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Consumer behavior clustering of food retail chains by machine learning algorithms

Published Online: Aug 8, 2021
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Analysis of the behavior of an economic agent is one of the central themes of microeconomics. Right now, with the comprehensive increase in the amount of data and the expansion of the computing capabilities of personal computers, there is a need to implement methods of behavioral economics in the study of human behavior. In the course of this study, a survey was created aimed at identification of patterns of behavior of the modern consumer according to his selection criteria stores and reactions to questions based on Behavioral Economics theorems. Clustering the obtained results were performed using machine learning algorithms, after which the Random Forest classification algorithm was trained. According to the results of Silhouette analysis, K-means clusters were selected as the main ones for further modeling. T-SNE algorithms, hierarchical and spectral analysis were used for additional visual representation. This study offers a tool for classifying customer preferences and analyzing current industry trends. A tool has been created to classify consumers of food retail chains in order to improve their "buyer's journey" and better understand their needs. The created tool for clustering and classification by machine learning methods can be used in business processes. To improve the result, it is necessary to choose a more representative sample, because used in this study consists of an average rationally thinking and knowledgeable individuals, which cannot be said of the average consumer not only among the older generation but also among the younger. Therefore, the next directions in the study may be to identify new ones behavioral trends in other industries; deepening understanding of food retail; use of geodata to improve analysis, etc. Potentially attractive the direction may be to add an assessment of the impact of network advertising on behavior consumers through semantics analysis and image recognition..
JEL Classification:
C83, C890, D120, D910
How to cite:
Liashenko, O., Kravets, T., Prokopenko, M. (2021). Consumer behavior clustering of food retail chains by machine learning algorithms. Access to science, business, innovation in digital economy, ACCESS Press, 2(3): 234-251.
  • Accuracy Score. Retrieved from: (accessed: June 2021)
  • Ahuja, Ravinder, et al. (2020). Classification and clustering algorithms of machine learning with their applications. Nature-Inspired Computation in Data Mining and Machine Learning. Springer, Cham, 225-248.
  • Balanced Accuracy Score. Retrieved from: (accessed: June 2021)
  • Balanced Accuracy Score. Retrieved from: score.html. (accessed: June 2021)
  • Barberis, N., Xiong, W. (2009) What drives the disposition effect? An analysis of a long-standing preference-based explanation, The Journal of Finance, LXIV(2).
  • Bechara, A. (2002) The somatic marker hypothesis: a neural theory of economic decision. Games and Economic Behavior, 52(2), 336-372.
  • Beerbaum, D., Puaschunder, J.M. (2018). A behavioral economics approach to digitalisation – the case of a principles-based taxonomy. Advances in Social Science, Education and Humanities Research, 211, 45-53.
  • de Arruda, T.J., de Moraes, M.B., de Araujo Querido Oliveira, E.A. (2015). Behavioral finance: a study on investments decisions, Business and Management Review Special Issue, 4(7). Retrieved from: (accessed: June 2021)
  • Della Vigna, S. (2018) Structural behavioral economics, Handbook of Behavioral Economics, vol. 1 (eds. D. Bernheim, S. DellaVigna, and D. Laibson), Elsevier.
  • Deloitte. 2021. Consumer sentiment of Ukrainians in 2020. Industry group for retail and wholesale distribution. Retrieved from: html (accessed: June 2021)
  • Dyer, J., Kolic, B. (2020). Public risk perception and emotion on Twitter during the Covid 19 pandemic. Applied Network Science, 5(99).
  • Food retail trends by foodnavigator. Retrieved from: (accessed: June 2021)
  • Google Trends in Ukraine (food retail), Retrieved from: (accessed: June 2021)
  • Hrnjic, E., Tomczak, N. (2019) Machine learning and behavioral economics for personalized choice architecture, preprint, arXiv:1907.02100v1 [econ.GN], 2019.
  • İnaç, H. (2019) A Theoretical Perspective on Behavioral Finance with Lagrangian Approach, Quantrade Journal of Complex Systems in Social Sciences, 1(1), 22-27.
