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Multiple intervention analysis with application to sales promotion data

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  • Y. Eric Shao

Abstract

The sales promotion data resulting from multiple marketing strategies are usually autocorrelated. Consequently, the characteristics of those data sets can be analyzed using time-series and/or intervention analysis. Traditional time-series intervention analysis focuses on the effects of single or few interventions, and forecasts may be obtained as long as the future interventions can be assured. This study is different from traditional approaches, and considers the cases in which multiple interventions and the uncertainty of future interventions exist in the system. In addition, this study utilizes a set of real sales promotion data to demonstrate the effectiveness of the proposed approach.

Suggested Citation

  • Y. Eric Shao, 1997. "Multiple intervention analysis with application to sales promotion data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 24(2), pages 181-192.
    • Handle: RePEc:taf:japsta:v:24:y:1997:i:2:p:181-192
    DOI: 10.1080/02664769723792
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    Cited by:

    1. Yuehjen Shao & Yue-Fa Lin & Soe-Tsyr Yuan, 1999. "Integrated application of time series multiple-interventions analysis and knowledge-based reasoning," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(6), pages 755-766.
    2. Ray, Mrinmoy & Rai, Anil & Singh, K.N. & V., Ramasubramanian & Kumar, Amrender, 2017. "Technology forecasting using time series intervention based trend impact analysis for wheat yield scenario in India," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 128-133.
    3. Jin-Hong Park, 2012. "Nonparametric approach to intervention time series modeling," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(7), pages 1397-1408, December.

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