A Customer churn prediction and corrective action suggestion model for the telecommunications industry using predictive analytics
Date
2024
Authors
Wanda, R. K.
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Publisher
Strathmore University
Abstract
The telecommunications industry is significantly susceptible to customer churn. Customer churn leads to loss of customer base which leads to reduction in revenue, reduced profit margins, increased customer acquisition costs and loss of brand value. Mitigating the effects of customer churn has proved to be a tall order for many organizations in the telecommunications industry. Most companies employ a reactive approach to customer churn and thus do not take any corrective actions until the customer has left. This approach does not enable organizations to know and prevent potential churn before it occurs. Alternatively, some organizations employ a more proactive approach to mitigate customer churn through predictive analytics. Although this approach is more effective, it only predicts which customers will churn without recommending the appropriate corrective action. In this dissertation, a customer churn prediction and corrective action suggestion model using predictive analytics was implemented to predict churn and suggest appropriate corrective actions. The IBM telco customer churn dataset accessed via API from the open machine learning.org website was used for this study. The dataset was subjected to pre-processing and exploratory data analysis to gain valuable insights into the data. To enhance the reliability of the developed model, an 80/20 train/test split was applied to the dataset. The training dataset was then divided into 5 folds before model fitting. Several classification algorithms; Logistic Regression, Gaussian Naive Bayes, Complement Naïve Bayes, K-NN, Random Forest and CatBoost were then fit with the training data and their performance was evaluated. Logistic Regression achieved a recall of 80% and was selected for system implementation. Logistic regression feature coefficients were then used to determine the appropriate corrective actions. A locally hosted web interface was then developed using the Python Streamlit library to enable users to feed input into the model and get churn predictions and corrective action suggestions. The developed model demonstrated ease of use and high performance and will enable telecommunication companies to accurately predict customer attrition and take appropriate corrective actions, reducing customer attrition's impact on the companies’ bottom line.
Keywords: churn, machine learning, predictive analytics, telecommunications industry
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Citation
Wanda, R. K. (2024). A Customer churn prediction and corrective action suggestion model for the telecommunications industry using predictive analytics [Strathmore University]. http://hdl.handle.net/11071/15652