Customer Churn Analysis In Banking Sector Using Data Mining Techniques

Customer churn has become a major problem within a customer centred banking industry and banks have always tried to track customer interaction with the company, in order to detect early warning signs in customer's behaviour such as reduced transactions, account status dormancy and take steps to prevent churn. This paper presents a data mining model that can be used to predict which customers are most likely to churn (or switch banks). The study used real-life customer records provided by a major Nigerian bank. The raw data was cleaned, pre-processed and then analysed using WEKA, a data mining software tool for knowledge analysis. Simple K-Means was used for the clustering phase while a rule-based algorithm, JRip was used for the rule generation phase. The results obtained showed that the methods used can determine patterns in customer behaviours and help banks to identify likely churners and hence develop customer retention modalities.