An effective architectural model for early churn prediction – NELCO

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Abstract

Customer is an asset of any business organization, whose probable chances of churn is loss. Several challenges are to be considered towards controlling customer churn. Machine learning approach is needed to predict an early churn. Even though various soft computational approaches had been proposed, an optimized computational approach which identifies early churn prediction is necessary. The proposed approach NELCO predicts early customer churn using Negative Correlation Learning (NCL) which uses k-means neighbourhood discriminant similarity indices over network of ensemble values. NELCO proves to have an optimal accuracy towards early prediction of churn, as well as suggests that customer retention rate is higher over PSO, ACO approaches.

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Manivannan, R., Saminathan, R., & Saravanan, S. (2019). An effective architectural model for early churn prediction – NELCO. International Journal of Engineering and Advanced Technology, 8(6), 4667–4672. https://doi.org/10.35940/ijeat.F9147.088619

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