Module VI: Unraveling Customer Churn with Supervised Learning: A Data-driven Approach

Introduction:

In today’s competitive business landscape, understanding and predicting customer churn is paramount for companies to retain their customer base and sustain growth. In this Medium post, we delve into the application of supervised learning techniques to extract insights from a dataset, aiming to address the question of customer churn prediction. We explore the construction of a dataset, the selection of a supervised learning model, the identification of misclassified samples, and the implications of our analysis for stakeholders.

Question and Stakeholder:

A telecom company seeking to reduce customer churn by identifying at-risk customers before they leave. The answer to this question will inform decisions regarding targeted retention strategies, personalized offers, and service improvements.

Data Description and Relevance:

To answer this question, we require telecom customer data containing fields such as call duration, monthly charges, contract type, internet usage, customer satisfaction scores, and churn status. Ground-truth labels for churn status are generated based on whether the customer terminated their subscription within a specified timeframe. This data is relevant as it provides insights into factors contributing to churn and enables proactive intervention to retain customers.

Data Collection:

We collect a subset of this data from the telecom company’s database using SQL queries. Alternatively, we can leverage APIs provided by the company’s CRM system to extract relevant customer information.

Supervised Learning Model:

For this analysis, we will use a classification model since the prediction task involves categorizing customers into churn or non-churn categories. Churn status is a binary outcome (churn or non-churn), making it suitable for a classification problem.

Features for Supervised Model:

The features we will use for our supervised model include call duration, monthly charges, contract type, internet usage, and customer satisfaction scores. These features are indicative of customer behavior and preferences, which influence their likelihood of churning.

Misclassified Samples:

After applying our trained classification model, we identify samples where the model made incorrect predictions. For example, a customer labeled as non-churn might have exhibited behaviors similar to churned customers, leading to a misclassification.

Answering the Question:

Our analysis reveals key predictors of churn, such as high monthly charges, short call durations, and contract type. By proactively targeting customers with these risk factors, the telecom company can implement targeted retention strategies to reduce churn rates and increase customer loyalty.

Summary of Findings:

We present visualizations summarizing the distribution of churned and non-churned customers based on the selected features. Additionally, a confusion matrix provides insights into the model’s performance, including accuracy, precision, recall, and F1-score.

Data Cleanup:

During data cleanup, we handle missing values, outliers, and inconsistencies in the dataset. Common bugs encountered include data leakage, where future information is inadvertently included in the training set, leading to overly optimistic model performance.

Limitations:

Limitations of our analysis include potential biases in the dataset, such as sampling bias or selection bias. Additionally, the model’s performance may vary based on the choice of features and hyperparameters, requiring careful validation and tuning.

Conclusion:

By leveraging supervised learning techniques, we gain valuable insights into customer churn prediction, empowering companies to take proactive measures to retain customers and foster long-term relationships. Through continuous evaluation and refinement of the model, businesses can adapt to changing customer behaviors and market dynamics, ensuring sustained growth and competitiveness.