**The problem:** Solving customer churn.</p>
You'll be working with a fictitious churn dataset from a Telecom provider called Databel. You are hired as a consultant.</p>
**The task:** Discover why customers are churning.</p>
The churn rate, also known as the rate of attrition or customer churn, is the rate at which customers stop doing business with an entity.<br>
It's easier to retain customers than to attract new customers, so for many companies it's a priority to reduce churn.</p>
*Churn rate = customers lost * 100% / total number of customers.*</p>
**Key characteristics of the data:**
* Databel, a fictitious Telecom provider.
* One big table containing 29 columns.
* One row per customer.
* Snapshot of the database at a specific moment in time.

**Key insights:**
* The churn rate for Databel is ~27%.
* ~45% of the reasons why customers churn are related to competitors.
* The churn rate in California is abnormally high (>60%).
* The churn rate for customers who are on a monthly contract is 46%. Almost 1600 out of 1800 churners come from this group.
* For a two-year contract, the churn rate is 2.78%.
* The churn rate for customers who pay for an international plan, but don't call internationally, is 71%.