Analyzing Fresh Food Customer Loyalty: A Clustering and Ordinal Regression Approach

Document Type : Original Research

Authors
Iman Radipour Kavkani
Roya Eshraghi Samani
Bita Rahimi Badr
Department of Agricultural Economics, Ka.C, Islamic Azad University, Karaj, Islamic Republic of Iran.
10.48311/jast.2026.16865
Abstract
This study investigates customer loyalty in Iran's chain stores, with a particular emphasis on fresh food consumers. The research utilizes a combination of K-means clustering and a weighted Recency, Frequency, Monetary (RFM) model, and ordinal logistic regression to analyze customer behavior. Using real transaction data from 9,014 customers alongside questionnaire responses, the analysis categorizes customers into four distinct groups: very loyal, loyal, at-risk, and disloyal. The weighted RFM model indicates that recency is the most significant predictor of loyalty. Further, the ordinal logistic regression identifies several key factors influencing loyalty: age, marital status, income level, perceived food quality, preference for modern stores, and brand image. These all have positive effect on loyalty; on the contrary, the importance of price and a preference for packaging-free products negatively impact loyalty. These findings provide actionable insights for retail managers, enabling them to develop cluster-specific strategies that enhance customer loyalty and strengthen competitiveness in Iran’s dynamic retail sector.


Keywords
Chain retailing
Customer segmentation
Purchase behavior
Subjects
1.       Ailobhio, D. T. and. Ikughur, J. A. 2024. A Review of Some Goodness-of-Fit Tests for Logistic Regression Model. Asian J. Prob. Stat., 26(7): 75-85.
2.       Akbar, M. A. 2024. Customer-Centric Strategies: Navigating the Dynamics of Marketing Management for Competitive Advantage. ABIM, 2(2): 96–109.
3.       Akinwande, M., Dikko, H. and Samson, A. (2015) Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis. Open J. Stat., 5: 754-767.
4.       Alves Gomes, M. and Meisen, T. 2023. A Review on Customer Segmentation Methods for Personalized Customer Targeting in E-commerce Use Cases. Inf. Syst. e-Bus. Manag., 21(3): 527-570.
5.       Bagirov, A. M., Aliguliyev, R. M. and Sultanova, N. 2023. Finding Compact and Well-Separated Clusters: Clustering Using Silhouette Coefficients. Pattern Recognit., 135: 1-15.
6.       Baltas, G., Argouslidis, P. C. and Skarmeas, D. 2010. The Role of Customer Factors in Multiple Store Patronage: A Cost–Benefit Approach. J. Retail., 86(1): 37–50.
7.       Barbosa, B., Shabani Shojaei, A. and Miranda, H. 2023. Packaging-Free Practices in Food Retail: The Impact on Customer Loyalty. Balt. J. Manag., 18(4): 474-492.
8.       Barus, O. P., Nathasya, C. and Pangaribuan, J. J. 2023. The Implementation of RFM Analysis to Customer Profiling Using K-Means Clustering. Math. Model. Eng. Probl., 10(1): 298-303.
9.       Chang, I. C., Horng, J. S., Liu, C. H., Chou, S. F., and Yu, T. Y. 2022. Exploration of Topic Classification in the Tourism Field with Text Mining Technology: A Case Study of the Academic Journal Papers. Sustainability, 14(7): 4053.
10.    Chiou, J. S., Wu, L. Y. and Sung, Y. P. 2009. Buyer Satisfaction and Loyalty Intention in Online Auctions: Online Auction Web Site versus Online Auction Seller. J. Serv. Manag., 20(5): 521-543.
11.    Chouaten, K., Rodriguez Rivero, C., Nack, F. and Reckers, M. 2024. Unlocking High-Value Football Fans: Unsupervised Machine Learning for Customer Segmentation and Lifetime Value. Front. Sports Act. Living, 6: 1362489.
12.    De Sousa, A., Moro, S., and Pereira, R. 2024. Cluster-Based Approaches Toward Developing a Customer Loyalty Program in a Private Security Company. Appl. Sci., 14(1): 78.
13.    Dey, D. and Banerjee, K. 2023. AI Driven Customer Segmentation and Recommendation of Product for Super Mall. J. Mines Met. Fuels, 71(5): 656–660.
