Pradeep K. Chintagunta

The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets

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. By conducting our empirical analysis at the DMA level and accounting for prerelease advertising, we can classify DMAs based on their responsiveness to firm-initiated marketing effort (advertising) and consumer-generated marketing (online word of mouth). A unique feature of our study is that it allows marketing managers to assess a DMAs responsiveness along these two dimensions. The substantive insights can help studios and distributors evaluate their future product rollout strategies. Although our empirical analysis is conducted using motion picture industry data, our approach to addressing the endogeneity of reviews is generalizable to other industry settings where products are sequentially rolled out.

Response Modeling with Nonrandom Marketing-Mix Variables

Sales response models are widely used as the basis for optimizing the marketing mix. Response models condition on the observed marketing-mix variables and focus on the specification of the distribution of observed sales given marketing-mix activities. The models usually fail to recognize that the levels of the marketing-mix variables are often chosen with at least partial knowledge of the response parameters in the conditional model. This means that contrary to standard assumptions, the marginal distribution of the marketing-mix variables is not independent of response parameters. The authors expand on the standard conditional model to include a model for the determination of the marketing-mix variables. They apply this modeling approach to the problem of gauging the effectiveness of sales calls (details) to induce greater prescribing of drugs by individual physicians. They do not assume a priori that details are set optimally, but instead they infer the extent to which sales force managers have knowledge of responsiveness, and they use this knowledge to set the level of sales force contact. The authors find that their modeling approach improves the precision of the physician-specific response parameters significantly. They also find that physicians are not detailed optimally; high-volume physicians are detailed to a greater extent than low-volume physicians without regard to responsiveness to detailing. It appears that unresponsive but high-volume physicians are detailed the most. Finally, the authors illustrate how their approach provides a general framework.

A Random-Coefficients Logit Brand-Choice Model Applied to Panel Data

A random-coefficients logit model that allows for unobserved heterogeneity in brand preferences and in the responses to marketing variables is empirically investigated using household-level panel data. The unknown underlying distribution of unobserved heterogeneity is approximated by a discrete distribution. The results reveal that there is significant unobserved heterogeneity across households and that ignoring its effects results in a downward bias in the parameter estimates of the marketing variables. It is therefore important to account for heterogeneity in both preferences and responses in the absence of any a priori knowledge about the nature of heterogeneity across households.

Investigating Category Pricing Behavior at a Retail Chain

In studying retailer pricing behavior, researchers typically assume that retailers maximize profits across all brands in a focal product category. In this article, the author attempts to study empirically the extent to which three factors affect retail prices: (1) the effects of payments from manufacturers to the retailer other than regular promotions, as well as the effects of additional costs borne by the retailer for these brands; (2) the retailers objectives specific to its store brand, such as maximizing store brand share; and (3) the effects of retail competition and store traffic. By specifying a demand function at the brand-chain level for each brand in the product category, the author derives pricing rules for the retailer. The author decomposes the retail price of a brand into effects due to wholesale price, markup (obtained from the demand functions), additional promotional payments, retail competition, and the retailers objectives for the store brand. The author carries out empirical analysis for a specific product category at a single retail grocery chain. The results indicate that the effects of the three factors vary across brands in the category.

How Does Assortment Affect Grocery Store Choice

The authors investigate the impact of product assortments, along with convenience, prices, and feature advertising, on consumers’ grocery store choice decisions. Extending recent research on store choice, they add assortments as a predictor, specify a general structure for heterogeneity, and estimate store choice and category needs models simultaneously. Using household-level market basket data, the authors find that, in general, assortments are more important than retail prices in store choice decisions. They find that the number of brands offered in retail assortments has a positive effect on store choice for most households, while the number of stockkeeping units per brand, sizes per brand, and proportion of stockkeeping units that are unique to the store (a proxy for presence of private labels) have a negative effect on store choice for most households. They also find more heterogeneity in response to assortment than to either convenience or price. Therefore, optimal assortments depend on the particular preferences of a retailers shoppers. Finally, the authors find a correlation in household-level responses to assortment and travel distance (r = .43), which suggests that the less important an assortment is to a consumers store choices, the more the consumer values convenience, and vice versa.

Investigating the Effects of Store-Brand Introduction on Retailer Demand and Pricing Behavior

Researchers have recently been interested in studying the drivers of store-brand success as well as factors that motivate retailers to introduce store brands. In this paper, we study the effects of the introduction of a store-brand into a particular product category. Specifically, we are interested in the effect of store-brand introduction on the demand as well as on the supply side. On the demand side, we investigate the changes in preferences for the national brands and price elasticities in the category. On the supply side, we study the effects of the new entrant on the interactions between the national brand manufacturers and the retailer introducing the store brand, including how these interactions influence the retailers pricing behavior. In doing so, we are also able to test whether the observed data are consistent with some of the commonly used assumptions regarding retailer pricing behavior. For the demand specification we use a random coefficients logit model that allows for consumer heterogeneity. The model parameters are estimated using aggregate data while explicitly accounting for endogeneity in retail prices.Our empirical results obtained from the oats product category based on store-level data from a multistore retail chain indicate that the store-brand introduction generates notable changes within the category. The store-brand introduction coincides with an increase in the retailers margins for the national brand. We find that the preferences for the national brand are relatively unaffected by the introduction of the store-brand. While consumers are, in general, more price sensitive (in terms of elasticities) than they were prior to store-brand introduction, a statistical test of the differences inmean price elasticities across stores and between the two regimes fails to reject the hypothesis of no change in these elasticities. Elasticities in specific stores however, do increase after the store brand is introduced. We also find that there is considerable heterogeneity in the preferences for the store-brand. On the supply side, we test several forms of manufacturer-retailer interactions to identify retailer pricing behavior most consistent with the data. Our results indicate that the data reject several, commonly imposed, forms of interactions. In examining the nature of manufacturer interactions with the retailer, we find that the manufacturer of the national brand appears to take a softer stance in its interactions with the retailer subsequent to store-brand entry. This finding is consistent with academic research and with articles in the popular press which suggest that the store brand enhances the retailers bargaining ability vis-A -vis the manufacturers of the national brands. We also provide results from a second product category frozen pasta) that are largely consistent with those found in the oats category.

