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Modeling intra-seasonal heterogeneity in hourly advertising-response models: Do forecasts improve?

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  • Kiygi-Calli, Meltem
  • Weverbergh, Marcel
  • Franses, Philip Hans

Abstract

We examine the situation in which hourly data are available for designing advertising-response models, whereas managerial decision-making can concern hourly, daily or weekly intervals. A key notion is that models for higher frequency data require the intra-seasonal heterogeneity to be addressed, while models for lower frequency data are much simpler. We use three large, actual real-life datasets to analyze the relevance of these additional efforts for managerial interpretation and for the out-of-sample forecast accuracy at various frequencies.

Suggested Citation

  • Kiygi-Calli, Meltem & Weverbergh, Marcel & Franses, Philip Hans, 2017. "Modeling intra-seasonal heterogeneity in hourly advertising-response models: Do forecasts improve?," International Journal of Forecasting, Elsevier, vol. 33(1), pages 90-101.
    • Handle: RePEc:eee:intfor:v:33:y:2017:i:1:p:90-101
    DOI: 10.1016/j.ijforecast.2016.06.005
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    References listed on IDEAS

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    Cited by:

    1. Dai, Hongyan & Xiao, Qin & Chen, Songlin & Zhou, Weihua, 2023. "Data-driven demand forecast for O2O operations: An adaptive hierarchical incremental approach," International Journal of Production Economics, Elsevier, vol. 259(C).
    2. Kiygi Calli, M. & Weverbergh, M. & Franses, Ph.H.B.F., 2017. "Call center performance with direct response advertising," Econometric Institute Research Papers EI2017-04, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Kiygi-Calli, Meltem & Weverbergh, Marcel & Franses, Philip Hans, 2021. "Forecasting time-varying arrivals: Impact of direct response advertising on call center performance," Journal of Business Research, Elsevier, vol. 131(C), pages 227-240.

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