The use of knowledge extraction in predicting customer churn in B2B

Abstract

Data mining techniques were used to investigate the use of knowledge extraction in predicting customer churn in insurance companies. Data were included from a health insurance company for providing insight into churn behaviour based on a design and application of a prediction model. Additionally, three promising data mining techniques were identified for the prediction of modeling, including logistic regression, neural network, and K-means. The decision tree method was used in the modeling phase of CRISP-DM for identifying the attributes of churned customers. The predictive analysis task is undertaken through classification and regression techniques. K-means clustering variation is selected for exploring if the clustering algorithms categorize the customers in churning and non-churning groups with homogeneous profiles. The findings of the study show that data mining procedures can be very successful in extracting hidden information and get to know customer's information. The 50:50 training set distribution resulted in effective outcomes when the logistic regression technique was used throughout this study. A 70:30 distribution worked effectively for the neural network technique. In this regard, it is concluded that each technique works effectively with a different training set distribution. The predicted findings can have direct implications for the marketing department of the selected insurance company, whereas the models are anticipated to be readily applicable in other environments via this data mining approach. This study has shown that the prediction models can be utilized throughout a health insurance company's marketing strategy and in a general academic context with a combination of a research-based emphasis with a business problem-solving approach.

Customer churn prediction model: a case of the telecommunication market

“…Knowing the risk period will be useful for the business in terms of preventing the customer churn by developing personalized offers and providing additional incentives for current clients. The conclusions of the research by Jamjoom (2021) prove that data mining methods can be highly successful in finding hidden insights and highlighting customer's information. Besides, it is found that each model works effectively with a different training dataset.…”

Detecting the Risk of Customer Churn in Telecom Sector: A Comparative Study

“…NN and SVM performed both best with an accuracy of 83.7%. Jamjoom [44] carried out an analysis of customer churn in B2B. Data from a health insurance company were included for providing insights into churn behavior based on a design and application of a prediction model.…”

Machine learning based customer churn prediction in home appliance rental business

“…Looking at previous studies of customer churn prediction models in the financial sector, research on prediction models was conducted in Korea, mainly in banks and the credit card industry with a large number of customers because academic access to financial data is relatively difficult compared to overseas [ 20 ]. Most of the preceding studies were divided into those that present a method of improving predictive performance by applying various machine learning algorithms [ 21 , 43 ] and those that segment the customer marketing indicators using predicted results [ 22 – 24 ].…”