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Forecasting purchase rates of new products introduced in existing categories

Author

Listed:
  • Mayukh Dass

    (Texas Tech University, MS 2101)

  • Masoud Moradi

    (Texas State University)

  • Fereshteh Zihagh

    (Texas State University)

Abstract

One of the challenges product managers face in formulating strategic decisions is how to effectively forecast the performance of a new product in the marketplace. This paper develops a stochastic model, which is then applied to household purchase data to predict ultimate trial and first repeat purchase rates for a new brand introduced in an existing category of common frequently purchased goods. Previous research directed at the estimation of these two common indicators of new product success, as well as critical components of volume forecasting models, has focused on the elapsed calendar time to purchase or repurchase of the new brand. However, typical scanner panel data also contain information regarding consumer purchasing behavior of other brands in the category. The model developed in this paper utilizes this richer category purchase data from a household scanner data to estimate these two critical forecasting parameters: ultimate trial and first repeat rates. Results from four instances, including two cases involving new products and two cases involving existing brands, are used to demonstrate the value of the category purchase approach in comparison to the elapsed calendar time to purchase approach. Results show that the category purchase approach is more accurate in predicting the percentage of clients who would ever try a new product in the market (i.e., eventual trial) and in predicting the first repeat rate. Results also demonstrate how this projected eventual trial and first repeat rates could be used to develop a better estimate of long-term market share. We demonstrate that this approach provides an improved forecasting tool and recommend that marketers should, when possible, take advantage of this method.

Suggested Citation

  • 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.
    • Handle: RePEc:pal:jmarka:v:11:y:2023:i:3:d:10.1057_s41270-022-00169-4
    DOI: 10.1057/s41270-022-00169-4
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    References listed on IDEAS

