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Marketing Automation Customer Growth Predictions Using Ensemble Learning

A. Karthik, Narsaiah Domala

Abstract


The percentage of a company's total customer base that decides to switch service providers is referred to as "customer churn." Client relationship management, also known as CRM, is a type of company strategy that aims to provide customers with an extraordinary experience by combining cutting-edge technology with customer requirements. Both the improvement of business ties and the assistance provided to the company in maintaining a distance from its consumers are of equal importance. One of our primary objectives is to construct a dependable churn prediction model so that we can reduce churn, keep churn under control, bring in new consumers, and keep our best clients. It grants the organization the ability to take the initiative, comprehend the outcomes, and analyses data in order to direct action. Despite the fact that it is impossible to establish the most basic of classifier ensembles in this domain, it is still difficult to create an ensemble of several classifiers in the systems that are already in place, and the result is inaccurate. The HYBRID FIREFLY algorithm produces the most accurate results, with an accuracy of 86.34 percent, when the random forest algorithm and the gradient boosting method are both used to generate predictions.


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References


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