IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i16p1836-d608250.html
   My bibliography  Save this article

An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail Business

Author

Listed:
  • Rocío G. Martínez

    (Department of Management and Marketing, Complutense University of Madrid, UCM, 28223 Madrid, Spain)

  • Ramon A. Carrasco

    (Department of Management and Marketing, Complutense University of Madrid, UCM, 28223 Madrid, Spain)

  • Cristina Sanchez-Figueroa

    (Department of Statistics and Applied Economy, UNED University, 28040 Madrid, Spain)

  • Diana Gavilan

    (Department of Management and Marketing, Complutense University of Madrid, UCM, 28223 Madrid, Spain)

Abstract

In the field of strategic marketing, the recency, frequency and monetary (RFM) variables model has been applied for years to determine how solid a database is in terms of spending and customer activity. Retailers almost never obtain data related to their customers beyond their purchase history, and if they do, the information is often out of date. This work presents a new method, based on the fuzzy linguistic 2-tuple model and the definition of product hierarchies, which provides a linguistic interpretability giving business meaning and improving the precision of conventional models. The fuzzy linguistic 2-tuple RFM model, adapted by the product hierarchy thanks to the analytical hierarchical process (AHP), is revealed to be a useful tool for including business criteria, product catalogues and customer insights in the definition of commercial strategies. The result of our method is a complete customer segmentation that enriches the clusters obtained with the traditional fuzzy linguistic 2-tuple RFM model and offers a clear view of customers’ preferences and possible actions to define cross- and up-selling strategies. A real case study based on a worldwide leader in home decoration was developed to guide, step by step, other researchers and marketers. The model was built using the only information that retailers always have: customers’ purchase ticket details.

