A Micromodel of New Product Adoption with Heterogeneous and Forward-Looking Consumers: Application to the Digital Camera Category

We develop an empirical model for the adoption process of a new durable product that accounts for consumer heterogeneity as well as consumers forward-looking behavior. Accounting for heterogeneity is important for two reasons. As the mix of consumers with different preferences and price sensitivities could change over time, firms need to update their marketing strategies. Further, it allows for a variety of shapes for the aggregate adoption process over time. As prices for durable and technology products fall over time with firms continually introducing enhanced products, consumers may anticipate these prices and improvements and delay their purchases in the product category. Forward-looking consumers optimize purchase timing by trading off their utilities from buying the product and their expectations on future prices, quality levels, and brand availability. Such forward-looking behavior will result in price dynamics in the marketplace as price changes today influence future purchases. And it results in different shapes of the new product sales pattern over time by influencing the time to take-off. We show how the parameters of our model can be estimated using aggregate data on the sales, prices, and attributes of brands in a product category. We apply our model to market data from the digital camera category. Our data are consistent with the presence of both heterogeneity and forward looking behavior among consumers. At the product category level, we are able to decompose the effects of the entry of Sony into primary demand expansion and switching from other brands. At the brand level, we find that there exist several segments in the market with different preferences for the brands and different price sensitivities leading to differences in adoption timing and brand choice across segments. For a given brand, we show how the changing customer mix over time has implications for that brands pricing strategies. We characterize how price effects vary across brands and over time and how price changes in a given time period influence sales in subsequent periods. Model comparison and validation results are also provided.     

Structural models of complementary choices

This article reviews the rapidly growing literature on structural models of complementary choices. It discusses recent modeling developments and identifies promising areas for future research.     

Empirical Analysis of Indirect Network Effects in the Market for Personal Digital Assistants

We present a framework to measure empirically the size of indirect network effects in high-technology markets with competing incompatible technology standards. These indirect network effects arise due to inter-dependence in demand for hardware and compatible software. By modeling the joint determination of hardware sales and software availability in the market, we are able to describe the nature of demand inter-dependence and to measure the size of the indirect network effects. We apply the model to price and sales data from the industry for personal digital assistants (PDAs) along with the availability of software titles compatible with each PDA hardware standard. Our empirical results indicate significant indirect network effects. By July 2002, the network effect explains roughly 22% of the log-odds ratio of the sales of all Palm O/S compatible PDA-s to Microsoft O/S compatible PDA-s, where the remaining 78% reflects price and model features. We also use our model estimates to study the growth of the installed bases of Palm and Microsoft PDA hardware, with and without the availability of compatible third party software. We find that lack of third party software negatively impacts the evolution of the installed hardware bases of both formats. These results suggest PDA hardware firms would benefit from investing resources in increasing the provision of software for their products. We then compare the benefits of investments in software with investments in the quality of hardware technology. This exercise helps disentangle the potential for incremental hardware sales due to hardware quality improvement from that of positive feedback due to market software provision.     

A pre-diffusion growth model of intentions and purchase

In this paper, we investigate whether information on the history of purchase intentions is useful in predicting actual purchase behavior. The research is motivated by two factors. The first factor is the empirical finding in the literature that measuring intentions just prior to purchase provides better predictions of actual purchase as compared to when these intentions are measured earlier. The second factor is the role of the timing of the formation of intentions prior to purchase. While one stream of literature based on preference fluency predicts that early formation of intentions is more likely to lead to actual purchase, the other stream based on the memory-based “recency” effect predicts that formation of intentions just prior to purchase is more likely to lead to actual purchase. Together, these two factors motivate the potential need to account for the entire history of intentions prior to purchase. A canonical example of a market where intention histories are tracked is the movie industry, where “first choice” movie watching intentions are tracked up to (and in some cases beyond) the time of release. Accommodating the history of intentions in an econometric model that predicts actual box office performance is challenging due to the differing numbers of observations for the movies, the large numbers of observations for certain movies, as well as the role of various time-invariant and time-varying covariates influencing intentions. We propose a two-part model where the first part involves a hierarchical growth model that summarizes the trajectories of intentions via “growth factors.” These growth factors also reflect the role of the various covariates. The second part is a regression of the box office performance on the growth factors and other covariates. The models are simultaneously estimated within a Bayesian framework. Consistent with the previous literature, we find that including information on intentions improves our ability to predict behavior, with the recent intentions being the most informative. Importantly, when the history of intentions is accounted for, our results indicate that the data support the “recency” literature—intentions grow over time leading up to purchase, and this growth has a positive impact on opening box office performance. While a linear growth model performs best for most movies, there exists a subset of movies for which the quadratic growth model better captures the “spike” in intentions just prior to purchase. Further, accounting for information on the history of intentions dramatically improves model fit and forecasting performance relative to when only the intentions at one point in time (e.g., the ones just prior to purchase) are accounted for.     

