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Forecasting new product trial in a controlled test market environment

Author

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  • Bruce G. S. Hardie

    (London Business School, UK)

  • Peter S. Fader

    (The Wharton School, University of Pennsylvania, USA)

  • Robert Zeithammer

    (MIT Sloan School of Management, USA)

Abstract

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.

Suggested Citation

  • Bruce G. S. Hardie & Peter S. Fader & Robert Zeithammer, 2003. "Forecasting new product trial in a controlled test market environment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(5), pages 391-410.
    • Handle: RePEc:jof:jforec:v:22:y:2003:i:5:p:391-410
    DOI: 10.1002/for.869
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    References listed on IDEAS

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    Cited by:

    1. Michael Braun & André Bonfrer, 2011. "Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes," Marketing Science, INFORMS, vol. 30(3), pages 513-531, 05-06.
    2. Mayukh Dass & Masoud Moradi & Fereshteh Zihagh, 2023. "Forecasting purchase rates of new products introduced in existing categories," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(3), pages 385-408, September.
    3. Wright, Malcolm J. & Stern, Philip, 2015. "Forecasting new product trial with analogous series," Journal of Business Research, Elsevier, vol. 68(8), pages 1732-1738.
    4. Peter S. Fader & Bruce G. S. Hardie & Chun-Yao Huang, 2004. "A Dynamic Changepoint Model for New Product Sales Forecasting," Marketing Science, INFORMS, vol. 23(1), pages 50-65, October.
    5. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.
    6. Teck-Hua Ho & Young-Hoon Park & Yong-Pin Zhou, 2006. "Incorporating Satisfaction into Customer Value Analysis: Optimal Investment in Lifetime Value," Marketing Science, INFORMS, vol. 25(3), pages 260-277, 05-06.
    7. John H. Roberts & Charles J. Nelson & Pamela D. Morrison, 2005. "A Prelaunch Diffusion Model for Evaluating Market Defense Strategies," Marketing Science, INFORMS, vol. 24(1), pages 150-164, August.
    8. Schweidel, David A. & Fader, Peter S., 2009. "Dynamic changepoints revisited: An evolving process model of new product sales," International Journal of Research in Marketing, Elsevier, vol. 26(2), pages 119-124.
    9. Bogomolova, Svetlana & Anesbury, Zachary & Lockshin, Larry & Kapulski, Natasha & Bogomolov, Tim, 2019. "Exploring the incidence and antecedents of buying an FMCG brand and UPC for the first time," Journal of Retailing and Consumer Services, Elsevier, vol. 46(C), pages 121-129.

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