Customer Attrition Estimation Modelling Based on Predominant Attributes Using Multi-layered Feed-Forward Neural Network
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In an era of increasingly competitive businesses, the biggest challenge that firms face is retaining existing customers. When a customer leaves a business, the recurring revenue of an organization is highly impacted. In the banking and financial sector, churn rates are as high as 30%. Typically, products like home loans and education loans have the longest customer relationship. In this proposed work, an artificial neural network has been used to design a mathematical churn model which will assist financial organizations to reduce attrition and increase profits. An accuracy of 89.5% has been achieved.
KeywordsChurn prediction Machine learning ANN
- 3.Deloitte (2015) Opportunities in telecom sector: arising from big data. Aegis School of Business, Data Science and TelecommunicationGoogle Scholar
- 5.Li C, Kang Q, Ge G, Song Q, Lu Q, Cheng J (2016) Deep BE: learning deep binary encoding for multi-label classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 39–46Google Scholar
- 6.Choong ACH, Lee NK (2017) Evaluation of convolutionary neural networks modeling of dna sequences using ordinal versus one hot encoding method. In: International conference on computer and drone applications, pp 60–65Google Scholar
- 7.Andrew G, Arora R, Bilmes J, Livescu K (2013) Deep canonical correlation analysis. In: International conference on machine learningGoogle Scholar