A new multivariate count data model to study multi-category physician prescription behavior

Multivariate count models represent a natural way of accommodating data from multiple product categories when the dependent variable in each category is represented by a positive integer. In this paper, we propose a new simultaneous equation multi-category count data model–the Poisson-lognormal simultaneous equation model–that allows for the Poisson parameter in one equation to be a function of the Poisson parameters in other equations. While generally applicable to any situation where simultaneity is an issue and the dependent variables are measured as counts, such a specification is particularly useful for our empirical application where physicians prescribe drugs in multiple categories. Accounting for the endogeneity of detailing in such situations requires us to explicitly allow for pharmaceutical firms’ detailing activities in one category to be influenced by their activities in other categories. Estimation of such a system of equations using traditional maximum likelihood method is cumbersome, so we propose a simple solution based on using Markov Chain Monte Carlo methods. Our simulation study demonstrates the validity of the solution algorithm and the biases that would result if such simultaneity is ignored in the estimation process.     

Empirical Analysis of a Dynamic Duopoly Model of Competition

Empirically validating and testing the specification of game theoretic models has received limited attention in the marketing literature. The authors provide an econometric framework for estimating the parameters of response functions when the observed data in the market place are the Nash equilibrium outcomes of an underlying dynamic duopoly game specification. Specifically, the estimation procedure accounts for the joint endogeneity of market shares and marketing efforts of market rivals using a system of simultaneous equations that included the market response function and the Nash equilibrium conditions. A formal statistical test is used to detect model misspecification. The empirical analysis is carried out using data from four product markets: pharmaceutical, soft drink, beer, and detergent. Comparisons are provided with conventional estimation of the response function parameters in which the equilibrium conditions are ignored in the estimation. Managerial implications of the empirical results are discussed.     

Investigating brand preferences across social groups and consumption contexts

Using a unique dataset on U.S. beer consumption, we investigate brand preferences of consumers across various social group and context related consumption scenarios (??scenarios??). As sufficient data are not available for each scenario, understanding these preferences requires us to share information across scenarios. Our proposed modeling framework has two main building blocks. The first is a standard continuous random coefficients logit model that the framework reduces to in the absence of information on social groups and consumption contexts. The second component captures variations in mean preferences across scenarios in a parsimonious fashion by decomposing the deviations in preferences from a base scenario into a low dimensional brand map in which the brand locations are fixed across scenarios but the importance weights vary by scenario. In addition to heterogeneity in brand preferences that is reflected in the random coefficients, heterogeneity in preferences across scenarios is accounted for by allowing the brand map itself to have a discrete heterogeneity distribution across consumers. Finally, heterogeneity in preferences within a scenario is accounted for by allowing the importance weights to vary across consumers. Together, these factors allow us to parsimoniously account for preference heterogeneity across brands, consumers and scenarios. We conduct a simulation study to reassure ourselves that using the kind of data that is available to us, our proposed estimator can recover the true model parameters from those data. We find that brand preferences vary considerably across the different social groups and consumption contexts as well as across different consumer segments. Despite the sparse data on specific brand-scenario combinations, our approach facilitates such an analysis and assessment of the relative strengths of brands in each of these scenarios. This could provide useful guidance to the brand managers of the smaller brands whose overall preference level might be low but which enjoy a customer franchise in a particular segment or in a particular context or a social group setting.     

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