A telco company (telecommunications service provider) is trying to improve customer retention by predicting customer churn behavior to develop focused retention programs. In this project, I will not only build and selected the best model to predict customer churn, but also extract and analyze important features related to customer churn, in order to recommend and inform business actions to reduce customer churn.
Telco-Customer-Churn.csv
- Customers who left within the last month – the column is called Churn
- Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies
- Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
- Demographic info about customers – gender, age range, and if they have partners and dependents
From the EDA, we know that these demographics have a high churn rate:
- customers using Fiber optic as their internet service
- customers without online security
- customers without online backup
- customers without tech support
- customers with a month-to-month contract
- customers who use electronic check as the payment method
- customers who pay a high monthly charge
- customers who are subscribed for only a few months
Potentially, these insights can be translated into business decisions. For example, Telco could -
- Change the pricing strategy for month-to-month contract so that customers would prefer to adopt yearly contracts
- Investigate why customers who pay with electronic checks have a high churn rate, and potentially make the electronic check payment process smoother and more user-friendly
- Develop a focused retention program for new customers to retain them