Using Fuzzy Logic to Make Decisions Based on the Data From Customer Relationship Management Systems

Bojanowska, Agnieszka Barbara; Kulisz, Monika

doi:10.12913/22998624/172374

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Tytuł artykułu

Using Fuzzy Logic to Make Decisions Based on the Data From Customer Relationship Management Systems

Autorzy

Bojanowska Agnieszka Barbara , Kulisz Monika

Treść / Zawartość

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DOI

10.12913/22998624/172374

Warianty tytułu

Języki publikacji

Abstrakty

The purpose of the article is to propose a fuzzy logic solution for decision-making based on data from CRM (Customer Relationship Management) systems to evaluate banking customer attractiveness. The article is based on theory about management IT systems, especially the CRM type. Based on the literature research, nine identified factors were proposed that can influence whether the relationship with the customer will be profitable for the bank. Factors that affect banking customer attractiveness are considered, including the share of the customer's wallet and the customer's tendency to express a positive opinion of the bank. Data allowing for the identification of these factors is collected in the bank's IT systems, among other CRMs. Based on the research, a model prepared in Simulink using a Mamdani-type Fuzzy Inference System was made. It is a decision model that provides a result in the form of a binary value of 0 or 1, where 1 means it is worth investing in a customer, while 0 means it is not. After considering the subjective opinions, competence and experience of specialists and confronting them with the results from the developed model, it can be confirmed that the model works as expected.

Słowa kluczowe

fuzzy logic AI CRM systems customer loyalty

Wydawca

Lublin University of Technology
Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin

Czasopismo

Advances in Science and Technology. Research Journal

Rocznik

2023

Tom

Vol. 17, no 5

Strony

269--279

Opis fizyczny

Bibliogr. 58 poz., fig., tab.

Twórcy

autor

Bojanowska Agnieszka Barbara

a.bojanowska@pollub.pl

Faculty of Management, Department of Marketing, Lublin University of Technology, ul. Nadbystrzycka 38, 20-618 Lublin, Poland

https://orcid.org/0000-0002-1624-5518

autor

Kulisz Monika

m.kulisz@pollub.pl

Faculty of Management, Department of Organization of Enterprise, Lublin University of Technology, ul. Nadbystrzycka 38, 20-618 Lublin, Poland