14.    Figler, S. A., Sriraj, P. S., Welch, E. W. and Yavuz, N. 2011. Customer Loyalty and Chicago, Illinois, Transit Authority Buses: Results from 2008 Customer Satisfaction Survey. Transp. Res. Rec, 2216(1): 148-156.
15.    Gustriansyah, R., Suhandi, N. and Antony, F. 2020. Clustering Optimization in RFM Analysis Based on K-Means. Indones. J. Electr. Eng. Comput. Sci., 18(1): 470-477.
16.    Gutiérrez, M. Pérez-Ortiz, J. Sánchez-Monedero, F., Fernández N. and Hervás-Martínez, C. 2016.Ordinal Regression Methods: Survey and Experimental Study. IEEE Trans. Knowl. Data Eng., 28(1):127-146.
17.    Ho, S. P. S. and Wong, A. 2023. The Role of Customer Personality in Premium Banking Services. J. Financ. Serv. Mark., 28(2): 285–305.
18.    Hosmer, D. W., Lemeshow, S. and Sturdivant, R. X., 2013. Applied Logistic Regression. 3rd Edition, John Wiley and Sons, Hoboken, New Jersey.
19.    Kasem, M.S., Hamada, M. and Taj-Eddin, I. 2024. Customer Profiling, Segmentation, and Sales Prediction Using AI in Direct Marketing. Neural Comput. Appl., 36: 4995–5005.
20.    Kotler, P. and Armstrong, G. 2012. Principles of Marketing. 14th Edition, Pearson Education Limited, Essex, England.
21.    Krejcie, R.V. and Morgan, D.W. 1970. Determining Sample Size for Research Activities. Educ. Psychol. Meas., 30(3): 607-610.
22.    Kumar, A. 2023. Customer Segmentation of Shopping Mall Users Using K-Means Clustering. Chapter 13. In: “Advancing SMEs Toward E-Commerce Policies for Sustainability”. IGI Global, PP. 248-270.
23.    Liang, Y.H. 2010. Integration of Data Mining Technologies to Analyze Customer Value for the Automotive Maintenance Industry. Expert Syst. Appl., 37(12): 7489–7496.
24.    Liu, D. R. and Shih, Y. Y. 2005. Integrating AHP and Data Mining for Product Recommendation Based on Customer Lifetime Value. Inf. Manag., 42(3): 387-400.
25.    Liu, H., Mu, Y., Fu, X. and Liu, Y. 2022. Passionately Attached or Properly Matched? The Effect of Self-Congruence on Grocery Store Loyalty. Br. Food J., 124(11): 4054-4071.
26.    MacQueen, J. B. 1967. Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics,  University of California Press, Berkeley, PP. 281-296.
27.    McMullan, R. and Gilmore, A. 2008. Customer Loyalty: An Empirical Study. Eur. J. Mark., 42(9/10): 1084-1094.
28.    Mensouri, D., Azmani, A. and Azmani, M. 2022. K-Means Customers Clustering by their RFMT and Score Satisfaction Analysis. Int. J. Adv. Comput. Sci. Appl., 13(6): 469-476.
29.    Mohammadian, M., and Makhani, I. 2019. RFM-Based Customer Segmentation as an Elaborative Analytical Tool for Enriching the Creation of Sales and Trade Marketing Strategies. Int. Acad. J. Acc. Fin. Manag. (IAJAFM), 6(1): 102-116.
30.    Noorani kootenaee, M., Rezaei Dolatabadi, H. and Mohammad Shafiee, M. 2021. Developing a Model for Retailer Brand Competitiveness with the Grounded Theory Approach. Q. J. Brand Manag., 8(3): 44-15.
31.    Pradhan, S., Patel, G. and Priya, P. 2021. Measuring Customer Lifetime Value: Application of Analytic Hierarchy Process in Determining Relative Weights of LRFM. Int. J. Anal. Hierarchy Process, 13(3): 526-547.
32.    Qin, W. and Hu, M. 2024. Dennis Supermarket Chain Customer Loyalty Study. FHSS, 4(7): 349–358.
33.    Rizkyanto, H. and Gaol, F. L. 2023. Customer segmentation of personal credit using Recency, Frequency, Monetary (RFM) and K-means on financial industry. Int. J. Adv. Comput. Sci. Appl., 14(4): 149-159.
