Retaining Customers: Churn Prediction

According to PWC, 32% of all customers would quit doing business with a brand they liked after a single unsatisfactory experience. And 73% of U.S. consumers declare that customer experience is a significant factor in their purchasing decision. It is so powerful that even if they love a company or product, 59% will still walk away after several bad experiences, and 17% will walk away after just one bad experience. A Mckinsey study shows that happy customers are more likely to upgrade or add services and are less prone to canceling. Confronted with poor customer service, 20% of consumers would complain publicly via social media says NewVoiceMedia.
In a nutshell, it is of prime importance to keep customers happy and anticipate the red zone into which a customer might walk out the door and display his/her dissatisfaction publicly on social media.

We want to see whether Quaartz can help identify which customers are likely to leave soon. We use a dataset containing customer-related data, which includes the following information:

1. Customer gender
2. Whether the customer is a senior citizen
3. Whether the customer has a partner
4. Whether the customer has dependents
5. Number of months with the company
6. Has a phone service
7. Has multiple lines
8. Has an internet service provider
9. Has online security
10. Has online backup
11. Has device protection
12. Has technical support
13. Has streaming TV
14. Has streaming movies
15. The contract term
16. Has paperless billing
17. The payment method used
18. The amount of monthly charges

We want to estimate who is likely to leave the company. And we would like to understand the characteristics of those customers likely to leave,i.e., how to recognize them before leaving. It would give us the tools to retain them proactively.