our DMA-level analysis. When we carry out our analysis with aggregated national data, we obtain the same results as those from previous studies, i.e., that volume matters but not the valence. Using various market-level controls in the national data model, we attempt to identify the source of this difference.