A Hierarchical Bayes Error Correction Model to Explain Dynamic Effects of Price Changes

Abstract

AND KEYWORDS AbstractThe authors put forward a sales response model to explain the differences in immediate and dynamic effects of promotional prices and regular prices on sales. The model consists of a vector autoregression rewritten in error-correction format which allows to disentangle the immediate effects from the dynamic effects. In a second level of the model, the immediate price elasticities, the cumulative promotional price elasticity and the long-run regular price elasticity are correlated with various brand-speciffic and category-speciffic characteristics. The model is applied to seven years of data on weekly sales of 100 different brands in 25 product categories.We find many significant moderating effects on the elasticity of price promotions. Brands in categories that are characterized by high price differentiation and that constitute a lower share of budget are less sensitive to price discounts. Deep price discounts turn out to increase the immediate price sensitivity of customers. We also find significant effects for the cumulative elasticity. The immediate effect of a regular price change is often close to zero. The long-run effect of such a decrease usually amounts to an increase in sales. This is especially true in categories characterized by a large price dispersion, frequent price promotions and hedonic, A Hierarchical Bayes Error Correction Model to Explain Dynamic Effects of Price Changes AbstractThe authors put forward a sales response model to explain the differences in immediate and dynamic effects of promotional prices and regular prices on sales.The model consists of a vector autoregression rewritten in error-correction format which allows to disentangle the immediate effects from the dynamic effects. In a second level of the model, the immediate price elasticities, the cumulative promotional price elasticity and the long-run regular price elasticity are correlated with various brand-specific and category-specific characteristics. The model is applied to seven years of data on weekly sales of 100 different brands in 25 product categories.We find many significant moderating effects on the elasticity of price promotions.Brands in categories that are characterized by high price differentiation and that constitute a lower share of budget are less sensitive to price discounts. Deep price discounts turn out to increase the immediate price sensitivity of customers. We also find significant effects for the cumulative elasticity. The immediate effect of a regular price change is often close to zero. The long-run effect of such a decrease usually amounts to an increase in sales. This is especially true in categories characterized by a large price dispersion, frequent price promotions and hedonic, non-perishable products.

Does competitive entry structurally change key marketing metrics?

“…As indicated in Table 7, the three series were found to be evolving in the single-break tests, implying that all shocks would have a steady-state effect (given by Campbell and Mankiw's regular shock persistency in the final column of Table 7), as opposed to our finding that only a limited number of known major events have an impact on the series' steady-state performance. Conceptually, the latter is a more appealing scenario, as also argued in Deleersnyder et al (2002) and Fok et al (2006). As for the impact of our focal event, we found a structural break in the slope of two of these three series, while this was not the case with the single-break tests.…”

“…However, recent marketing studies have questioned how realistic it is to assume that all shocks can have a permanent effect (see e.g. Deleersnyder et al, 2002or Fok, Horváth, Paap, & Franses, 2006. In a TS model without breaks, in contrast, none of the events that took place during the time span under investigation has a long-run effect.…”

Interaction Between Shelf Layout and Marketing Effectiveness and Its Impact on Optimizing Shelf Arrangements

“…To allow for dynamic effects and if data availability permits, one may also include lagged prices and/or lagged sales (see Fok et al 2006). We let the error term i t be independently distributed N 0 2 i .…”

Modeling Marketing Dynamics by Time Series Econometrics

“…Fok et al (2004), for example, develop a Hierarchical Bayes Error Correction Model to study the dynamic effects of price promotions, while Litterman (1984) argues that Bayesian VAR models are less susceptible to the aforementioned Lucas Critique.…”