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The effectiveness of high-frequency direct-response commercials

Author

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

Abstract

To optimally schedule commercials for a car repair service, we investigate the impact of direct-response commercials on incoming calls at a national call center by using a unique hourly data set from a Belgium-based company. We address the question of whether advertising effects vary across the hours of the week and thereby provide opportunities for the company to optimize its media plan. We summarize the data with a random-effects hierarchical linear model that treats the hours within the week as a panel of 168 interrelated time series. This model highlights the intraday and intraweek heterogeneity of advertising elasticities.

Suggested Citation

  • Kiygi Calli, Meltem & Weverbergh, Marcel & Franses, Philip Hans, 2012. "The effectiveness of high-frequency direct-response commercials," International Journal of Research in Marketing, Elsevier, vol. 29(1), pages 98-109.
    • Handle: RePEc:eee:ijrema:v:29:y:2012:i:1:p:98-109
    DOI: 10.1016/j.ijresmar.2011.09.001
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    References listed on IDEAS

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    1. Prasad A. Naik & Murali K. Mantrala & Alan G. Sawyer, 1998. "Planning Media Schedules in the Presence of Dynamic Advertising Quality," Marketing Science, INFORMS, vol. 17(3), pages 214-235.
    2. Koen Pauwels & Shuba Srinivasan & Philip Hans Franses, 2007. "When Do Price Thresholds Matter in Retail Categories?," Marketing Science, INFORMS, vol. 26(1), pages 83-100, 01-02.
    3. Marnik G. Dekimpe & Dominique M. Hanssens, 1995. "The Persistence of Marketing Effects on Sales," Marketing Science, INFORMS, vol. 14(1), pages 1-21.
    4. Gerard J. Tellis & Philip Hans Franses, 2006. "Optimal Data Interval for Estimating Advertising Response," Marketing Science, INFORMS, vol. 25(3), pages 217-229, 05-06.
    5. Philip Hans Franses & Richard Paap, 2011. "Random‐coefficient periodic autoregressions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 65(1), pages 101-115, February.
    6. Jean-Pierre Dubé & Günter Hitsch & Puneet Manchanda, 2005. "An Empirical Model of Advertising Dynamics," Quantitative Marketing and Economics (QME), Springer, vol. 3(2), pages 107-144, June.
    7. Frank M. Bass & Norris Bruce & Sumit Majumdar & B. P. S. Murthi, 2007. "Wearout Effects of Different Advertising Themes: A Dynamic Bayesian Model of the Advertising-Sales Relationship," Marketing Science, INFORMS, vol. 26(2), pages 179-195, 03-04.
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    Cited by:

    1. Kosse, Anneke, 2013. "Do newspaper articles on card fraud affect debit card usage?," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5382-5391.
    2. Eckert, C. & J. Hohberger (Jan) & Franses, Ph.H.B.F., 2022. "Gaussian Copula Regression in the Presence of Thresholds," Econometric Institute Research Papers 2022-02, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. 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.
    4. 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.
    5. 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.
    6. Philip Hans Franses, 2021. "Marketing response and temporal aggregation," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(2), pages 111-117, June.

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