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Abstract:
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. In order 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.

Econometric Models, Hi-Tech Marketing, Advertising, Product Line Attractiveness, Product Development, Nested Logit Models, Kalman Filter

Abstract:
We study the optimal levels of advertising and promotion budgets in dynamic markets with brand equity as a mediating variable. To this end, we develop and estimate a state-space model based on the Kalman filter that captures the dynamics of brand equity as influenced by its drivers that include a brand's advertising and sales promotion expenditures. By integrating the Kalman filter with the random coefficients logit demand model, our estimation allows us to capture the dynamics of brand equity as well as model consumer heterogeneity using store-level data. Using these demand model estimates, we determine the Markov Perfect Equilibrium advertising and promotion strategies. Our empirical analysis is based on store-level scanner data in the orange juice category, which comprises two major brands - Tropicana and Minute Maid. As expected, we find that sales promotions have a significant positive effect on consumers' utility and induce consumers to switch to the promoted brand. However, there is also a negative effect of promotions on brand equity that carries over from period to period. Overall, we find that while sales promotions have a net positive impact both in the short-term and in the long-term, the implied total elasticity including the long-term effect is smaller than the short-term elasticity. Correspondingly, we expect myopic decision-makers to allocate higher than optimal expenditures to sales promotions. Our results from the supply side analysis reveal that the actual promotion levels for both brands are indeed higher than the optimal budgets for the forward-looking (long-term orientation) as well as the two-year planning horizon scenarios. Hence, it may be profitable for both brands to reduce their promotion levels. Further, we find that although the forward-looking promotional spending levels are higher for the smaller brand, Minute Maid, it is the market leader, Tropicana, which spends more on sales promotions. Turning to optimal advertising budgets, we find that the equilibrium forward-looking advertising levels are higher for Tropicana, the brand that has higher brand equity and a higher responsiveness to advertising. Further, as expected, the optimal forward-looking advertising levels are higher than the myopic levels and the two-year planning horizon levels for both brands. However, the forward-looking advertising levels are lower than the actual advertising expenditures for both brands. This implies that even when we consider the long-term effects of advertising, the brands are over-spending on advertising.

Dynamic Brand Equity, Kalman Filter, Optimal Advertising, Optimal Promotions, Markov Perfect Equilibrium

Abstract:
We present a framework for modeling consumer adoption of multiple categories of technology products that may be related as complements (or substitutes). The context of technology products as well as the relationship between categories poses some unique challenges. First, the declining prices (and the corresponding increase in quality levels) over time imply that consumers anticipate these changes and make a trade-off between adopting the product early on and consuming the product for a longer time versus adopting later at lower prices. Second, the durable nature of technology products implies that even if two categories are related as complements, consumers may stagger their purchases over several periods; unlike in the case of packaged goods, one cannot infer complementary relationships between these categories based on joint purchases. Third, the adoption decision for some categories (such as printers) may be contingent upon adoption of another related category (such as a personal computer). We illustrate how our proposed modeling framework is flexible enough to address these issues in the context of two related categories and discuss how it can be extended to multiple categories. We apply our modeling framework to a unique dataset that contains information on consumer adoption of three related categories of technology products - personal computers, digital cameras, and printers. The results reveal a strong complementary relationship between these categories. As a result, the probability that a consumer would adopt a given category increases significantly if she has already adopted one or more of the related categories. Policy simulations based on a temporary price decrease in any one category provide interesting insights into how consumers would modify their adoption behavior over time as well as across categories as a consequence of the price decrease.

Technology products, product adoption, multiple categories, complementarity, forward-looking consumers

Abstract:
We investigate the effect of competitive entry on manufacturer and retailer pricing behavior. Since the observed price changes can be due to entry-induced changes in a) demand conditions or b) costs, c) manufacturer's competitive behavior, or d) retailer's competitive behavior, a robust empirical model should be able to parse out these four sources of price changes. In order to understand realistic and managerially relevant responses to entry/brand introduction, we model manufacturer and retailer pricing as an outcome of maximizing a combination of shares and profits. This inclusion of shares in the objective function enables us to isolate the impact on prices of the four effects. Furthermore, our formulation of the objective function enables us to parsimoniously capture the full range of competitive behavior from very competitive to collusive outcomes. This formulation builds on well-established theoretical literature (including the strategic incentive design and entry literatures), anti-trust evidence and managerial practice the notion that firms' competitive conduct manifests itself as maximizing not just pure profits but a weighted combination of profits and market shares or sales. As a result, we are able to address the following questions: (1) how do manufacturers change competitive pricing conduct as entry happens? Do some of them price to protect their market shares? Are there any patterns in this behavior in terms of weaker/stronger brands and are these patterns consistent with theoretical predictions? (2) what is the retailer's pricing objective and conduct for each brand? Does this change with entry and in what manner? (3) how does channel power as measured by profit percentages for manufacturer versus retailer change as a result of these brand introductions, and the accompanying changes in competitive conduct? (4) how do these changes in manufacturer and retailer behavior and their power depend on the type of brand introduction i.e. if it is a de novo brand introduction versus a line extension of an established brand?

Our empirical investigation is based on the toothpaste category for the time period January 1993-February 1995. In this period, the category saw three brand introductions in two rounds of entry. We find that incumbent manufacturers' response to brand introductions is a function of the entrant manufacturer's brand introduction stance as well as the threat posed by incumbents. For example, the de novo entrant chooses a non-aggressive brand introduction stance. In response to this brand introduction, incumbent manufacturers price to preserve their market share, and therefore exhibit an aggressive pricing response. On the other hand, line extensions of existing brands enter more aggressively and are, in turn, accommodated more softly by other incumbent manufacturers. We find that the retailer's response to entry is predominantly similar to that of the manufacturers'. When manufacturers respond to brand extensions aggressively by resorting to price cuts, retailers' share of the total channel profits increases; the reverse is true for a soft accommodation of entry. Contrary to what one might expect, the entrant brand is not necessarily disadvantaged relative to incumbent brands in terms of channel profit share. Entrants who can generate good demand pull and who have a soft entry stance can expect more favorable accommodation from retailers and other manufacturers.

channels pricing, channels competitive conduct, brand introductions, profit and sales maximization

Abstract:
Management of brand equity has come to be viewed as critical to the optimal long-term performance of a brand. In this paper, we evaluate the usefulness of brand equity estimates obtained from store-level data for monitoring the health of a brand. For this purpose, we use a random coefficients logit demand model calibrated on store-level scanner data to track brand equity estimates over time in two consumer packaged goods categories that experienced several new product introductions during the time period of our empirical investigation. Using these tracked measures, we also study the impact of marketing actions such as advertising, sales promotions, and product innovations on brand equity. We find that the brand equity estimates effectively capture the high equity of strongly positioned popular brands as well as brands that command a significant price premium in niche markets. Using an example, we illustrate how these brand equity estimates can be used to monitor changes in brand equity, which measures such as market share may fail to capture. Our substantive results indicate that advertising has a positive effect on brand equity in both the product categories whereas the effect of sales promotions is not significant in either category. Further, our results reveal that new product innovations have a positive impact on brand equity and can explain a significant proportion of its variation. Overall, our analysis shows a brand manager can track brand equity using store-level data, gain insights into the drivers of the brand's equity, and manage these drivers to achieve brand equity targets.

Brand Preference, Choice Models, Advertising, Sales Promotion, Innovation, Econometric Models