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    1. David C. Schmittlein & Albert C. Bemmaor & Donald G. Morrison, 1985. "Technical Note—Why Does the NBD Model Work? Robustness in Representing Product Purchases, Brand Purchases and Imperfectly Recorded Purchases," Marketing Science, INFORMS, vol. 4(3), pages 255-266.
    2. Berge, Travis J., 2018. "Understanding survey-based inflation expectations," International Journal of Forecasting, Elsevier, vol. 34(4), pages 788-801.
    3. Canina, Linda & Carvell, Steven A. & Ma, Qingzhong & Ukhov, Andrey D., 2013. "Business cycle and asset valuation in the gaming industry," Journal of Business Research, Elsevier, vol. 66(9), pages 1689-1695.
    4. Wright, Malcolm J. & Stern, Philip, 2015. "Forecasting new product trial with analogous series," Journal of Business Research, Elsevier, vol. 68(8), pages 1732-1738.
    5. Armstrong, J. Scott & Morwitz, Vicki G. & Kumar, V., 2000. "Sales forecasts for existing consumer products and services: Do purchase intentions contribute to accuracy?," International Journal of Forecasting, Elsevier, vol. 16(3), pages 383-397.
    6. L. Beril Toktay & Lawrence M. Wein & Stefanos A. Zenios, 2000. "Inventory Management of Remanufacturable Products," Management Science, INFORMS, vol. 46(11), pages 1412-1426, November.
    7. S. Arunachalam & S. Cem Bahadir & Sundar G. Bharadwaj & Rodrigo Guesalaga, 2020. "New product introductions for low-income consumers in emerging markets," Journal of the Academy of Marketing Science, Springer, vol. 48(5), pages 914-940, September.
    8. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    9. Brandt, Patrick T. & Freeman, John R. & Schrodt, Philip A., 2014. "Evaluating forecasts of political conflict dynamics," International Journal of Forecasting, Elsevier, vol. 30(4), pages 944-962.
    10. Berk, K. & Hoffmann, A. & Müller, A., 2018. "Probabilistic forecasting of industrial electricity load with regime switching behavior," International Journal of Forecasting, Elsevier, vol. 34(2), pages 147-162.
    11. Lyócsa, Štefan & Todorova, Neda, 2020. "Trading and non-trading period realized market volatility: Does it matter for forecasting the volatility of US stocks?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 628-645.
    12. Huber, Jakob & Stuckenschmidt, Heiner, 2020. "Daily retail demand forecasting using machine learning with emphasis on calendric special days," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1420-1438.
    13. Kristiaan Helsen & David C. Schmittlein, 1993. "Analyzing Duration Times in Marketing: Evidence for the Effectiveness of Hazard Rate Models," Marketing Science, INFORMS, vol. 12(4), pages 395-414.
    14. Belvedere, Valeria & Goodwin, Paul, 2017. "The influence of product involvement and emotion on short-term product demand forecasting," International Journal of Forecasting, Elsevier, vol. 33(3), pages 652-661.
    15. 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.
    16. Meade, Nigel & Islam, Towhidul, 2010. "Using copulas to model repeat purchase behaviour - An exploratory analysis via a case study," European Journal of Operational Research, Elsevier, vol. 200(3), pages 908-917, February.
    17. Morwitz, Vicki G. & Steckel, Joel H. & Gupta, Alok, 2007. "When do purchase intentions predict sales?," International Journal of Forecasting, Elsevier, vol. 23(3), pages 347-364.
    18. Frank M. Bass & Moshe M. Givon & Manohar U. Kalwani & David Reibstein & Gordon P. Wright, 1984. "An Investigation into the Order of the Brand Choice Process," Marketing Science, INFORMS, vol. 3(4), pages 267-287.
    19. 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.
    20. Young, Robert B. & Javalgi, Rajshekhar G., 2007. "International marketing research: A global project management perspective," Business Horizons, Elsevier, vol. 50(2), pages 113-122.
    21. Fildes, Robert & Ma, Shaohui & Kolassa, Stephan, 2019. "Retail forecasting: research and practice," MPRA Paper 89356, University Library of Munich, Germany.
    22. Sarkar, Mainak & De Bruyn, Arnaud, 2021. "LSTM Response Models for Direct Marketing Analytics: Replacing Feature Engineering with Deep Learning," Journal of Interactive Marketing, Elsevier, vol. 53(C), pages 80-95.
    23. Gonzalez, Rodrigo Barbone & Marinho, Leonardo Sousa Gomes & Lima, Joaquim Ignacio Alves de Vasconcellos e, 2017. "Re-anchoring countercyclical capital buffers: Bayesian estimates and alternatives focusing on credit growth," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1007-1024.
    24. Ma, Shaohui & Fildes, Robert & Huang, Tao, 2016. "Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information," European Journal of Operational Research, Elsevier, vol. 249(1), pages 245-257.
    25. Morrison, Donald G & Schmittlein, David C, 1988. "Generalizing the NBD Model for Customer Purchases: What Are the Implications and Is It Worth the Effort?," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(2), pages 145-159, April.
    26. Marie Diron, 2008. "Short-term forecasts of euro area real GDP growth: an assessment of real-time performance based on vintage data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(5), pages 371-390.
    27. Mahajan, Vijay & Wind, Yoram, 1988. "New product forecasting models : Directions for research and implementation," International Journal of Forecasting, Elsevier, vol. 4(3), pages 341-358.
    28. Fader, Peter S. & Hardie, Bruce G.S., 2009. "Probability Models for Customer-Base Analysis," Journal of Interactive Marketing, Elsevier, vol. 23(1), pages 61-69.
    29. Morrison, Donald G & Schmittlein, David C, 1988. "Generalizing the NBD Model for Customer Purchases: What Are the Implications and Is It Worth the Effort? Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(2), pages 165-166, April.
    30. Danaher, Peter J. & Dagger, Tracey S. & Smith, Michael S., 2011. "Forecasting television ratings," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1215-1240, October.
    31. Kumar, V. & Nagpal, Anish & Venkatesan, Rajkumar, 2002. "Forecasting category sales and market share for wireless telephone subscribers: a combined approach," International Journal of Forecasting, Elsevier, vol. 18(4), pages 583-603.
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