Advanced Algorithm for Prediction of Churn in Various Industries in the Fast Growing World

In the study it has been proven that the cost of acquisition of a new customer is much more then thecost of the retention of the existing customer. Also, it becomes very easy for the organizations if they come to know of the customers that are likely to get churn in advance studying the behavioral aspects so that they can take appropriate measures to keep the customer in their own territory. So, there has been a lot of study on the existing algorithms to understand which can provide the better accuracy in terms of the prediction analysis of their customers. This can be very useful for the service providers to in order to maintain trust and loyaltytowards their customers and in good will against their competitors

Stacking Ensemble Approach for Churn Prediction: Integrating CNN and Machine Learning Models with CatBoost Meta-Learner

In the telecom industry, predicting customer churn is crucial for improving customer retention. In literature, the use of single classifiers is predominantly focused. Customer data is complex data due to class imbalance and contain multiple factors that exhibit nonlinear dependencies. In these complex scenarios, single classifiers may be unable to fully utilize the available information to capture the underlying interactions effectively. In contrast, ensemble learning that combines various base classifiers empowers a more thorough data analysis, leading to improved prediction performance. In this paper, a heterogeneous ensemble model is proposed for churn prediction in the telecom industry. The model involves exploratory data analysis, data pre-processing and data resampling to handle class imbalance. In this proposed model, multiple trained base classifiers with different characteristics are integrated through a stacking ensemble technique. Specifically, convolutional-based neural network, logistic regression, decision tree and Support Vector Machine (SVM) are considered as the base classifiers in this work. The proposed stacking ensemble model utilizes the unique strengths of each base classifier and leverages collective knowledge to improve prediction performance with a meta-learner. The efficacy of the proposed model is assessed on a real-world dataset, i.e., Cell2Cell. The empirical results demonstrate the superiority of the proposed model in churn prediction with 62.4% f1-score and 60.62% recall

A swarm intelligence-based ensemble learning model for optimizing customer churn prediction in the telecommunications sector

In today's competitive market, predicting clients' behavior is crucial for businesses to meet their needs and prevent them from being attracted by competitors. This is especially important in industries like telecommunications, where the cost of acquiring new customers exceeds retaining existing ones. To achieve this, companies employ Customer Churn Prediction approaches to identify potential customer attrition and develop retention plans. Machine learning models are highly effective in identifying such customers; however, there is a need for more effective techniques to handle class imbalance in churn datasets and enhance prediction accuracy in complex churn prediction datasets. To address these challenges, we propose a novel two-level stacking-mode ensemble learning model that utilizes the Whale Optimization Algorithm for feature selection and hyper-parameter optimization. We also introduce a method combining K-member clustering and Whale Optimization to effectively handle class imbalance in churn datasets. Extensive experiments conducted on well-known datasets, along with comparisons to other machine learning models and existing churn prediction methods, demonstrate the superiority of the proposed approach