Analysis of customer relationship management using Machine Learning
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Abstract
Customer retention is key to business sustenance and have become more important in the quality of service (QoS) that organizations can provide them. Services provided by different vendors are not highly distinguished which increases competition between organizations to maintain and increase their QoS. Customer Relationship Management systems are used to enable organizations to acquire new customers, establish a continuous relationship with them and increase customer
retention for more profitability Understanding of customer satisfaction, persona analysis of customer website visit pattern, customer feedback analysis are requirement for future to retain customers. This paper discusses methods using machine learning and analysis to achieve these objectives, and redesign the product according to customized needs.CRM systems usemachine-learning models to analyze customers’ personal and behavioural data to give organization a competitive advantage
by increasing customer retention rate. Those models can predict customers who are expected to churn and reasons of churn.Predictions are used to design targeted marketing plans and service offers. This paper tries to compare and analyze the performance of different machine-learning techniques that are used for prediction problem. Analytical techniques that belong to different categories of learning are chosen for this study. The chosen techniques include Discriminant Analysis, Decision Trees (CART), instance-based learning (k-nearest neighbours), Support Vector Machines, Logistic Regression, ensemble– based learning techniques (Random Forest, Ada Boosting trees and Stochastic Gradient Boosting), Naïve Bayesian, and Multi-layer perceptron. Models were applied on a dataset of companies that contains CRM feedback records. Results show that both random forest and ADA boost outperform all other techniques with almost the same accuracy 97%. Both Multilayer perceptron and Support vector machine can be recommended as well with 95% accuracy. Decision tree achieved 92%,
naïve Bayesian 90% and finally logistic regression and Linear Discriminant Analysis (LDA) with accuracy 88.7%.
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