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Effects of Brand Preference, Product Attributes, and Marketing Mix Variables in Technology Product Markets

School of Business, University of Connecticut, 2100 Hillside Road, Storrs, Connecticut 06269
Graduate School of Business, University of Chicago, 5807 South Woodlawn Avenue, Chicago, Illinois 60637
Amrita School of Business, Ettimadai, Coimbatore 641105, India
ssriram{at}business.uconn.edu
pradeep.chintagunta{at}chicagogsb.edu
n_ramya{at}ettimadai.amrita.edu

We develop a demand model for technology products that captures the effect of changes in the portfolio of models offered by a brand as well as the influence of the dynamics in its intrinsic preference on that brand’s performance. To account for the potential correlation in the preferences of models offered by a particular brand, we use a nested logit model with the brand (e.g., Sony) at the upper level and its various models (e.g., Mavica, FD, DSC, etc.) at the lower level of the nest. Relative model preferences are captured via their attributes and prices. We allow for heterogeneity across consumers in their preferences for these attributes and in their price sensitivities in addition to heterogeneity in consumers’ intrinsic brand preferences. Together with the nested logit assumption, this allows for a flexible substitution pattern across models at the aggregate level. The attractiveness of a brand’s product line changes over time with entry and exit of new models and with changes in attribute and price levels. To allow for time-varying intrinsic brand preferences, we use a state-space model based on the Kalman filter, which captures the influence of marketing actions such as brand-level advertising on the dynamics of intrinsic brand preferences. Hence, the proposed model accounts for the effects of brand preferences, model attributes and marketing mix variables on consumer choice. First, we carry out a simulation study to ensure that our estimation procedure is able to recover the true parameters generating the data. Then, we estimate our model parameters on data for the U.S. digital camera market. Overall, we find that the effect of dynamics in the intrinsic brand preference is greater than the corresponding effect of the dynamics in the brand’s product line attractiveness. Assuming plausible profit margins, we evaluate the effect of increasing the advertising expenditures for the largest and the smallest brands in this category and find that these brands can increase their profitability by increasing their advertising expenditures. We also analyze the impact of modifying a camera model’s attributes on its profits. Such an analysis could potentially be used to evaluate if product development efforts would be profitable.

Key Words: econometric models; hi-tech marketing; advertising; product line attractiveness; product development; nested logit models; Kalman filter
History: Received: June 28, 2004;

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