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.
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.
