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Your MMM is Broken: Identification of Nonlinear and Time-varying Effects in Marketing Mix Models

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  • Ryan Dew
  • Nicolas Padilla
  • Anya Shchetkina

Abstract

Recent years have seen a resurgence in interest in marketing mix models (MMMs), which are aggregate-level models of marketing effectiveness. Often these models incorporate nonlinear effects, and either implicitly or explicitly assume that marketing effectiveness varies over time. In this paper, we show that nonlinear and time-varying effects are often not identifiable from standard marketing mix data: while certain data patterns may be suggestive of nonlinear effects, such patterns may also emerge under simpler models that incorporate dynamics in marketing effectiveness. This lack of identification is problematic because nonlinearities and dynamics suggest fundamentally different optimal marketing allocations. We examine this identification issue through theory and simulations, wherein we explore the exact conditions under which conflation between the two types of models is likely to occur. In doing so, we introduce a flexible Bayesian nonparametric model that allows us to both flexibly simulate and estimate different data-generating processes. We show that conflating the two types of effects is especially likely in the presence of autocorrelated marketing variables, which are common in practice, especially given the widespread use of stock variables to capture long-run effects of advertising. We illustrate these ideas through numerous empirical applications to real-world marketing mix data, showing the prevalence of the conflation issue in practice. Finally, we show how marketers can avoid this conflation, by designing experiments that strategically manipulate spending in ways that pin down model form.

Suggested Citation

  • Ryan Dew & Nicolas Padilla & Anya Shchetkina, 2024. "Your MMM is Broken: Identification of Nonlinear and Time-varying Effects in Marketing Mix Models," Papers 2408.07678, arXiv.org.
  • Handle: RePEc:arx:papers:2408.07678
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    References listed on IDEAS