Suggested Citation

  • Rocío G. Martínez & Ramon A. Carrasco & Cristina Sanchez-Figueroa & Diana Gavilan, 2021. "An RFM Model Customizable to Product Catalogues and Marketing Criteria Using Fuzzy Linguistic Models: Case Study of a Retail Business," Mathematics, MDPI, vol. 9(16), pages 1-31, August.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1836-:d:608250
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/16/1836/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/16/1836/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kumar, V., 2008. "Customer Lifetime Value — The Path to Profitability," Foundations and Trends(R) in Marketing, now publishers, vol. 2(1), pages 1-96, August.
    2. Bresciani, Stefano & Ferraris, Alberto & Del Giudice, Manlio, 2018. "The management of organizational ambidexterity through alliances in a new context of analysis: Internet of Things (IoT) smart city projects," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 331-338.
    3. Buckinx, Wouter & Van den Poel, Dirk, 2005. "Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting," European Journal of Operational Research, Elsevier, vol. 164(1), pages 252-268, July.
    4. Gabriel R. Bitran & Susana V. Mondschein, 1996. "Mailing Decisions in the Catalog Sales Industry," Management Science, INFORMS, vol. 42(9), pages 1364-1381, September.
    5. Verhoef, Peter C. & Broekhuizen, Thijs & Bart, Yakov & Bhattacharya, Abhi & Qi Dong, John & Fabian, Nicolai & Haenlein, Michael, 2021. "Digital transformation: A multidisciplinary reflection and research agenda," Journal of Business Research, Elsevier, vol. 122(C), pages 889-901.
    6. Jan Roelf Bult & Tom Wansbeek, 1995. "Optimal Selection for Direct Mail," Marketing Science, INFORMS, vol. 14(4), pages 378-394.
    7. Ruth N. Bolton, 1998. "A Dynamic Model of the Duration of the Customer's Relationship with a Continuous Service Provider: The Role of Satisfaction," Marketing Science, INFORMS, vol. 17(1), pages 45-65.
    8. K. Coussement & D. Van Den Poel, 2008. "Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/527, Ghent University, Faculty of Economics and Business Administration.
    9. Yao Zhang & Eric T. Bradlow & Dylan S. Small, 2015. "Predicting Customer Value Using Clumpiness: From RFM to RFMC," Marketing Science, INFORMS, vol. 34(2), pages 195-208, March.
    10. Daniel G. J. Kuchinka & Szilvia Balazs & Marius Dan Gavriletea & Borivoje-Boris Djokic, 2018. "Consumer Attitudes toward Sustainable Development and Risk to Brand Loyalty," Sustainability, MDPI, vol. 10(4), pages 1-25, March.
    11. Heldt, Rodrigo & Silveira, Cleo Schmitt & Luce, Fernando Bins, 2021. "Predicting customer value per product: From RFM to RFM/P," Journal of Business Research, Elsevier, vol. 127(C), pages 444-453.
    12. Eger, Ludvík & Komárková, Lenka & Egerová, Dana & MiÄ Ã­k, Michal, 2021. "The effect of COVID-19 on consumer shopping behaviour: Generational cohort perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 61(C).
    13. M. Joseph Sirgy, 2018. "Self-congruity theory in consumer behavior: A little history," Journal of Global Scholars of Marketing Science, Taylor & Francis Journals, vol. 28(2), pages 197-207, April.
    14. Philip Kotler & Kevin Keller & Delphine Manceau & Aurélie Hemonnet-Goujot, 2019. "Marketing Management (16e édition)," Post-Print hal-02176421, HAL.
    15. Matarazzo, Michela & Penco, Lara & Profumo, Giorgia & Quaglia, Roberto, 2021. "Digital transformation and customer value creation in Made in Italy SMEs: A dynamic capabilities perspective," Journal of Business Research, Elsevier, vol. 123(C), pages 642-656.
    16. Baesens, Bart & Viaene, Stijn & Van den Poel, Dirk & Vanthienen, Jan & Dedene, Guido, 2002. "Bayesian neural network learning for repeat purchase modelling in direct marketing," European Journal of Operational Research, Elsevier, vol. 138(1), pages 191-211, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    2. Baumgartner, Bernhard & Hruschka, Harald, 2005. "Allocation of catalogs to collective customers based on semiparametric response models," European Journal of Operational Research, Elsevier, vol. 162(3), pages 839-849, May.
    3. Roland T. Rust & Tuck Siong Chung, 2006. "Marketing Models of Service and Relationships," Marketing Science, INFORMS, vol. 25(6), pages 560-580, 11-12.
    4. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.
    5. Singh, Shiwangi & Sharma, Meenakshi & Dhir, Sanjay, 2021. "Modeling the effects of digital transformation in Indian manufacturing industry," Technology in Society, Elsevier, vol. 67(C).
    6. Drèze, Xavier & Bonfrer, André, 2008. "An empirical investigation of the impact of communication timing on customer equity," Journal of Interactive Marketing, Elsevier, vol. 22(1), pages 36-50.
    7. M. Ballings & D. Van Den Poel & E. Verhagen, 2013. "Evaluating the Added Value of Pictorial Data for Customer Churn Prediction," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 13/869, Ghent University, Faculty of Economics and Business Administration.
    8. A. Prinzie & D. Van Den Poel, 2005. "Constrained optimization of data-mining problems to improve model performance: A direct-marketing application," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 05/298, Ghent University, Faculty of Economics and Business Administration.
    9. Yao Zhang & Eric T. Bradlow & Dylan S. Small, 2015. "Predicting Customer Value Using Clumpiness: From RFM to RFMC," Marketing Science, INFORMS, vol. 34(2), pages 195-208, March.
    10. Ballings, Michel & Van den Poel, Dirk, 2015. "CRM in social media: Predicting increases in Facebook usage frequency," European Journal of Operational Research, Elsevier, vol. 244(1), pages 248-260.
    11. Roland T. Rust & Peter C. Verhoef, 2005. "Optimizing the Marketing Interventions Mix in Intermediate-Term CRM," Marketing Science, INFORMS, vol. 24(3), pages 477-489, December.
    12. Bolton, R.N. & Lemo, K.N. & Verhoef, P.C., 2002. "The Theoretical Underpinnings of Customer Asset Management," ERIM Report Series Research in Management ERS-2002-80-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    13. Christou, Prokopis & Hadjielias, Elias & Simillidou, Aspasia & Kvasova, Olga, 2023. "The use of intelligent automation as a form of digital transformation in tourism: Towards a hybrid experiential offering," Journal of Business Research, Elsevier, vol. 155(PB).
    14. Risselada, Hans & Verhoef, Peter C. & Bijmolt, Tammo H.A., 2010. "Staying Power of Churn Prediction Models," Journal of Interactive Marketing, Elsevier, vol. 24(3), pages 198-208.
    15. Philipp Afèche & Mojtaba Araghi & Opher Baron, 2017. "Customer Acquisition, Retention, and Service Access Quality: Optimal Advertising, Capacity Level, and Capacity Allocation," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 674-691, October.
    16. Van den Poel, Dirk & Lariviere, Bart, 2004. "Customer attrition analysis for financial services using proportional hazard models," European Journal of Operational Research, Elsevier, vol. 157(1), pages 196-217, August.
    17. Blattberg, Robert C. & Malthouse, Edward C. & Neslin, Scott A., 2009. "Customer Lifetime Value: Empirical Generalizations and Some Conceptual Questions," Journal of Interactive Marketing, Elsevier, vol. 23(2), pages 157-168.
    18. Durango-Cohen, Elizabeth J., 2013. "Modeling contribution behavior in fundraising: Segmentation analysis for a public broadcasting station," European Journal of Operational Research, Elsevier, vol. 227(3), pages 538-551.
    19. Zahoor, Nadia & Zopiatis, Anastasios & Adomako, Samuel & Lamprinakos, Grigorios, 2023. "The micro-foundations of digitally transforming SMEs: How digital literacy and technology interact with managerial attributes," Journal of Business Research, Elsevier, vol. 159(C).
    20. Zhou, Zhongsheng & Li, Zhuo, 2023. "Corporate digital transformation and trade credit financing," Journal of Business Research, Elsevier, vol. 160(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:16:p:1836-:d:608250. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.