Порівняння методів

	Price Elasticity from Scanner Data	Reference Price Modeling
Галузь	Маркетинг	Маркетинг
Родина	Regression model	Regression model
Рік появи	1988	1995
Автор методу	Dick R. Wittink, Peter S. H. Leeflang and colleagues (SCAN*PRO)	Gurumurthy Kalyanaram & Russell S. Winer (synthesis); prospect-theory pricing tradition
Тип	Multiplicative store-level sales-response regression	Behavioral price-response model with reference dependence
Основоположне джерело	Leeflang, P. S. H., Wittink, D. R., Wedel, M., & Naert, P. A. (2000). Building Models for Marketing Decisions. Kluwer Academic Publishers. ISBN: 9780792377726	Kalyanaram, G., & Winer, R. S. (1995). Empirical Generalizations from Reference Price Research. Marketing Science, 14(3 Supplement), G161-G169.
Інші назви	SCAN*PRO Model, Store-Level Sales Response Model, Multiplicative Sales Response Model, Promotion Sales-Effect Model	Reference Price Effects, Sticker-Shock Model, Asymmetric Price Response Model, Prospect-Theoretic Pricing Model
Пов'язані	3	3
Підсумок	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.	Reference price models capture the behavioral reality that consumers judge a price not in absolute terms but relative to an internal benchmark — a reference price they have formed from past prices. When the observed price falls below the reference the shopper perceives a gain; when it rises above, a loss, an unpleasant 'sticker shock.' Drawing on prospect theory, these models enter gains and losses as separate terms and let losses weigh more heavily than equivalent gains, an asymmetry known as loss aversion. Kalyanaram and Winer's 1995 synthesis crystallized three robust empirical generalizations: consumers use reference prices, they form them largely from past prices, and they respond more strongly to losses than to gains. The reference price itself is usually constructed by exponentially smoothing past prices, the same smoothing logic Guadagni and Little used to build loyalty variables, and the gain and loss terms are embedded in a brand-choice logit or demand model estimated on scanner panel data. The result is a richer, behaviorally grounded picture of how price changes move demand than a single symmetric price coefficient allows.