Ryan Dew, Nicolas Padilla, Anya Shchetkina
arXiv 14 Aug 2024 · Econometrics · 3 citations (OpenAlex)
arXiv:2408.07678 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Jin, Y., Wang, Y., Sun, Y., Chan, D., and Koehler, J (2017) Bayesian Methods for Media Mix Modeling with Carryover and Shape Effects | 1.000 | 7 | 4 | 100% |
| 2 | Palda, K. S (1965) The Measurement of Cumulative Advertising Effects | 1.000 | 7 | 3 | 100% |
| 3 | Meta (2024) Robyn MMM | 1.000 | 5 | 4 | 100% |
| 4 | Bass, F. M. and Clarke, D. G (1972) Testing Distributed Lag Models of Advertising Effect | 1.000 | 5 | 3 | 100% |
| 5 | Dew, R., Ansari, A., and Li, Y (2020) Modeling Dynamic Heterogeneity Using Gaussian Processes self | 1.000 | 5 | 3 | 100% |
| 6 | Winer, R. S (1979) An Analysis of the Time-Varying Effects of Advertising: The Case of Lydia Pinkham | 1.000 | 5 | 3 | 100% |
| 7 | Google (2024) Meridian | 0.928 | 4 | 3 | 100% |
| 8 | Ng, E., Wang, Z., and Dai, A (2021) Bayesian Time Varying Coefficient Model with Applications to Marketing Mix Modeling | 0.928 | 4 | 3 | 100% |
| 9 | Bultez, A. V. and Naert, P. A (1979) Does Lag Structure Really Matter in Optimizing Advertising Expenditures? | 0.874 | 5 | 2 | 100% |
| 10 | Hanssens, D. M., Parsons, L. J., and Schultz, R. L (2003) Market Response Models: Econometric and Time Series Analysis | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 62 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
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| 1 | Blind targeting: Personalization under Third-Party Privacy Constraints | 0.405 | 1 | 1 |