The "84/14/2" rule revisited : what drives choice, incidence and quantity elasticities?
|Authors||Bell, David R.
|Source||Marketing Working Paper Series ; MKTG 97.089|
|Summary||A brand's total price elasticity, conditional on a purchase occasion, can be decomposed into three components: the brand choice, purchase incidence and purchase quantity elasticity. Gupta (1988) has analyzed this relationship within the context of a single product category. That study reported that the main impact of a price promotion falls on brand choice (84%), and to a lesser extent on purchase timing acceleration (14%) and stockpiling (2%). This study pursues the empirical generalizability of this previous finding on decomposition of price response. In doing so, the research makes three new substantive contributions to the field. First, while we confirm that the majority of the promotion effect is derived from choice, the relative emphasis on incidence and quantity varies systematically across categories. For instance, storable products have relatively higher weight on quantity, perishable products have a higher weight on incidence. Second, we utilize a generalized least squares meta analysis procedure (Montgomery and Srinivasan, 1996) to show how factors such as marketing effort, category structure, brand franchise and consumer demographics explain variance in each of the three types of elasticities (choice, incidence and quantity). One of the key findings here is that variable related to category structure play the greatest role in explaining variance in all three elasticity types. Furthermore, unpredictability of marketing effort has more influence on elasticity response than does relative levels of marketing effort. Third, we show that in several instances where important exogenous variables do not affect total elasticities, this is due to offsetting effects within two or more of the three behavioral components of elasticity. We also utilize existing literature and consumer behavior theory to rationalize the influence of key exogenous variables on promotional response. Thus, the paper offers an empirical generalization of a key finding on promotional response, and new insights into factors that explain variance in all three elasticity types. As such, the finding are of interest to researchers who are concerned with generalizability of marketing phenomena, and to managers who must plan promotion campaigns within the confines of particular product categories. To calibrate our choice and meta-analytic models, we use a multicategory scanner panel dataset to generate a total of 519 choice, incidence and quantity elasticity estimates for 173 brands within 13 categories. Our choice model is a hybrid of that developed in Krishamurthi and Raj (1988) and Chiang (1991). After presenting our empirical findings, we conclude the paper with a discussion of managerial implications for developing effective promotion strategies.|
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