Modeling and restoration of customer preferences

Yu. A. Korablev

News of the Kabardin-Balkar scientific center of RAS, no. 3 (2020), pp. 74-91.

Résumé

This article proposes an approach to restore the dynamics of changes in consumer preferences between two alternative products from the rare events data associated with the purchases of these goods, based on the capacity method of rare events analysis. The main idea of the capacity method, in which rare events are analyzed from the point of view of the processes occurring inside the sources of events, is briefly described. Consumption processes, which are the most common in the economy during the events formation, can be modeled as processes of emptying / filling capacity. This allows to restore the change rate of this capacity level (the rate of products stock change) using the mathematical method of restoring the function by integrals. Four variants for the events formation with the replenishment of two alternative products stocks are considered. For each variant, suggestions on how to restore the changes dynamics in consumer preferences between two alternative products are presented. For the variant in which the consumer replenishes the stocks of both goods when any of the goods stock runs out, it is necessary to first perform the recognition task and determine whether the event is formed by end of stock of the first or second product. Recommendations for the implementation of this recognition are given. The restoration examples of the change dynamic in preference between two alternative products are shown.

Détail

Keywords: rare events, capacitive method, consumption rate, recovery, preference, alternative products.

Bibliographie

[1] J. D. Croston, “Forecasting and stock control for intermittent demands”, Operational Research Quarterly (1970-1977), 23(3) (1972), 289–303 | DOI | Zbl

[2] F. R. Johnston, J. E. Boylan, “Forecasting intermittent demand: a comparative evaluation of Croston's method”, Comment. International journal of forecasting, 12(2) (1996), 297–298 | DOI

[3] N. S. Altman, “An introduction to kernel and nearest-neighbor nonparametric regression”, The American Statistician, 46(3) (1992), 175–185 | MR

[4] T. Cove, P. Har, “Nearest neighbor pattern classification”, IEEE Transactions on Information Theory, 13(1) (1967), 21–27 | DOI | Zbl

[5] S. H. Walker, D. B. Duncan, “Estimation of the probability of an event as a function of several independent variables”, Biometrika, 54 (1/2) (1967), 167–178 | DOI | MR

[6] B. Efron, R. J. Tibshirani, An introduction of the Bootstrap, Chapman Hall, New York, 1993 | MR

[7] T. R. Willemain, D. S. Park, Y. B. Kim, K. I. Shin, “Simulation output analysis using the thresh-old bootstrap”, European Journal of Operational Research, 134(1) (2001), 17–28 | DOI | MR | Zbl

[8] E. S. Ventzel, L. A. Ovcharov, The theory of random processes and its engineering applications, Textbook, allowance for technical colleges, 2nd ed., Higher school, M., 2000, 383 pp.

[9] Yu. A. Korablev, “Yemkostnyy metod opredeleniya funktsii skorosti potrebleniya”, Capacitive method for determining the consumption rate function, 2015, no. 15 (1.1), 140–150, Scientific Book «Publishing House», Voronezh

[10] Yu. A. Korablev, “The error of the capacitive method of analysis of rare events, remoteness from the end consumer”, News of the Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences, 2019, no. 3 (89), 48–77, Nalchik

[11] Yu. A. Korablev, “A capacitive method for the analysis of rare events in the trade in various goods”, Business. Education. Law. Bulletin of the Volgograd Institute of Business, 2019, no. 3 (48), 121–131

[12] Yu. A. Korablev, “Analysis of the causes and error estimation of anomalous pictures in the capacitive method of analysis of rare events”, Economics and management: problems, solutions, 2017, no. 8 (6), 8–12, Scientific library, M.

[13] J. Bowersox Donald, J. Kloss David, Logistics: Integrated Supply Chain, 2-ye izd, eds. Transl. from English N.N. Baryshnikova, B.S. Pinsker, Olymp-Business CJSC, M., 2008, 640 pp.

[14] Yu. A. Korablev, “A method of recovering a function by integrals for analysis and forecasting of rare events in the economy”, Economics and Mathematical Methods, 2020, no. 3 (in press) | Zbl

[15] S. Barbera, P. J. Hammond, S. Christian, Handbook of utility theory, v. 1, Kluwer academic Publishers, 1998

[16] A. G. Vershinina, A. E. Holodkova, “Consumer preferences, as a basis for the formation of an assortment line of flour confectionery”, Azimuth of Scientific Research: Economics and Management, 8:3 (28) (2019), 105–108

[17] K. F. Mirnaya, E. O. Ermolaeva, V. M. Pozdnyakovsky, “Analysis of consumer preferences when choosing smoked-boiled poultry meat rolls”, University News, Food Technology, 2015, no. 1, 113–115

[18] C. Y. Ng, K. M.Y. Law, “Investigating consumer preferences on product designs by analyzing opinions from social networks using evidential reasoning”, Computers and Industrial Engineering, 139 (2020) | Zbl