A Data Mining Approach to Consumers’ Choice of Retail Market: The Case of Urban Retail Markets in Iran

مجله اقتصاد و توسعه کشاورزی

Article 1, Volume 38, Issue 4 - Serial Number 65, January 2025, Pages 370-351   

Document Type: Research Article-en

DOI: 10.22067/jead.2024.88151.1269

Authors

H. Hashemzadeh; N. Yousefian; S. Esfandiari Bahraseman; A. Karbasi; A. Firoozzare^*

Department of Agricultural Economics, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

Abstract

Urban retail markets are state-owned retail markets that were recently established in Iran to increase the welfare of consumers and producers. To achieve this goal and expand its presence in the Iranian retail sector, it is essential to gain a comprehensive understanding of consumer behavior within these markets. This study examines the various socio-economic factors influencing consumers' decisions in the retail market by using the C4.5 algorithm. The data were collected using a random sampling method through a survey of 189 consumers, focusing on the population of Mashhad, Iran, during 2019-2020. Results revealed that awareness of available discounts significantly drives consumer choices in urban retail markets. Despite existing discounts, awareness among consumers remains low, suggesting a need to review promotional strategies within the marketing mix. The study also identifies previous purchases from urban markets, household income, and education as influential factors. Findings offer valuable insights for policymakers, market strategists, and stakeholders seeking to enhance the effectiveness of local retail markets in Iran. By leveraging insights into consumer behavior and market dynamics, these markets can thrive, benefiting Iran's retail sector and overall economy. Following the study, recommendations such as enhanced promotional campaigns, education-oriented strategies, loyalty programs, collaborations with local producers, and inclusive marketing policies was made aim to improve access for all consumers to urban retail markets.

Keywords

Consumer behavior; Data mining; Decision tree; Machine learning

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