Modeling Marketing Mix

Abstract

The marketing mix refers to variables that a marketing manager can control to influence a brand’s sales or market share. Traditionally, these variables are summarized as the four Ps of marketing: product, price, promotion, and place (i.e., distribution; McCarthy, 1996). Product refers to aspects such as the firm’s portfolio of products, the newness of those products, their differentiation from competitors, or their superiority to rivals ’ products in terms of quality. Promotion refers to advertising, detailing, or informative sales promotions such as features and displays. Price refers to the product’s list price or any incentive sales promotion such as quantity discounts, temporary price cuts, or deals. Place refers to delivery of the product measured by variables such as distribution, availability, and shelf space. The perennial question that managers face is, what level or combination of these variables maximizes sales, market share, or profit? The answer to this question, in turn, depends on the following question: How do sales or market share respond to past levels of or expenditures on these variables? PHILOSOPHY OF MODELING Over the past 45 years, researchers have focused intently on trying to find answers to this question (e.g., see Tellis, 1988b). To do so, they have developed a variety of econometric models of market response to the marketing mix. Most of these models have focused on market response to advertising and pricing (Sethuraman & Tellis, 1991). The reason may be that expenditures on these variables seem the most discretionary, so marketing managers are most concerned about how they manage these variables. This chapter reviews this body of literature. It focuses on modeling response to these variables, though most of the principles apply as well to other variables in the marketing mix. It relies on elementary models that Chapters 12 and 13 introduce. To tackle complex problems, this chapter refers to advanced models, which Chapters 14, 19, and 20 introduce. The basic philosophy underlying the approach of response modeling is that past data on consumer and market response to the marketing mix contain valuable information that can enlighten our understanding of response. Those data also enable us to predict how consumers

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