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A statistical method for estimating piecewise linear sales trends

Author

Listed:
  • Taku Moriyama

    (Tottori University)

  • Masashi Kuwano

    (Tottori University)

  • Masahito Nakayama

    (Tottori University)

Abstract

Due to the structural breaks in time series, estimated current trend of product sales differs between the case where data for the entire span are used and the case where only the most recent data are used. The purpose of this study is to establish the piecewise linear approximation (PLA) as a trend analysis method that accounts for the structural breaks. PLA uses the complete data to simultaneously estimate the breakpoints and the continuously connected trends, immediately before and after the break. Thus, PLA not only ensures the ease of interpretation of the results, but also eliminates the probability of using discretion by uniquely determining the current trend, making the estimated result reliable. The case study demonstrates the proposition that, several products’ sales trends and the necessity of determining an appropriate time span for data analysis, underwent changes at least once. The method’s validity is demonstrated by showing the changes of the sales trends immediately after analyzed store’s renovation. Data collection from the time period immediately prior to a structural break can help identify the factors changing the product sales.

Suggested Citation

  • Taku Moriyama & Masashi Kuwano & Masahito Nakayama, 2024. "A statistical method for estimating piecewise linear sales trends," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 436-444, June.
  • Handle: RePEc:pal:jmarka:v:12:y:2024:i:2:d:10.1057_s41270-023-00207-9
    DOI: 10.1057/s41270-023-00207-9
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    References listed on IDEAS

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    1. Tomohiro Ando, 2008. "Measuring the baseline sales and the promotion effect for incense products: a Bayesian state-space modeling approach," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 60(4), pages 763-780, December.
    2. Polli, Rolando & Cook, Victor, 1969. "Validity of the Product Life Cycle," The Journal of Business, University of Chicago Press, vol. 42(4), pages 385-400, October.
    3. 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.
    4. Maheu, John M & McCurdy, Thomas H, 2000. "Identifying Bull and Bear Markets in Stock Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 100-112, January.
    5. Koen Pauwels & Dominique M. Hanssens, 2007. "Performance Regimes and Marketing Policy Shifts," Marketing Science, INFORMS, vol. 26(3), pages 293-311, 05-06.
    6. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    7. Sungho Park & Sachin Gupta, 2011. "A Regime-Switching Model of Cyclical Category Buying," Marketing Science, INFORMS, vol. 30(3), pages 469-480, 05-06.
    8. Yossi Hadad & Baruch Keren & Gregory Gurevich, 2017. "Improving demand forecasting using change point analysis," International Journal of Business Forecasting and Marketing Intelligence, Inderscience Enterprises Ltd, vol. 3(2), pages 130-151.
    9. Marnik G. Dekimpe & Dominique M. Hanssens, 1995. "Empirical Generalizations About Market Evolution and Stationarity," Marketing Science, INFORMS, vol. 14(3_supplem), pages 109-121.
    Full references (including those not matched with items on IDEAS)

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