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A Systematic Review of Algorithms applied for Telecom Churn Prediction

The emergence of market liberalization and globalization are altering the market competitiveness field essentially. The existence of modem technique in processes of business has intensified the rivalry and put forth new barriers for service providing firms. To cope up with the altering scenarios organizations are shifting their attention on retaining existing customers rather than recruiting new ones. It has become compulsory for service providers to reduce the rate of chum because the negligence could be resulted as reduction of profitability in major perspective. The prediction of churn helps in recognizing customers who are likely to leave a firm. Telecom sector is coping with the problem of ever developing rate of chum. The prediction of chum is very important in telecom sector to retain their customers. The main aim of the research is to examine the systematic review of the machine learning techniques enables telecom firms to be equipped with efficient approaches for reducing the rate of churn. The main intention of the research is to examine a systematic review of algorithms applied for telecom chum prediction. This research proposes a hybrid approach (ANN + KNN) for chum prediction in telecom companies.