https://orcid.org/0000-0002-8111-2316

Bibliografia

1. Hart M.L. Customer relationship management: Are software applications aligned with business objectives? SAJBM. 2006 Jun 30; 37(2): 17–32.
2. Pan S.L., Lee J.N.. Using e-CRM for a unified view of the customer. Commun ACM. 2003 Apr; 46(4): 95–9.
3. Goodhue D., Wixom B., Watson H. Realizing Business Benefits Through CRM: Hitting the Right Target in the Right Way. MIS Quarterly Executive. 2002; 1(2).
4. Karimi J., Somers T.M., Gupta Y.P.. Impact of Information Technology Management Practices on Customer Service. Journal of Management Information Systems. 2001 Mar; 17(4): 125–58.
5. Sigala M. Customer Relationship Management (CRM) Evaluation: Diffusing CRM Benefits into Business Processes. Association for Information Systems. 2004; 172.
6. Zablah A.R., Bellenger D., Johnston W.J. Customer Relationship Management (CRM) Implementation Gaps. Journal of Personal Selling and Sales Management. 2004; 24: 279–95.
7. Ang L., Buttle F. CRM software applications and business performance. J Database Mark Cust Strategy Manag. 2006 Oct; 14(1): 4–16.
8. Stefanov T., Varbanova S., Stefanova M., Ivanov I. CRM System as a Necessary Tool for Managing Commercial and Production Processes. TEM Journal. 2023 May 29; 785–97.
9. Libai B., Bart Y., Gensler S., Hofacker C.F., Kaplan A., Kötterheinrich K., et al. Brave New World? On AI and the Management of Customer Relationships. Journal f Interactive Marketing. 2020 Aug; 51: 44–56.
10. ColsonE.WhatAI-DrivenDecisionMakingLooksLike. Harvard Business Rewiev [Internet]. 2019 Jul 8 [cited 2023 Jun 1]; Available from: https://hbr.org/2019/07/ what-ai-driven-decision-making-looks-like
11. Ledro C., Nosella A., Vinelli A. Artificial intelligence in customer relationship management: literature review and future research directions. JBIM. 2022 Dec 19; 37(13): 48–63.
12. Sahar F. Machine-Learning Techniques for Customer Retention: A Comparative Study. ijacsa [Inernet]. 2018 [cited 2023 Jul 10]; 9(2). Available from: http://thesai.org/Publications/ViewPaper?Vo lume=9&Issue=2&Code=ijacsa&SerialNo=38
13. Padilla N., Ascarza E. Overcoming the Cold Start Problem of Customer Relationship Management Using a Probabilistic Machine Learning Approach. Journal of Marketing Research. 2021 Oct; 58(5): 981–1006.
14. Zadeh L.A. Fuzzy sets. Information and Control. 1965 Jun; 8(3): 338–53.
15. Zadeh L.A. Fuzzy logic and approximate reasoning: In memory of Grigore Moisil. Synthese. 1975; 30(3–4): 407–28.
16. Trillas E., Eciolaza L. Fuzzy Logic: An Introductory Course for Engineering Students [Internet]. Cham: Springer International Publishing; 2015 [cited 2023 Jul 10]. (Studies in Fuzziness and Soft Computing; vol. 320). Available from: https://link.springer. com/10.1007/978-3-319-14203-6
17. Metody prognozowania: Podstawy logiki rozmytej [Internet]. [cited 2023 Jun 12]. Available from: https://m6.pk.edu.pl/materialy/mp/MP_06_logika_rozmyta.pdf
18. Fuzzy Logic | Introduction [Internet]. [cited 2023 May 10]. Available from: https://www.geeksfor- geeks.org/fuzzy-logic-introduction/
19. Darłak B., Kowalska-Włodarczyk M. Zastosowanie logiki rozmytej w budowie modeli geologicznych. Nafta-Gaz. 2019; 65(6): 454–61.
20. Szymański Z. Zastosowanie metod sztucznej inteligencji w układach sterowania maszyn transportu poziomego i pionowego. Napędy i Sterowanie. 2007; 9(12): 114–20.
21. Lisowski E., Filo G. Zastosowanie logiki rozmytej w inżynierii mechanicznej na przykładzie hydraulicznego układu pozycjonowania ładunku. Czasopismo Techniczne Mechanika. 2011; 108(7).
22. Rogowska D. Zastosowanie logiki rozmytej w zarządzaniu zapasami. Logistyka. 2011; (5).
23. Torres A., Nieto J.J. Fuzzy Logic in Medicine and Bioinformatics. Journal of Biomedicine and Biotechnology. 2006; 2006: 1–7.
24. Prawie wszystko o Logice Rozmytej [Internet]. [cited 2023 Jun 10]. Available from: https://web. archive.org/web/20121025051659/http://www. isep.pw.edu.pl/ZakladNapedu/dyplomy/fuzzy/
25. Kumar V., Ramani G., Bohling T. Customer life time value approaches and best practice applica-ions. Journal of Interactive Marketing. 2004 Aug; 18(3): 60–72.
26. Ekinci Y., Uray N., Ülengin F. A customer lifetime value model for the banking industry: a guide to marketing actions. European Journal of Marketing. 2014 Apr 8; 48(3/4): 761–84.
27. Berger P.D., Nasr N.I. Customer lifetime value: Marketing models and applications. Journal of Interactive Marketing. 1998 Jan; 12(1): 17–30.
28. Blattberg R., Deighton J. Manage marketing by the customer equity test. Harvard Business Review. 1996; 74(4): 136–44.
29. Sohrabi B., Amir K. Customer lifetime value (CLV) measurement based on RFM model. Iranian Accounting & Auditing Review. 2007; 14(47): 14–20.
30. Cele i zalety wskaźnika Customer Lifetime Value [Internet]. [cited 2023 Jun 12]. Available from: https://questus.pl/blog/customer-lifetime-value-czyli-jak-mierzyc-zyciowa-wartosc-klienta/
31. Tysowecka M. Customer Lifetime Value (clv), czyli jak zmierzyć wartość życiową klienta? [Internet]. [cited 2023 Jun 1]. Available from: https://www. greenweb.pl/customer-lifetime-value-clv-czyli-jak-zmierzyc-wartosc-zyciowa-klienta/
32. Laketa M., Sanader D., Laketa L., Misic Z. Customer relationship management: Concept and importance for banking sector. UTMS Journal of Economics. 2015; 6(2): 241–54.
