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Reconstructing systematic persistent impacts of promotional marketing with empirical nonlinear dynamics

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  • Ray Huffaker
  • Andrew Fearne

Abstract

An empirical question of long-standing interest is how price promotions affect a brand’s sale shares in the fast-moving consumer-goods market. We investigated this question with concurrent promotions and sales records of specialty beer brands pooled over Tesco stores in the UK. Most brands were continuously promoted, rendering infeasible a conventional approach of establishing impact against an off-promotion sales baseline, and arguing in favor of a dynamics approach. Moreover, promotion/sales records were volatile without easily-discernable regularity. Past work conventionally attributed volatility to the impact of exogenous random shocks on stable markets, and reasoned that promotions have only an ephemeral impact on sales shares in stationary mean-reverting stochastic markets, or a persistent freely-wandering impact in nonstationary markets. We applied new empirical methods from the applied sciences to uncover an overlooked alternative: ‘systematic persistence’ in which promotional impacts evolve systematically in an endogenously-unstable market governed by deterministic-nonlinear dynamics. We reconstructed real-world market dynamics from the Tesco dataset, and detected deterministic-nonlinear market dynamics. We used reconstructed market dynamics to identify a complex network of systematic interactions between promotions and sales shares among competing brands, and quantified/characterized the dynamics of these interactions. For the majority of weeks in the study, we found that: (1) A brand’s promotions drove down own sales shares (a possibility recognized in the literature), but ‘cannibalized’ sales shares of competing brands (perhaps explaining why brands were promoted despite a negative marginal impact on own sales shares); and (2) Competitive interactions between brands owned by the same multinational brewery differed from those with outside brands. In particular, brands owned by the same brewery enjoyed a ‘mutually-beneficial’ relationship in which an incremental increase in the sales share of one marginally increased the sales share of the other. Alternatively, the sales shares of brands owned by different breweries preyed on each other’s market shares.

Suggested Citation

  • Ray Huffaker & Andrew Fearne, 2019. "Reconstructing systematic persistent impacts of promotional marketing with empirical nonlinear dynamics," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-28, September.
  • Handle: RePEc:plo:pone00:0221167
    DOI: 10.1371/journal.pone.0221167
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    References listed on IDEAS

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    1. Golyandina, Nina & Korobeynikov, Anton, 2014. "Basic Singular Spectrum Analysis and forecasting with R," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 934-954.
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    Cited by:

    1. Cole, Matthew T. & McCullough, Michael, 2023. "California beer price posting: An exploratory analysis of pricing along the supply chain," Journal of Wine Economics, Cambridge University Press, vol. 18(3), pages 205-225, August.
    2. Ray Huffaker & Garry Griffith & Charles Dambui & Maurizio Canavari, 2021. "Empirical Detection and Quantification of Price Transmission in Endogenously Unstable Markets: The Case of the Global–Domestic Coffee Supply Chain in Papua New Guinea," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    3. Haydar Demirhan, 2020. "dLagM: An R package for distributed lag models and ARDL bounds testing," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-23, February.
    4. Andrés Martínez & Alfonso Salafranca & Ana E. Sipols & Clara Simon Blas & Daniel Hengel, 2024. "Distributed lags using elastic-net regularization for market response models: focus on predictive and explanatory capacity," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 417-435, June.

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