Next Best Action Using Prediction Analysis

As the market has become increasingly saturated, churn prediction and management has become of great concern to many industries. A company wishing to retain its customer needs to be able to predict those who are likely to churn and will make those customers the focus of customer retention efforts. Today Customer data has properties of large samples, high dimensions, and more noises. In response to the limitations of existing feature selection in churn-prediction, we introduce and experimentally evaluate Support vector machine-recursive feature elimination attribute selection algorithm. It can identify key attributes of customer churn, rule out the related and redundant attributes, and reduce the dimensions of data. It is more important that this algorithm is related to the followed classification learning algorithm so that it can be better integrated into churn prediction. The empirical evaluation results suggest that the proposed feature selection algorithm extracts less key attributes and exhibits better satisfactory predictive effectiveness than the other three comparable attribute selection algorithms. Using this algorithm, a company can predict which customer has more probability to churn and can provide a scheme or take appropriate actions so that the customer continues to use the services of the company.