  • Jabnidze, N.; Tsetskhladze, L.; Meskhidze, I. Food Security Problems for Developing Countries in the Conditions of COVID-19: Case of Georgia. Economics. Ecology. Socium 2021, 5, 8-17.
  • Johnson, E.J., Gächter, S., Herrmann, A. (2006). Exploring the nature of loss aversion. IZA Discussion Papers, Institute for the Study of Labor, 2015.
  • Kahneman, D. (2003). Maps of bounded rationality: psychology for behavioral economics. The American Economic Review, 93(5), 1449–1475.
  • Kahneman, D. (2011). Thinking, fast and slow, New York: Farrar, Straus and Giroux.
  • Kahneman, D., Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica. 47, 263-291.
  • Kolumbus, Y., Noti, G. (2019). Neural networks for predicting human interactions in repeated games. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence Main track, 392-399.
  • Lekovic, M. (2019). Behavioral portfolio theory and behavioral asset pricing model as an alternative to standard finance concepts, Economic Horizons, 21(3), 255 – 271.
  • Liaw, A., Wiener, M. (2002). Classification and regression by randomForest. R news, 2.3, 18-22.
  • Machina, M., Schmeidler, D. (1992). A more robust definition of subjective probability. Econometrica, 60(4), 745-780.
  • March, C. (2019) The behavioral economics of artificial intelligence: lessons from experiments with computer players, CESifo Working Paper, 7926, category 13: Behavioural Economics.
  • Marsh, H.W. (2005). Big Fish Little Pond Effect on Academic Self-concept: Cross-cultural and Cross-Disciplinary Generalizability, SELF Research Centre, University of Western Sydney.
  • Marsh, Herbert W., et al. (2008). The big-fish–little-pond-effect stands up to critical scrutiny: Implications for theory, methodology, and future research. Educational psychology review 20(3), 319-350.
  • McKinsey&Company, 2020. Perspectives on retail and consumer goods. Retrieved from: (accessed: June 2021)
  • MOOC «Behavioral Finance». Retrieved from: (accessed: June 2021)
  • Murtagh, F., Contreras, P. (2012). Algorithms for hierarchical clustering: an overview. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2.1, 86-97.
  • Najdenovska, I., Stojanovska, F., Gievska, S. (2018). Detecting emotions in tweets based on hybrid approach, Proceedings of the 15th Conference for Informatics and Information Technology: CIIT 2018, 20-22 Mavrovo, Macedonia, pp. 235-240. Retrieved from: (accessed: June 2021)
  • Podcast “Freakonomics Radio”. Retrieved from: (accessed: June 2021)
  • Podcast “No Stupid Questions”. Retrieved from: (accessed: June 2021)
  • Prince, E.T (2018). Risk management and behavioral finance. Financial markets, institutions and risks, 2(2), 5-21. DOI: 10.21272/fmir.2(2).5-21.2018.
  • Reyes, J.A.P., Miranda, M.R., Vera-Martinez, J. (2019). Capital structure construct: a new approach to behavioral finance. Investment Management and Financial Innovations, 16(4), 86-97.
  • Roster on Debit Cards in food retail. Retrieved from: (accessed: June 2021)
  • Roweis, S. (1998). EM algorithms for PCA and SPCA. Advances in neural information processing systems 626-632. Retrieved from: (accessed: June 2021)
  • Stefánsson, H.O., Bradley, R. (2017). What is risk aversion? The British Journal for the Philosophy of Science, 70(1), 77–102.
  • The Behavioral Economics Guide 2014. Introduction to behavioral economics. Retrieved from: (accessed: June 2021)
  • Train_test_split. Retrieved from: test_split.html. (accessed: June 2021)
  • Tversky, A. (1986). Rational choice and framing of decisions. Journal of Business, 59, 252–278.
  • Von Luxburg, U. (2007). A tutorial on spectral clustering. Statistics and computing 17.4, 395-416.
  • Wattenberg, M., Viégas, F., Johnson, I. (2016). How to use t-SNE effectively. Distill, 1(10).
  • Yadav, J. (2017). Selecting optimal number of clusters in KMeans Algorithm (Silhouette Score). Medium. Retrieved from: (accessed: June 2021)

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