No abstract

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 DMA's 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.online word of mouth, sequential new product release, endogeneity, instrumental variables, generalized method of moments, motion pictures

Households incur transaction costs when choosing among off-line stores for grocery purchases. They may incur additional transaction costs when buying groceries online versus off-line. We integrate the various transaction costs into a channel choice framework and empirically quantify the relative transaction costs when households choose between the online and off-line channels of the same grocery chain. The key challenges in quantifying these costs are (i) the complexity of channel choice decision and (ii) that several of the costs depend on the items a household expects to buy in the store, and unobserved factors that influence channel choice also likely influence the items purchased. We use the unique features of our empirical context to address the first issue and the plausibly exogenous approach in a hierarchical Bayesian framework to account for the endogeneity of the channel choice drivers. We find that transaction costs for grocery shopping can be sizable and play an important role in the choice between online and off-line channels. We provide monetary metrics for several types of transaction costs, such as travel time and transportation costs, in-store shopping time, item-picking costs, basket-carrying costs, quality inspection costs, and inconvenience costs. We find considerable household heterogeneity in these costs and characterize their distributions. We discuss the implications of our findings for the retailer's channel strategy.

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 retailer's 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 consumer's store choices, the more the consumer values convenience, and vice versa.

We develop a comprehensive utility maximizing framework to study the impact of marketing variables on the category purchase, brand choice and purchase quantity decisions of households for frequently purchased packaged goods. The model allows for dependence among the three decisions while ensuring that these decisions provide, in combination, the greatest possible utility to the household. By accounting for variations in reservation prices and intrinsic brand preferences across households, the modeling framework explicitly captures the effects of unobserved heterogeneity on all three purchase decisions. The principal empirical finding from analyzing the A. C. Nielsen data for the yogurt product category is that the substantive implications for the effects of marketing variables are sensitive to whether these effects are determined conditional or unconditional on a product category purchase. Our results show that reservation prices and intrinsic brand preferences vary across households, and not accounting for these variations in the estimation could lead to biased estimates for the coefficients of the marketing variables. A comparison of our results to those obtained from a nested logit model of purchase incidence and brand choice reveals that our proposed model performs better using both a formal statistical test as well as the criterion of predictive validity in a holdout sample of panelists. Further, the purchase quantity model compares favorably with two alternative models of quantity choice in the validation sample.buyer behavior, econometric models, choice models