Effects of Brand Preference, Product Attributes, and Marketing Mix Variables in Technology Product Markets
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.
Volume (Year): 25 (2006)
Issue (Month): 5 (September)
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- Kamel Jedidi & Carl F. Mela & Sunil Gupta, 1999. "Managing Advertising and Promotion for Long-Run Profitability," Marketing Science, INFORMS, vol. 18(1), pages 1-22.
- K. Sudhir, 2001. "Competitive Pricing Behavior in the Auto Market: A Structural Analysis," Marketing Science, INFORMS, vol. 20(1), pages 42-60, January.
- Prasad A. Naik & Murali K. Mantrala & Alan G. Sawyer, 1998. "Planning Media Schedules in the Presence of Dynamic Advertising Quality," Marketing Science, INFORMS, vol. 17(3), pages 214-235.
- Nevo, Aviv, 2001.
"Measuring Market Power in the Ready-to-Eat Cereal Industry,"
Econometric Society, vol. 69(2), pages 307-342, March.
- Nevo, Aviv, 1998. "Measuring Market Power in the Ready-To-Eat Cereal Industry," Research Reports 25164, University of Connecticut, Food Marketing Policy Center.
- Nevo, Aviv, 1999. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Competition Policy Center, Working Paper Series qt7cm5p858, Competition Policy Center, Institute for Business and Economic Research, UC Berkeley.
- Aviv Nevo, 2003. "Measuring Market Power in the Ready-to-Eat Cereal Industry," Microeconomics 0303006, EconWPA.
- Aviv Nevo, 1998. "Measuring Market Power in the Ready-to-Eat Cereal Industry," NBER Working Papers 6387, National Bureau of Economic Research, Inc.
- Nevo, Aviv, 1998. "Measuring Market Power in the Ready-To-Eat Cereal Industry," Food Marketing Policy Center Research Reports 037, University of Connecticut, Department of Agricultural and Resource Economics, Charles J. Zwick Center for Food and Resource Policy.
- Barry L. Bayus & William P. Putsis, Jr., 1999. "Product Proliferation: An Empirical Analysis of Product Line Determinants and Market Outcomes," Marketing Science, INFORMS, vol. 18(2), pages 137-153.
- Michaela Draganska & Dipak C. Jain, 2006. "Consumer Preferences and Product-Line Pricing Strategies: An Empirical Analysis," Marketing Science, INFORMS, vol. 25(2), pages 164-174, 03-04.
- Harvey,Andrew C., 1991. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521405737, January.
- Harvey,Andrew C., 1990. "Forecasting, Structural Time Series Models and the Kalman Filter," Cambridge Books, Cambridge University Press, number 9780521321969, January.
- Elie Ofek & V. Srinivasan, 2002. "How Much Does the Market Value an Improvement in a Product Attribute?," Marketing Science, INFORMS, vol. 21(4), pages 398-411, June.
- Sunder Kekre & Kannan Srinivasan, 1990. "Broader Product Line: A Necessity to Achieve Success?," Management Science, INFORMS, vol. 36(10), pages 1216-1232, October.
- Steven T. Berry, 1994. "Estimating Discrete-Choice Models of Product Differentiation," RAND Journal of Economics, The RAND Corporation, vol. 25(2), pages 242-262, Summer.
- Prasad A. Naik & Kalyan Raman & Russell S. Winer, 2005. "Planning Marketing-Mix Strategies in the Presence of Interaction Effects," Marketing Science, INFORMS, vol. 24(1), pages 25-34, June.
- Leonard M. Lodish & Magid M. Abraham & Jeanne Livelsberger & Beth Lubetkin & Bruce Richardson & Mary Ellen Stevens, 1995. "A Summary of Fifty-Five In-Market Experimental Estimates of the Long-Term Effect of TV Advertising," Marketing Science, INFORMS, vol. 14(3_supplem), pages 133-140.
- Michaela Draganska & Dipak C. Jain, 2005. "Product-Line Length as a Competitive Tool," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 14(1), pages 1-28, 03.
- Harvey, Andrew & Snyder, Ralph D., 1990. "Structural time series models in inventory control," International Journal of Forecasting, Elsevier, vol. 6(2), pages 187-198, July.
- K. Sudhir, 2001. "Competitive Pricing Behavior in the US Auto Market: A Structural Analysis," Yale School of Management Working Papers ysm228, Yale School of Management.
- Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
- M. Tolga Akçura & Füsun F. Gönül & Elina Petrova, 2004. "Consumer Learning and Brand Valuation: An Application on Over-the-Counter Drugs," Marketing Science, INFORMS, vol. 23(1), pages 156-169, April.
- Pradeep K. Chintagunta, 2001. "Endogeneity and Heterogeneity in a Probit Demand Model: Estimation Using Aggregate Data," Marketing Science, INFORMS, vol. 20(4), pages 442-456, December.
- Inseong Song & Pradeep Chintagunta, 2003. "A Micromodel of New Product Adoption with Heterogeneous and Forward-Looking Consumers: Application to the Digital Camera Category," Quantitative Marketing and Economics (QME), Springer, vol. 1(4), pages 371-407, December. Full references (including those not matched with items on IDEAS)
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