Reconstructing systematic persistent impacts of promotional marketing with empirical nonlinear dynamics

English version

Autoři: Ray Huffaker ^aff001; Andrew Fearne ^aff002
Působiště autorů: Department of Agricultural and Biological Engineering, University of Florida, Gainesville, Florida, United States of America ^aff001; Norwich Business School, University of East Anglia, Norwich, England, United Kingdom ^aff002
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0221167

Souhrn

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.

Klíčová slova:

Biology and life sciences – Organisms – Eukaryota – Physical sciences – Engineering and technology – Animals – Social sciences – Sociology – Communications – Computer and information sciences – Mathematics – Vertebrates – Amniotes – Mammals – Statistics – Marketing – Physics – Statistical data – Leporids – Hares – Cats – Signal processing – Systems science – Nonlinear dynamics – Thermodynamics – Entropy – Dynamical systems – Lynx

Zdroje

1. Gedenik K, Neslin S, Ailawadi K. Sales Promotion. 2nd ed. Krafft M, Mantrala M, editors. New York: Springer; 2010.

2. Dodson J, Tybout A, Sternthal B. Impact of deals and deal retraction on brand switching. Journal of Marketing Research. 1978;15:72–81.

3. Del Vecchio D, Henard D, Freling T. The effect of sales promotion on post-promotion brand preference: A meta-analysis. Journal of Retailing. 2006;82(3):203–13.

4. Blattberg R, Briesch R, Fox E. How promotions work. Marketing Science. 1995;14(3):G122–G32.

5. Bass F, Clarke D. Testing distributed lag models of advertising effect. Journal of Marketing Research. 1972;9:298–308.

6. Dekimpe M, Hanssens D. The persistence of marketing effects on sales. Marketing Science. 1995;14(1):1–21.

7. Valera H, Lee J. Do rice prices follow a random walk? Evidence from Markov switching unit root tests for Asian markets. Agicultural Economics. 2016;47(6):683–95.

8. Economist The. Big economic ideas. 2016a;July 23.

9. Economist The. If economists reformed themselves. 2016b;May 16.

10. Galtier F. Managing food price instability: Critical assessment of the dominant doctrine. Global Food Security. 2013;2:72–81.

11. Ellenberg J. How Not to Be Wrong: The Power of Mathematical Thinking. New York: Penguin Books; 2015.

12. Takens F. Detecting strange attractors in turbulence. In: Rand D, Young L., editor. Dynamical Systems and Turbulence New York: Springer; 1980. p. 366–81.

13. Kantz H, Schreiber T. Nonlinear Time Series Anaysis. Cambridge: Cambridge University Press; 1997.

14. Huffaker R, Bittelli M, Rosa R. Nonlinear Time Series Analysis with R. Oxford, U.K.: Oxford University Press; 2017.

15. Sugihara G, May R, Hao Y, Chih-hao H, Deyle E, Fogarty M, et al. Detecting causality in complex ecosystems. Science. 2012;338:496–500. doi: 10.1126/science.1227079 22997134

16. Deyle E, May R, Munch S, Sugihara G. Tracking and forecasting ecosystem interactions in real time. Proc R Soc B. 2018;283:201522358.

17. Gabaix X, Laibson D. The Seven Properties of Good Models. In: Caplin A, Schotter A, editors. The Foundations of Positive and Normative Economics: A Handbook. Oxford: Oxford University Press; 2008.

18. Dhrymes P, Howrey E, Hymans S, Kmenta J, Leamer E, Quandt R, et al. Criteria for evaluation of econometric models. Annals of Economic and Social Measurement. 1972;1(3):291–324.

19. Oreskes N, Shrader-Frechette K, Belitz K. Verification, validation, and confirmation of numerical models in the earth sciences. Science. 1994;263:641–6. doi: 10.1126/science.263.5147.641 17747657

20. Rykiel E. Testing ecological models: the meaning of validation. Ecological Modeling. 1991;90:229–44.

21. Feder G. Pesticides, information, and pest management under uncertainty. American Journal of Agricultural Economics. 1979;61(February):97–103.

