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Forecasting new product trial with analogous series

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  • Wright, Malcolm J.
  • Stern, Philip

Abstract

This study develops a simple method for forecasting consumer trial for national product launches. The number of consumers who try a brand in its first year on the market is accurately predicted from the number trying the brand in the first thirteen weeks following launch. No information about the specific category or marketing activities is required — just a simple multiplier computed from analogous series in other markets. These analogues provide an empirical generalization that can be easily applied by practicing managers to track and forecast the success of new brand launches. When subject to an out-of-sample test involving 34 fresh data sets, the analogues demonstrated 43% reduction in mean absolute percentage error compared to the most accurate marketing science model.

Suggested Citation

  • Wright, Malcolm J. & Stern, Philip, 2015. "Forecasting new product trial with analogous series," Journal of Business Research, Elsevier, vol. 68(8), pages 1732-1738.
  • Handle: RePEc:eee:jbrese:v:68:y:2015:i:8:p:1732-1738
    DOI: 10.1016/j.jbusres.2015.03.032
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    References listed on IDEAS

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    Cited by:

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    2. Brighton, Henry & Gigerenzer, Gerd, 2015. "The bias bias," Journal of Business Research, Elsevier, vol. 68(8), pages 1772-1784.
    3. Ramírez-Hassan, Andrés & Montoya-Blandón, Santiago, 2020. "Forecasting from others’ experience: Bayesian estimation of the generalized Bass model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 442-465.
    4. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2022. "Retail forecasting: Research and practice," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1283-1318.
    5. Fabio Antonialli, 2019. "International benchmark on experimentations with Autonomous Shuttles for Collective Transport," Post-Print hal-02489797, HAL.
    6. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    7. 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.
    8. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    9. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    10. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
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