33. Al Amosh H., Khatib S.F.A. Websites Visits and Financial Performance for GCC Banks: The Mod erating Role of Environmental, Social and Governance Performance. Global Business Review. 2022 Jul 13;097215092211095.
34. Elhajjar S., Ouaida F. An analysis of factors affecting mobile banking adoption. IJBM. 2019 Jul 18; 38(2): 352–67.
35. Kaura V., Durga Prasad ChS, Sharma S. Service quality, service convenience, price and fairness, customer loyalty, and the mediating role of customer satisfaction. International Journal of Bank Marketing. 2015 Jun 1; 33(4): 404–22.
36. Kebede A.M., Tegegne Z.L. The effect of customer relationship management on bank performance: In context of commercial banks in Amhara Region, Ethiopia. Wright LT, editor. Cogent Business & Management. 2018 Jan 1;5(1):1499183.
37. Amril A.P., Wardi Y., Masdupi E. The Effect of Customer Relationship Management, Customer Value and Dimension of Service Quality on Customer Satisfaction and The Impact on Customer Loyalty of PT. Bank Tabungan Negara (Persero), Tbk Kas Siteba Padang Office. In: Proceedings of the 2nd Padang International Conference on Education, Economics, Business and Accounting (PICEEBA-2 2018) [Internet]. Padang, Indonesia: Atlantis Press; 2019 [cited 2023 Jul 10]. Available from: https:// www.atlantis-press.com/article/125907973
38. Siqueira J.R., Peña N.G., Ter Horst E., Molina G. Spreading the Word: How Customer Experience in a Traditional Retail Setting Influences Consumer Traditional and Electronic Word-of-mouth Intention. Electronic Commerce Research and Applications. 2019 Sep; 37:100870.
39. Loureiro S.M.C. The Effect Of Perceived Benefits, Trust, Quality, Brand Awareness/Associations and Brand Loyalty on Internet Banking Brand Equity. ijecs. 2013 Dec; 4(2): 139–58.
40. Cooil B., Keiningham T.L., Aksoy L., Hsu M.A Longitudinal Analysis of Customer Satisfaction and Share of Wallet: Investigating the Moderating Effect of Customer Characteristics. Journal of Marketing. 2007 Jan; 71(1): 67–83.
41. Editor Academic Journals &Amp; Conferences. Improving the Loaning Process in Commercial Banks. 2022 Aug 30 [cited 2023 Jun 2]; Available from: https://osf.io/2jfpm/
42. Schmitt C.V. Push or Pull: Recommendations and Alternative Approaches for Public Science Communicators. Front Commun. 2018 Apr 3; 3:13.
43. Kumar M., Misra M. Evaluating the effects of CRM practices on organizational learning, its antecedents and level of customer satisfaction. JBIM. 2021 Jan 12; 36(1): 164–76.
44. Gupta S., Hanssens D., Hardie B., Kahn W., Kumar V., Lin N., et al. Modeling Customer Lifetime Value. Journal of Service Research. 2006 Nov; 9(2): 139–55.
45. Teck H.H., Young-Hoon P., Yong-Pin Z. Incorporating Satisfaction into Customer Value Analysis: Optimal Investment in Lifetime Value. Marketing Science. 25(3): 260–77.
46. Helgesen Ø. Are Loyal Customers Profitable? Customer Satisfaction, Customer (Action) Loyalty and Customer Profitability at the Individual Level. Journal of Marketing Management. 2006Apr; 22(3–4): 245–66.
47. Teles G., Rodrigues J.J.P.C., Saleem K., Kozlov S., Rabêlo R.A.L. Machine learning and decision support system on credit scoring. Neural Comput & Applic. 2020 Jul; 32(14): 9809–26.
48. Du R.Y., Kamakura W.A., Mela C.F. Size and Share of Customer Wallet. Journal of Marketing. 2007 Apr;71(2):94–113.
49. Babakus E., Yavas U. Does customer sex influence the relationship between perceived quality and share of wallet? Journal of Business Research. 2008 Sep; 61(9): 974–81.
50. Homburg C., Giering A. Personal characteristics as moderators of the relationship between customer satisfaction and loyalty?an empirical analysis. Psychol Mark. 2001 Jan; 18(1): 43–66.
51. Engel J.F., Blackwell R.D., Winiard P.W., Budijanto F.X. Consumer behavior. 6th ed. Jakarta: Binarupa Aksara; 1994.
52. Razak A., Palilati A., Hajar I., Madjid R. Customer Income Role as Moderation Variable of Satisfaction Effect on Customer Loyalty in Bank Negara Indonesia (Persero), Tbk. In Southeast Sulawesi. The International Journal Of Engineering And Science (IJES). 2016; 5(3): 58-64.
53. Shafia M.A., Mahdavi Mazdeh M., Vahedi M., Pournader M. Applying fuzzy balanced scorecard for evaluating the CRM performance. Industrial Management & Data Systems. 2011 Aug 23; 111(7): 1105–35.
54. Soft computing for business intelligence. 1st edition. New York: Springer; 2014. (Studies in computational intelligence).
55. Geramian A., Abraham A. Customer classification: A Mamdani fuzzy inference system standpoint for modifying the failure mode and effect analysis based three dimensional approach. Expert Systems with Applications. 2021 Dec; 186: 115753.
56. Yasar O., Korkusuz Polat T. A Fuzzy-Based Application for Marketing 4.0 Brand Perception in the COVID-19 Process. Sustainability. 2022 Dec 8; 14(24): 16407.
57. Bazmara A., Donighi S.S. Bank Customer Credit Scoring by Using Fuzzy Expert System. IJISA. 2014 Oct 8; 6(11): 29–35.
58. Bernardo D., Hagras H., Tsang E. A Genetic Type-2 fuzzy logic based system for financial applications modelling and prediction. In: 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) [Internet]. Hyderabad, India: IEEE; 2013 [cited 2023 Jul 10]. p. 1–8. Available from: http://ieeex- plore.ieee.org/document/6622310/

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Typ dokumentu

Bibliografia

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