Analysis of fuzzy logic methods for forecasting customer churn

Andrii Papa,Andrii Yarovyi,Yevhen Shemet

doi:10.15587/2706-5448.2021.225285

Andrii Papa, Andrii Yarovyi + Show 1 more

Open Access

https://doi.org/10.15587/2706-5448.2021.225285

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Abstract

The object of research is the process of predicting the churn of customers of telecommunications companies based on fuzzy logic and neural networks. The research carried out is based on the application of an approach that is implemented through the combined use of fuzzy logic and neural networks. The main assumption of the study is the hypothesis that the use of a fuzzy neural network formed on the basis of fuzzy logic algorithms can improve the accuracy of predicting customer churn relative to available solutions. This result can’t be achieved neglecting the existing resource constraints and requirements, which must be determined separately for each case of research. The relevance of the problem of forecasting customer churn for companies with a large number of users is considered. A model for predicting customer churn is proposed based on the combined use of fuzzy logic and neural networks. The main feature of this approach is that a test sample of normalized data is used at the basis of fuzzy neural networks, which are processed to form the parameters of membership functions that correspond to the inference system, that is, conclusions are made on the basis of a fuzzy logic apparatus. Also, to find the parameters of the membership function, neural network algorithms are used. Such systems can use previously known information, learn, gain new knowledge, predict time series, perform image classification, and besides, they are quite visual to the user. The application of methods of fuzzy logic is considered, they make it possible to obtain a result in the form of a fuzzy inference. The expediency of choosing these methods is explained by the fact that they were previously used in fuzzy automatic control systems and showed sufficiently high quality results. The expediency and prospects of using the proposed approach in the problem of predicting the outflow of customers of telecommunications companies are shown, and the results of software implementation are presented.

Highlights

Today one of the most urgent tasks for companies working with a large number of users is to retain existing and attract new customers
The main assumption of the study is the hypothesis that the use of a fuzzy neural network formed on the basis of fuzzy logic algorithms can improve the prediction of customer churn relative to available solutions
The main feature of this approach is that a test sample of normalized data is used at the basis of fuzzy neural networks, which are processed to formulate the parameters of membership functions that correspond to the inference system, that is, conclusions are TECHNOLOGY AUDIT AND PRODUCTION RESERVES — No 1/2(57), 2021

Summary

Introduction

Today one of the most urgent tasks for companies working with a large number of users is to retain existing and attract new customers. Most of the economic problems have to be solved under conditions of uncertainty of the initial information [1]. To solve the problem of predicting customer churn in order to retain existing users, it is advisable to analyze the methods of fuzzy logic, with the possibility of their combined use with neural networks. The object of research is the process of predicting the churn of customers of telecommunications companies based on fuzzy logic and neural networks. The aim of research is to study existing methods of fuzzy logic, as well as improve the forecast of customer churn through the use of a fuzzy neural network

Methods of research

The accumulation of the conclusions of the rules of

Aggregation of the premise of the rules

Findings

Conclusions