Forecasting volumes for trade promotions in CPG industry using market drivers
ABSTRACT Promotions are an integral part of the consumer packaged goods (CPG) industry. Anywhere between 30-40% of the sales volumes are achieved through various promotions. Promotions are instrumental in creating brand visibility and awareness. In this study, we attempt to analyse the impact of promotions with feature advertises, in-store display, temporary price discounts etc. Five different multivariate regression models have been developed to forecast the total sales of a product considering pricing and distribution variables. The performance of these models has been analysed by using syndicated data. Based on the results, it is found that the S-shaped (double-log) model has shown superior performance over the other models considered in this study.
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ABSTRACT: The use of price promotions to stimulate brand and firm performance is increasing. We discuss how (i) the availability of longer scanner data time series, and (ii) persistence modelling, have lead to greater insights into the dynamic effects of price promotions, as one can now quantify their immediate, short-run, and long-run effectiveness. We review recent methodological developments, and illustrate how the analysis of numerous brands and product categories has resulted in various empirical generalizations. Finally, we argue that persistence modelling should not only be applied to traditional performance metrics such as sales, but also to metrics such as firm value and customer equity. Copyright © 2005 John Wiley & Sons, Ltd.Applied Stochastic Models in Business and Industry 06/2005; 21(4‐5):409 - 416. · 0.54 Impact Factor
- The Journal of Business. 02/1968; 41:488-488.
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ABSTRACT: The authors show analytically, empirically, and numerically through simulation that the estimated effects from linearly aggregated market- level data differ substantially from comparable effects that are obtained from store-level data. The magnitude of this difference renders market- level data largely unsuitable for econometric modeling, unless the mar- keting manager compensates for the bias that results from the incompat- ible aggregation. The authors introduce a new approach, a relatively sim- ple debiasing procedure derived from simulated data. They show that this debiasing approach results in substantially improved parameter esti- mates. They illustrate the value of the procedure by applying it to scanner data for powdered detergents and comparing the debiased parameter estimates to results obtained from store-level data and an alternative aggregation method that maintains homogeneity for selected promotional activities.Journal of Marketing Research 08/1997; 34(3):322-334. · 2.52 Impact Factor