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In the dynamic world of telecommunications, customer churn remains a pressing challenge for companies striving to achieve sustained success. Customers have alot of options and evolving preferences, accurately predicting churn has become important to retain and satisfy them. Customer churn is basically the loss of customers.
In this project, we use Supervised Machine Learning (classification) to explore the significance of churn analytics as a strategic tool for telecommunication companies to proactively identify potential risk factors for churn, optimize retention efforts, and cultivate lasting customer relationships. By leveraging data-driven insights and advanced analytics, companies can gain a competitive edge and ensure sustainable growth in an ever-changing industry.
The project followed the The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model that serves as the base for a data science process.
Steps of the project
The project consists of the following sections:
- Business Understanding
- Data Understanding
- Data Preparation
- Modeling
- Model Evaluation
- Conclusion
The churn analytics predictive model is a data-driven solution designed to address the persistent challenge of customer churn in subscription-based industries. This model aims to identify customers at risk of churn, enabling businesses to take proactive measures and implement targeted retention strategies.
The primary objective is to reduce customer churn rates and retain valuable customers by providing insights into customer behavior, preferences, and patterns. Utilizing machine learning models such as logistic regression and decision trees, the model evaluates the data to build accurate predictions.
Its performance is assessed through metrics like accuracy, precision, recall and F1-score. Armed with the model’s insights, businesses can create personalized offers, marketing campaigns, and proactive customer support initiatives, thus improving customer satisfaction and fostering loyalty.
Hypothesis
Null Hypothesis (H0): “There is no significant relationship between customer tenure and churn rate in the telecom company.”
Alternative Hypothesis (Ha): “There is a significant relationship between customer tenure and churn rate in the telecom company.”
Research Questions
1.How does customer tenure relate to churn rates? Are long-tenured customers more likely to stay with the company, and do new customers exhibit higher churn behavior?
2 .Is there a correlation between the total charges and churn rates? Do customers with higher total charges exhibit different churn behavior compared to those with lower total charges
3. What is the impact of contract type on churn rates? Do customers on long-term contracts have significantly lower churn rates compared to those on short-term contracts?
4.Are there significant differences in churn behavior between customers who have device protection and those who don’t?
5.What is the relationship between the availability of tech support and churn rates? Are customers with access to tech support more likely to remain with the company?
6 . Do streaming services play a role in customer churn? Are customers with streaming services, such as StreamingTV and StreamingMovies, more likely to stay with the company?
7.How does the choice of payment method impact churn rates? Are customers with specific payment methods more prone to churn than others?