Churn prediction

Find out in advance which customers are at risk of leaving. Find out what drives them to leave and offer them optimal solutions. Our churn analysis will tell you how.

Prediction and retaining the customers we are interested in

Retaining an existing customer is less expensive than getting a new one. Therefore, retaining the customer is a priority for successful companies. One way to retain your customers is to be able to identify the moment and the likelihood of their leaving. Based on historical data and churn analysis, we can predict the probability and define the reasons for leaving relatively reliably. We create customer profiles and optimize the machine learning model for each of them. The result is a score that tells us particular customer likelihood to leave. The analysis also includes recommendations on how to retain the customer, i.e. what to offer them to stay. Customers' behaviour is affected by their current lifestyle or the lifestyle of their families or friends. This is also taken into consideration. 
 

Data Science - Data Mind - Churn prediction

 

DICTIONARY:

(Pro) Active Retention - Customer retention activities based on churn prediction.

Antichurn - Marketing strategy for customer retention. Composed of a predictive component (churn prediction), predicting customer testimonials and subsequent marketing activities such as special retention offers

Churn rate - The rate of customers' withdrawals for reasons such as leaving to competition, etc. Reasons for withdrawal from the contract that cannot be influenced (e.g. moving) are usually excluded from the definition of churn

Churn prediction model - A model predicting the probability of withdrawing for reasons which are avoidable, such as leaving to competition, etc.

Retention - Efforts to retain customer using marketing means

Reactive retention - Customer retention activities after customer has declared complaint or intention to leave to competition

Subscription based - Business model based on regular fees, i.e. usually a monthly fee.