Predictive Customer Analytics: Machine Learning for Churn Prediction and Retention
DOI:
https://doi.org/10.59994/ajbtme.2024.1.11Abstract
Customer churn presents a big challenge in the industry. Businesses have to deal with the problem of customers stopping using their products and services due to dissatisfaction, competitive offers, more affordable alternatives, or changing needs. Churn can be damaging to businesses since it causes revenue loss and higher costs. To address this issue, our research aimed to develop a prediction model that helps predict customer churn. We started with getting the data set about telecommunication. Our analysis and model development were based on this dataset. Then we did data visualization to gain a better understanding of the data through multiple charts. After that, we performed data preparation. First, we did data transformation, data cleansing to address missing values and outliers; feature selection was done, and finally, in this step, the data set was split into testing and training sets. Multiple machine learning algorithms were used for modeling, such as decision trees, random forests, logistic regression, support vector machines, Naïve Bayes, and neural networks. Following model development, we evaluated the model performance with each algorithm using tune model hyperparameters. The decision tree algorithm performed the best with %96.7 accuracy, %96.9 precision, %99.3 recall, and %98.1 F1-score. These findings showed how effective decision tree algorithms are in predicting customer churn. This predictive model will enable telecommunication businesses to predict potential churn, make retention strategies, reduce customer churn and increase customer retention rates.
Keywords:
Customer churn, Machine learning, Tune Model Hypermeters, Accuracy, Precision, Recall, F1-scoreReferences
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