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    1. Guhl, Daniel & Baumgartner, Bernhard & Kneib, Thomas & Steiner, Winfried J., 2018. "Estimating time-varying parameters in brand choice models: A semiparametric approach," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 394-414.
    2. Brett R. Gordon & Robert Moakler & Florian Zettelmeyer, 2023. "Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement," Marketing Science, INFORMS, vol. 42(4), pages 768-793, July.
    3. S. Sriram & Pradeep K. Chintagunta & Ramya Neelamegham, 2006. "Effects of Brand Preference, Product Attributes, and Marketing Mix Variables in Technology Product Markets," Marketing Science, INFORMS, vol. 25(5), pages 440-456, September.
    4. 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.
    5. Jin Gyo Kim & Ulrich Menzefricke & Fred M. Feinberg, 2007. "Capturing Flexible Heterogeneous Utility Curves: A Bayesian Spline Approach," Management Science, INFORMS, vol. 53(2), pages 340-354, February.
    6. 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.
    7. Winer, Russell S, 1979. "An Analysis of the Time-varying Effects of Advertising: The Case of Lydia Pinkham," The Journal of Business, University of Chicago Press, vol. 52(4), pages 563-576, October.
    8. Ron Berman, 2018. "Beyond the Last Touch: Attribution in Online Advertising," Marketing Science, INFORMS, vol. 37(5), pages 771-792, September.
    9. Shin Oblander & Daniel Minh McCarthy, 2023. "Frontiers: Estimating the Long-Term Impact of Major Events on Consumption Patterns: Evidence from COVID-19," Marketing Science, INFORMS, vol. 42(5), pages 839-852, September.
    10. John C. Liechty & Duncan K. H. Fong & Wayne S. DeSarbo, 2005. "Dynamic Models Incorporating Individual Heterogeneity: Utility Evolution in Conjoint Analysis," Marketing Science, INFORMS, vol. 24(2), pages 285-293, November.
    11. Danny Klinenberg, 2023. "Synthetic Control with Time Varying Coefficients A State Space Approach with Bayesian Shrinkage," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1065-1076, October.
    12. Hongshuang (Alice) Li & P. K. Kannan & Siva Viswanathan & Abhishek Pani, 2016. "Attribution Strategies and Return on Keyword Investment in Paid Search Advertising," Marketing Science, INFORMS, vol. 35(6), pages 831-848, November.
    13. Fred M. Feinberg, 1992. "Pulsing Policies for Aggregate Advertising Models," Marketing Science, INFORMS, vol. 11(3), pages 221-234.
    14. Kim, Jin Gyo & Menzefricke, Ulrich & Feinberg, Fred M., 2005. "Modeling Parametric Evolution in a Random Utility Framework," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 282-294, July.
    15. Koen Pauwels & Imran Currim & Marnik Dekimpe & Dominique Hanssens & Natalie Mizik & Eric Ghysels & Prasad Naik, 2004. "Modeling Marketing Dynamics by Time Series Econometrics," Marketing Letters, Springer, vol. 15(4), pages 167-183, December.
    16. Liye Ma & Baohong Sun & Sunder Kekre, 2015. "The Squeaky Wheel Gets the Grease—An Empirical Analysis of Customer Voice and Firm Intervention on Twitter," Marketing Science, INFORMS, vol. 34(5), pages 627-645, September.
    17. Demetrios Vakratsas & Fred M. Feinberg & Frank M. Bass & Gurumurthy Kalyanaram, 2004. "The Shape of Advertising Response Functions Revisited: A Model of Dynamic Probabilistic Thresholds," Marketing Science, INFORMS, vol. 23(1), pages 109-119, April.
    18. Min Tian & Paul R. Hoban & Neeraj Arora, 2024. "What Cookie-Based Advertising Effectiveness Fails to Measure," Marketing Science, INFORMS, vol. 43(2), pages 407-418, March.
    19. Mohamed Lachaab & Asim Ansari & Kamel Jedidi & Abdelwahed Trabelsi, 2006. "Modeling preference evolution in discrete choice models: A Bayesian state-space approach," Quantitative Marketing and Economics (QME), Springer, vol. 4(1), pages 57-81, March.
    20. Brezger, Andreas & Steiner, Winfried J., 2008. "Monotonic Regression Based on Bayesian PSplines: An Application to Estimating Price Response Functions From Store-Level Scanner Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 90-104, January.
    21. Don H. Mann, 1975. "Optimal Theoretic Advertising Stock Models: A Generalization Incorporating the Effects of Delayed Response from Promotional Expenditure," Management Science, INFORMS, vol. 21(7), pages 823-832, March.
    22. M. L. Vidale & H. B. Wolfe, 1957. "An Operations-Research Study of Sales Response to Advertising," Operations Research, INFORMS, vol. 5(3), pages 370-381, June.
    23. Ryan Dew & Yuhao Fan, 2021. "Correlated Dynamics in Marketing Sensitivities," Papers 2104.11702, arXiv.org, revised May 2024.
    24. Petr Mariel, 2005. "Nonparametric Estimation of the Effects of Advertising: The Case of Lydia Pinkham," The Journal of Business, University of Chicago Press, vol. 78(2), pages 649-674, March.
    25. 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.
    26. Alain V. Bultez & Philippe A. Naert, 1979. "Does Lag Structure Really Matter in Optimizing Advertising Expenditures?," Management Science, INFORMS, vol. 25(5), pages 454-465, May.
    27. Wildt, Albert R & Winer, Russell S, 1983. "Modeling and Estimation in Changing Market Environments," The Journal of Business, University of Chicago Press, vol. 56(3), pages 365-388, July.
    28. Daniel Zantedeschi & Eleanor McDonnell Feit & Eric T. Bradlow, 2017. "Measuring Multichannel Advertising Response," Management Science, INFORMS, vol. 63(8), pages 2706-2728, August.
    29. Bradley T. Shapiro & Günter J. Hitsch & Anna E. Tuchman, 2021. "TV Advertising Effectiveness and Profitability: Generalizable Results From 288 Brands," Econometrica, Econometric Society, vol. 89(4), pages 1855-1879, July.
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