A New RFM Model Approach: RFMS

Semra Erpolat Taşabat (), Tayfun Özçay (), Salih Sertbaş () and Esra Akca ()
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Semra Erpolat Taşabat: Mimar Sinan Fine Arts University
Tayfun Özçay: Borusan R&D and Information Technologies
Salih Sertbaş: Linktera Information Technologies
Esra Akca: Borusan R&D and Artificial Intelligence Solutions

A chapter in Industry 4.0 and the Digital Transformation of International Business, 2023, pp 143-172 from Springer

Abstract: Abstract With the advancement of technology and the widespread use of the Internet, the concept of big data, which we have started to hear of frequently, has emerged. The conversion of big data, which is shortly described as unstructured data stack, into meaningful information, can be performed via different methods. One of these methods is the RFM analysis. RFM analysis is the abbreviation of the words Recency, Frequency and Monetary, and it is an effective and practical marketing model that realizes behaviour-based customer segmentation. The mainstay of the RFM analysis, which helps to develop marketing strategies, is the view that customers who have recently done shopping, who shop frequently and who bring high returns on their purchases will be potential customers who may reflect positive returns to future marketing campaigns. In this study, a new model has been proposed by modifying the RFM analysis. The classical RFM model was improved to RFMS by adding the “economic” variable under the name Sensitivity, which is one of the effective factors of the PESTEL (Political, Economic, Social, Technological, Legal and Environmental) analysis. This model aims to improve the efficiency processes of companies and to develop a classification method that will manage customer relations more accurately. Thus, saving time and cost and establishing a profitable relationship between the customer and the company is aimed. The effects of the proposed model were analysed using the customer database of BORUSANCAT Machine and Power Systems. In order to achieve the best result, different models were created for different customer groups and scores were obtained. Customers with high favourableness potential for offers were identified following the analysis. Thus, time and cost savings have been achieved.

Keywords: RFM; RFMS; Data analysis; Customer segmentation; PESTEL analysis (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-19-7880-7_9

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DOI: 10.1007/978-981-19-7880-7_9

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