方法对比

	Attraction Market-Share Model	Price Elasticity from Scanner Data
领域	市场营销	市场营销
方法族	Regression model	Regression model
起源年份	1975	1988
提出者	David E. Bell, Ralph L. Keeney & John D. C. Little; Lee G. Cooper & Masako Nakanishi	Dick R. Wittink, Peter S. H. Leeflang and colleagues (SCAN*PRO)
类型	Competitive market-share regression model	Multiplicative store-level sales-response regression
开创性文献	Bell, D. E., Keeney, R. L., & Little, J. D. C. (1975). A Market Share Theorem. Journal of Marketing Research, 12(2), 136-141.	Leeflang, P. S. H., Wittink, D. R., Wedel, M., & Naert, P. A. (2000). Building Models for Marketing Decisions. Kluwer Academic Publishers. ISBN: 9780792377726
别名	Market Share Theorem Models, MCI Model, Multiplicative Competitive Interaction Model, Differential-Effects Attraction Model	SCAN*PRO Model, Store-Level Sales Response Model, Multiplicative Sales Response Model, Promotion Sales-Effect Model
相关	3	3
摘要	The attraction market-share model expresses each brand's market share as its own 'attraction' divided by the total attraction of all brands competing in the market, guaranteeing shares that are non-negative and sum to one by construction. Its theoretical foundation is the 1975 'market share theorem' of Bell, Keeney, and Little, who proved that the familiar share-equals-effort-over-total-effort relationship follows from three mild axioms about attraction. Cooper and Nakanishi turned this into a practical empirical technology, the Multiplicative Competitive Interaction (MCI) model and its exponential MNL variant, in which attraction is built from the marketing mix — price, distribution, advertising, promotion. A log-centering transformation converts the inherently nonlinear share equation into a linear regression that can be estimated by ordinary or generalized least squares. The fitted parameters yield managerially crucial own- and cross-elasticities of share that respect the logically-consistent zero-sum nature of competition. The approach is a cornerstone of competitive market-share analysis and a close cousin of brand-choice logit models.	Estimating price elasticity from scanner data means fitting a store-level sales-response model to the weekly unit-sales, price, and promotion records that retail checkout scanners generate, in order to recover how sensitive demand is to price. The canonical specification is the SCAN*PRO model developed by Dick Wittink, Peter Leeflang, and colleagues: a multiplicative model in which a brand's unit sales in a store-week are a product of relative-price terms raised to elasticity powers and promotion multipliers for feature and display. Taking logarithms turns this into a linear regression whose price coefficients are directly interpretable as own- and cross-price elasticities, while the promotion coefficients become multiplicative lift factors. Pooled across many stores with store-specific intercepts, the model delivers stable, managerially usable elasticities and quantifies the sales lift from promotions. Later work, such as Van Heerde, Gupta, and Wittink, decomposed the promotional sales bump into brand switching, purchase acceleration, and category expansion, refining the interpretation of what an elasticity captures. It is the standard aggregate demand model in retail analytics.