34.    Rousseeuw, P. J. 1987. Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. J. Comput. Appl. Math. J. Comput. Appl. Math., 20: 53–65.
35.    Salameh, A. A., Hatamleh, A., Azim, M. S. and Kanaan, A. G. 2020. Customer-Oriented Determinants of e-CRM Success Factors. Uncertain Supply Chain Manag., 8(4):713-720.
36.    Sarkar, S. K. and Midi, H. 2010. Importance of Assessing the Model Adequacy of Binary Logistic Regression. J. Appl. Sci., 10: 479-486.
37.    Seifollahi, N., Rahimi clever, H. and Shirnezhad Moghanlou, M. 2020. The Effect of Response to Point of Purchase Advertising on Store Brand Loyalty with the Mediating Role of the Customer's Self-Concept. Q. J. Brand Manag., 7(1): 213-260.
38.    Sen, S. S., Alexandrov, A., Jha, S., Mcdowell, W. C. and Babakus, E. 2023. Convenient Competitive? How Brick-And-Mortar Retailers Can Cope with Online Competition. Rev. Manag. Sci., 17(5): 1615–1643.
39.    Smaili, M.Y. and H. Hachimi. 2023. New RFM-D Classification Model for Improving Customer Analysis and Response Prediction. Ain Shams Eng. J., 14(12): 102254.
40.    Stone, B. 1995. Successful Direct Marketing Methods. 5th Edition, NTC Business Books, Lincolnwood, IL.
41.    Suriansha, R. 2023. The Role of Customer Loyalty on Customer Retention in Retail Companies. Int. J. Multidiscipl. Res. Anal., 6 (7): 3359-3365.
42.    Suyanto, A. and Femi, S. R. 2023. Analysis of the Effect of Impulsive Purchase and Service Quality on Customer Satisfaction and Loyalty in Beauty E-Commerce. Qual. Access Success, 24(194): 18 - 23.
43.    Tan C. K. and Nurulhuda F. M. A. 2021. Customer Profiling for Malaysia Online Retail Industry using K-Means Clustering and RM Model. Int. J. Adv. Comput. Sci. Appl., 12(1): 106-113.
44.    Tanveer, M., Ahmad, A. R., Mahmood, H. and Ul Haq, I. 2021. Role of Ethical Marketing in Driving Consumer Brand Relationships and Brand Loyalty: A Sustainable Marketing Approach. Sustainability, 13(12): 1-17.
45.    Taulant K., Brunela T. and Shkelqim F. 2024. Understanding Customer Satisfaction Factors: A Logistic Regression Analysis. J. Educ. Soc. Res., 14(2): 218
46.    Wang, H., Quintana, F. G., Lu, Y., Mohebujjaman, M. and Kamronnaher, K. 2022. How Are BMI, Nutrition, and Physical Exercise Related? An Application of Ordinal Logistic Regression. Life, 12(12): 2098.
47.    Wilbert, H. J., Hoppe, A. F., Sartori, A., Stefenon, S. F. and Silva, L. A. 2023. Recency, Frequency, Monetary Value, Clustering, and Internal and External Indices for Customer Segmentation from Retail Data. Algorithms, 16(9): 396.
48.    Wooldridge, J. 2016. Introductory Econometrics: A Modern Approach. 6th Edition, Standalone Book, Cengage Learning.
49.    Zhang, D., Shen, Z. and Li, Y. 2023. Requirement Analysis and Service Optimization of Multiple Category Fresh Products in Online Retailing Using Importance-Kano Analysis. J. Retail. Consum. Serv., 72:103253.
50.    Zikienė, K., Kyguolienė, A. and Kisieliauskas, J. 2024. The Influence of Creative and Innovative Loyalty Programs’ Features on Customers’ Attitudinal, Aonative, and Behavioural Loyalty. Creat. Stud., 17(1): 254–273.
51.    Zintso, Y., Fedorishina, I., Zaiachkovska, H., Kovalchuk, O. and Tyagunova, Z. 2023. Analysis of Current Trends in The Use of Digital Marketing for The Successful Promotion of Goods and Services in Ukraine. Financ. Credit Act.: Probl. Theory Pract. 3(50): 174-184.
Journal of Agricultural Science and Technology
Volume 28, Issue 3
May and June 2026
Pages 481-492