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Modeling the effectiveness of hourly direct-response radio commercials

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  • KIYGI CALLI, Meltem
  • WEVERBERGH, Marcel
  • FRANSES, Philip Hans

Abstract

The authors investigate the impact of direct-response commercials on incoming calls at a national call center. To this end, the authors analyze the data of a fast service for repairs of (parts of) a durable consumption good in Flanders, Belgium. The authors have access to data at the 15 minute interval covering 30 months in which 5172 radio commercials were broadcasted on six radio stations at various times of the day and at with differing commercial lengths. Their model is a two-level model, where the first-level estimates of the short-run and long-run effects are correlated with various aspects of the commercial in the second level. Their main conclusion is that GRPs are the key drivers of the effectiveness of commercials.

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Bibliographic Info

Paper provided by University of Antwerp, Faculty of Applied Economics in its series Working Papers with number 2008005.

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Length: 53 pages
Date of creation: Apr 2008
Date of revision:
Handle: RePEc:ant:wpaper:2008005

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Postal: Prinsstraat 13, B-2000 Antwerpen
Web page: https://www.uantwerp.be/en/faculties/applied-economic-sciences/
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Keywords: Advertising effectiveness; Two-level model; Advertising response; Long-run elasticity; Short-run effects;

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References

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  1. Dekimpe, M.G. & Hanssens, D., 1995. "The persistence of marketing effects on sales," Open Access publications from Tilburg University urn:nbn:nl:ui:12-358916, Tilburg University.
  2. Richard Paap & Philip Hans Franses, 2000. "A dynamic multinomial probit model for brand choice with different long-run and short-run effects of marketing-mix variables," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 717-744.
  3. 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.
  4. 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, 02.
  5. Baghestani, Hamid, 1991. "Cointegration Analysis of the Advertising-Sales Relationship," Journal of Industrial Economics, Wiley Blackwell, vol. 39(6), pages 671-81, December.
  6. Marnik G. Dekimpe & Dominique M. Hanssens, 1995. "The Persistence of Marketing Effects on Sales," Marketing Science, INFORMS, vol. 14(1), pages 1-21.
  7. Fok, D. & Paap, R. & Horváth, C. & Franses, Ph.H.B.F., 2005. "A Hierarchical Bayes Error Correction Model to Explain Dynamic Effects of Price Changes," ERIM Report Series Research in Management ERS-2005-047-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus Uni.
  8. Kamel Jedidi & Carl F. Mela & Sunil Gupta, 1999. "Managing Advertising and Promotion for Long-Run Profitability," Marketing Science, INFORMS, vol. 18(1), pages 1-22.
  9. Robert P. Leone, 1995. "Generalizing What Is Known About Temporal Aggregation and Advertising Carryover," Marketing Science, INFORMS, vol. 14(3_supplem), pages G141-G150.
  10. Shuba Srinivasan & Koen Pauwels & Dominique M. Hanssens & Marnik G. Dekimpe, 2004. "Do Promotions Benefit Manufacturers, Retailers, or Both?," Management Science, INFORMS, vol. 50(5), pages 617-629, May.
  11. Tülin Erdem, 1996. "A Dynamic Analysis of Market Structure Based on Panel Data," Marketing Science, INFORMS, vol. 15(4), pages 359-378.
  12. Koen Pauwels & Shuba Srinivasan, 2004. "Who Benefits from Store Brand Entry?," Marketing Science, INFORMS, vol. 23(3), pages 364-390, July.
  13. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
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