22. Golyandina N, Nekrutkin V, Zhigljavsky A. Analysis of Time Series Structure. New York: Chapman & Hall/CRC; 2001.

23. Elsner J, Tsonsis A. Singular Spectrum Analysis. New York: Plenum Press; 2010.

24. Hassani H. Singular spectrum analysis: Methodology and comparison. Journal of Data Science. 2007;5:239–57.

25. Kaplan D, Glass L. Understanding Nonlinear Dynamics. New York: Springer; 1995.

26. Schreiber T. Detecting and analyzing nonstationarity in a time series with nonlinear cross predictions. Phys Rev Lett. 1997;78:843–6.

27. Itoh N, Marwan N. An extended singular spectrum analysis transformation (SST) for the investigation of Kenyan precipitation data. Nonlinear Processes in Geophysics. 2013;20:467–81.

28. Moskvina V, Zhigljavsky A. An algorithm based on singular spectrum analysis for change-point detection. Communications in Statistics. 2003;32:319–53.

29. Theiler J, Eubank S, Longtin A, Galdrikian B, Farmer J. Testing for nonlinearity in time series: The method of surrogate data. Physica D. 1992;58:77–94.

30. Schreiber T, Schmitz A. Surrogate time series. Physica D. 2000;142:346–82.

31. Sprott J. Chaos and Time Series Analysis. Oxford: Oxford University Press; 2003.

32. Brown T. Measuring Chaos Using the Lyapunov Exponent. In: Kiel E, Elliott E, editors. Chaos Theory in the Social Sciences: Foundations and Applications. Michigan: University of Michigan; 1996. p. 53–66.

33. Theiler J. Estimating the fractal dimension of chaotic time series. Lincoln Laboratory Journal. 1990;3:63–86.

34. Odum E. Fundamentals of Ecology. Philadelphia: W. B. Saunders; 1953.

35. Breeden J, Hubler A. Reconstructing equations of motion from experimental data with unobserved variables. Physica Review A. 1990;42(10):5817–26.

36. Williams G. Chaos Theory Tamed. Washington D.C.: John Henry Press; 1997.

37. Theiler J. Spurious dimension from correlation algorithms applied to limited time series data. Phys Rev A. 1986;34:2427–32.

38. Deyle E, Sugihara G. Generalized Theorems for Nonlinear State Space Reconstruction. PLoS One. 2011;6(3):1–8.

39. Gotoda H, Kobayashi H. Chaotic dynamics of a swirling flame front instability generated by a change in gravitational orientation. Physical Review E. 2017;95:022201. doi: 10.1103/PhysRevE.95.022201 28297884

40. Brandt C, Pompe B. Permutation entropy: a natural complexity measure for time series. Phys Rev Lett. 2012;88:174102.

41. Anderson C, Sugihara G. Simplex projection (downloaded July 12, 2018, http://deepeco.ucsd/simplex/) 2018.

42. Golyandina N, Korobeynikov A. Basic singular spectrum analysis and forecasting with R. Computational Statistics and Data Analysis. 2014;71:934–54.

43. Di Narzo A, Di Narzo F. tseriesChaos: analysis of nonlinear time series. Retrieved from https://cranr-projectorg/package=tseriesChaos. 2013.

44. Constantine W, Percival D. fractal: Fractal time series modeling and analysis. Retrieved from https://cranr-projectorg/package=fractal. 2014.

45. Furrer R, Paige J, Sain S. Tools for spatial data. Retrieved from https://cranr-projectorg/package=fields. 2018.

46. Csardi G, Nepusz T. The igraph software package for complex network research. Retrieved from https://igraphorg. 2006.

47. Ye H, Deyle E, Munch S. Applications of empirical dynamic modeling from time series. Retrieved from https://cranr-projectorg/package=rEDM. 2018.

48. Origin. Version 2019, OriginLab Corporation. Northampton, MA, USA.

49. Gammelgaard J, Dorrenbacher C. Introduction. In: Gammelgaard J, Dorrenbacher C, editors. The Global Brewery Industry. Northampton, Massachusetts: Edward Elgar Publishing; 2013. p. 1–17.

50. Vautard R. Patterns in time: SSA and MSSA in analysis of climate. In: von Storch H, Navarra A, editors. Analysis of Climate Variability: Springer; 1999.

51. Ghil M, Allen M, Dettinger M, Ide K, Kondrashov D, Mann M, et al. Advanced spectral methods for climatic time series. Reviews of Geophysics. 2002;40(1):1–41.

Reconstructing systematic persistent impacts of promotional marketing with empirical nonlinear dynamics

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