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Pooling and Dynamic Forgetting Effects in Multitheme Advertising: Tracking the Advertising Sales Relationship with Particle Filters

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  • Norris I. Bruce

    (School of Management, The University of Texas at Dallas, Richardson, Texas 75083)

Abstract

Firms often use a pool or series of advertising themes in their campaigns. Thus, for example, a firm may employ some of its advertising to promote price-related themes or messages and other of its advertising to promote product-related themes. This study examines the interdependence that can occur between pairs of themes in a pool (i.e., ), the impact of these pooling effects on the allocation of advertising expenditures, and the factors that can affect forgetting rates (or, conversely, carry-over rates) in a multitheme advertising environment. The study measures pooling, wear out, and forgetting (carry-over) effects for a campaign that uses five different advertising themes. To obtain these measures, I extend the linear Nerlove-Arrow (NA) (1962) model to a nonlinear model of advertising theme quality and goodwill and estimate the extended model using Markov chain Monte Carlo (MCMC) and particle filtering ideas. Particle filtering belongs to a class of sequential Monte Carlo (SMC) methods designed to estimate nonlinear/nonnormal state space models. Results show that forgetting (or carry-over) rates may be time varying and a function of prior goodwill (past advertising) and other advertising variables. Results show, moreover, that pooling effects can reduce theme wear out and, in turn, significantly improve advertising efficiency.

Suggested Citation

  • Norris I. Bruce, 2008. "Pooling and Dynamic Forgetting Effects in Multitheme Advertising: Tracking the Advertising Sales Relationship with Particle Filters," Marketing Science, INFORMS, vol. 27(4), pages 659-673, 07-08.
  • Handle: RePEc:inm:ormksc:v:27:y:2008:i:4:p:659-673
    DOI: 10.1287/mksc.1070.0317
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    3. Olivier Rubel & Prasad A. Naik, 2017. "Robust Dynamic Estimation," Marketing Science, INFORMS, vol. 36(3), pages 453-467, May.
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    7. Jason R. Blevins & Ahmed Khwaja & Nathan Yang, 2018. "Firm Expansion, Size Spillovers, and Market Dominance in Retail Chain Dynamics," Management Science, INFORMS, vol. 64(9), pages 4070-4093.
    8. Ashwin Aravindakshan & Prasad A. Naik, 2015. "Understanding the Memory Effects in Pulsing Advertising," Operations Research, INFORMS, vol. 63(1), pages 35-47, February.
    9. A. Ronald Gallant & Han Hong & Ahmed Khwaja, 2018. "The Dynamic Spillovers of Entry: An Application to the Generic Drug Industry," Management Science, INFORMS, vol. 64(3), pages 1189-1211, March.
    10. Ivan Guitart & Stefan Stremersch, 2021. "The impact of informational and emotional television ad content on online search and sales," Post-Print hal-03193729, HAL.
    11. Ceren Kolsarici & Demetrios Vakratsas, 2015. "Correcting for Misspecification in Parameter Dynamics to Improve Forecast Accuracy with Adaptively Estimated Models," Management Science, INFORMS, vol. 61(10), pages 2495-2513, October.
    12. Gaku Fukunaga & Hideki Takayasu & Misako Takayasu, 2016. "Property of Fluctuations of Sales Quantities by Product Category in Convenience Stores," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-19, June.
    13. Ashwin Aravindakshan & Prasad Naik, 2011. "How does awareness evolve when advertising stops? The role of memory," Marketing Letters, Springer, vol. 22(3), pages 315-326, September.
    14. Guler, Ali Umut, 2023. "Category expansion through cross-channel demand spillovers," International Journal of Research in Marketing, Elsevier, vol. 40(3), pages 629-658.
    15. Mitsukuni Nishida & Nathan Yang, 2014. "Better Together? Retail Chain Performance Dynamics in Store Expansion Before and After Mergers," Working Papers 14-08, NET Institute.
    16. Kim, Ho & Bruce, Norris I., 2018. "Should sequels differ from original movies in pre-launch advertising schedule? Lessons from consumers' online search activity," International Journal of Research in Marketing, Elsevier, vol. 35(1), pages 116-143.

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