Using Fuzzy Logic to Make Decisions Based on Data From CRM Systems

Abstract

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. The 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 as well as confronting them with the results from the developed model, it can be confirmed that the model works as expected.

Optimizing Customer Retention in the Telecom Industry: A Fuzzy-Based Churn Modeling with Usage Data

“…Our findings complement other results in the existing literature [4,9,11,12] with two fuzzy systems based on hard-to-reach real-world data from Poland. Based on a limited set of features, we showed ways of engineering and extracting new features, which turned out useful in fuzzy churn modeling with two different models utilizing operator data.…”

“…There is a much bigger selection of churn modeling papers based on other than fuzzy modeling techniques, which are out of the scope of this study, but there is some research worth mentioning. In one of the most recent studies, Bojanowska and Kulisz [12] prepared a Mamdani model with data from CRM systems for churn customer prediction in the banking sector. In [13], Toor and Usman proposed the churn detector (OTCCD), which handles the problems of class imbalance and concept drift.…”

The use of fuzzy logic in the decision-making process regarding the use of outsourcing in companies

“…systemy logiki rozmytej (Aksoy & Öztürk, 2016). Zbiory rozmyte zostały wprowadzone przez Zadeha w 1965 roku, a ich wykorzystanie pozwala na pracę z danymi gdzie mamy do czynienia z nieprecyzyjnymi granicami (Bojanowska & Kulisz, 2023). Jedna z definicji mówi, że logika rozmyta to narzędzie, które jest przyjazne dla użytkownika i pozwala na elastyczność w procesach podejmowania decyzji (Altinoz & Winchester, 2001).…”