Tipping and Concentration in Markets with Indirect Network Effects

This paper develops a framework for measuring “tipping”---the increase in a firms market share dominance caused by indirect network effects. Our measure compares the expected concentration in a market to the hypothetical expected concentration that would arise in the absence of indirect network effects. In practice, this measure requires a model that can predict the counterfactual market concentration under different parameter values capturing the strength of indirect network effects. We build such a model for the case of dynamic standards competition in a market characterized by the classic hardware/software paradigm. To demonstrate its applicability, we calibrate it using demand estimates and other data from the 32/64-bit generation of video game consoles, a canonical example of standards competition with indirect network effects. In our example, we find that indirect network effects can lead to a strong, economically significant increase in market concentration. We also find important roles for beliefs on both the demand side, as consumers tend to pick the product they expect to win the standards war, and on the supply side, as firms engage in penetration pricing to invest in growing their networks.

Balancing Profitability and Customer Welfare in a Supermarket Chain

We investigate the impact of price discrimination by a large Chicago supermarket chain. First we measure the impact of the chains current zone-pricing policy on shelf prices, variable profits and consumer welfare across its stores. Using the chains database to simulate a finer store-specific micro-pricing policy, we study the implications of this policy on profits and welfare. We show how a store-pricing policy that is constrained to offer consumers at least as much surplus as a uniform chain wide pricing policy still enables the retailer to generate substantial incremental profits.To ensure our pricing problem exhibits a well-defined optimum, we use the parsimonious, mixed-logit demand function that allows for flexible substitution patterns across brands and also retains a link to consumer theory. We discuss the issue of price endogeneity when estimating the demand parameters with weekly store-level data. Standard instrumental variables techniques used to account for such endogeneity also seem to increase the magnitudes of own-price elasticities thereby offsetting the problem encountered by previous researchers of predicted prices from a demand model exceeding those in the actual data.

Beyond the Endogeneity Bias: The Effect of Unmeasured Brand Characteristics on Household-Level Brand Choice Models

We investigate the role of potential weekly brand-specific characteristics that influence consumer choices, but are unobserved or unmeasurable by the researcher. We use an empirical approach, based on the estimation methods used for standard random coefficients logit models, to account for the presence of such unobserved attributes. Using household scanner panel data, we find evidence that ignoring such time-varying latent (to the researcher) characteristics can lead to two types of problems. First, consistent with previous literature, we find that these unobserved characteristics may lead to biased estimates of the mean price response parameters. This argument is based on a form of price endogeneity. If marketing managers set prices based on consumer willingness to pay, then the observed prices will likely be correlated with the latent (to the researcher) brand characteristics. We resolve this problem by using an instrumental variables procedure. Our findings suggest that simply ignoring these attributes may also lead to larger estimates of the variance in the heterogeneity distribution of preferences and price sensitivities across households. This could overstate the benefits from marketing activities such as household-level targeting. We resolve the problem by using weekly brand intercepts, embedded in a random coefficients brand choice model, to control for weekly brand-specific characteristics, while accounting for household heterogeneity. Overall, our results extend the finding on the endogeneity bias from the mean of the heterogeneity distribution (i.e., the price effect) to include the variance of that distribution.

A discrete-continuous model for multicategory purchase behavior of households

This article provides an integrated utility-maximizing framework for households’ multicategory purchase incidence, brand choice, and purchase quantity decisions. On a store visit, households maximize utility across several categories subject to an overall budget constraint. Then, they choose to purchase a subset of categories, in which they select a specific brand and an associated quantity level. Under their assumptions for the stochastic terms in the specification, the authors obtain closed-form expressions for the household demands, the marginal and joint purchase incidence, and the brand choice probabilities along with conditional purchase quantities. The authors show that the modeling framework accounts for both coincidence and complementarity/ substitutability across categories. In addition, it accounts for unobserved heterogeneity in the estimation. Thus, the three key features of multicategory models proposed in the literature are all reflected in the framework. The authors provide an empirical application of the methodology using four categories from the Stanford market basket data—laundry detergent, fabric softener, toilette tissue, and paper towel. The results indicate that, in general, cross-category effects, as measured by the cross-category price elasticities between brands in the categories, are small, and they arise mostly through consumers’ joint purchase incidence decisions rather than at the purchase quantity decision conditional on brand choice. This finding indicates that the effects of coincidence are larger than the effects of complementarity in these data.