Forecasting new product trial in a controlled test market environment

A number of researchers have developed models that use test market data to generate forecasts of a new product's performance. However, most of these models have ignored the effects of marketing covariates. In this paper we examine what impact these covariates have on a model's forecasting...

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Bibliographic Details
Published in:Journal of forecasting 2003-08, Vol.22 (5), p.391-410
Main Authors: Fader, Peter S., Hardie, Bruce G. S., Zeithammer, Robert
Format: Article
Language:English
Subjects:
Calibration
Economic models
Economics
Error
Forecasts
Market
Marketing
Mathematical models
Methods
Model testing
new product sales forecasting
Packaged goods
Purchasing
Sales
Sales forecasting
Studies
test market
Test markets
trial and repeat
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Summary:A number of researchers have developed models that use test market data to generate forecasts of a new product's performance. However, most of these models have ignored the effects of marketing covariates. In this paper we examine what impact these covariates have on a model's forecasting performance and explore whether their presence enables us to reduce the length of the model calibration period (i.e. shorten the duration of the test market). We develop from first principles a set of models that enable us to systematically explore the impact of various model ‘components’ on forecasting performance. Furthermore, we also explore the impact of the length of the test market on forecasting performance. We find that it is critically important to capture consumer heterogeneity, and that the inclusion of covariate effects can improve forecast accuracy, especially for models calibrated on fewer than 20 weeks of data. Copyright © 2003 John Wiley & Sons, Ltd.
ISSN:0277-6693
1099-131X
DOI:10.1002/for.869