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How can Churn Prediction help you?
Churn Prediction helps you to identify potential quitters and their motives at an early stage. In this way, you can take specific measures to retain customers and save new acquisition costs.
Our individual software solution provides you with a churn probability for each of your customers as well as a clear presentation of the most important reasons for churn.
Prevent your customers from churning.
Identify reasons for churn
Find out why your customers are churning.
Discover vulnerable product categories and markets.
Increase customer loyalty
Increase customer happiness and loyalty in the long term.
What is churn prediction?
Churn prediction means the ability to calculate the churn probability at any time, individually, for every customer. For this purpose, machine learning processes evaluate contract, transaction and customer relationship data, for example, and recognize patterns that indicate migration. In this way, explicitly endangered product categories and markets can be identified quickly and easily. A clear presentation of the most important reasons for migration enables you to react quickly and with the right measures
Recovering lost customers is usually 10 times more expensive than keeping an existing customer. Nevertheless, assessments are often based on empirical values and are sometimes very imprecise. The aim of Churn Prediction is to identify and prevent existing customers from churning at an early stage. Before your customers switch to the competition, you can use this early warning system to bind them to the company with attractive offers. Not only can churn be monitored and controlled, but knowledge about the reasons for churn can also be gained. This enables potential emigrants to be identified with an accuracy of 72%.
At a glance
All you need to know about Churn Prediction
The increasingly transparent presentation of offers by competitors on the Internet is constantly increasing competitive pressure. With the help of churn prediction, potential graduates can be identified at an early stage, marketing measures can be controlled accordingly, and sales costs can be reduced. But how does the implementation succeed and which data is required for the implementation? And how exactly is machine learning used for all of this? You can find out all of this here.
Why Churn Prediction?
Churn prediction has enormous potential in terms of cost reduction combined with a significant increase in efficiency in sales and marketing. The following advantages of churn prediction can be specifically named:
- Detection of a dissatisfied customer who is willing to leave before it comes to churn
- Uncover the causes and reasons for churn
- Reduce the cost of sales and marketing campaigns
- Improved customer approach with targeted marketing measures
- Identification of explicitly endangered markets & product categories
Where can Churn Prediction be used?
Finding out why a customer no longer buys is by no means trivial, but involves high costs and time. An early warning system can help to bind customers to the company with attractive offers even before they switch to the competition. This enables a wide range of possible uses for churn prediction, as both private and business customers can be analyzed in terms of their behavior and characteristics. Thus, churn prediction is helpful for all areas, no matter how long the life cycles of your customers are.
What kind of data do I need for Churn Prediction?
The following three data sources are particularly relevant for the implementation of an early warning system and a churn analysis with churn prediction:
- Contract data
- Transaction data
- Customer relationship data
The contract data form the basis of the required data. These include on the one hand demographic and structural customer data and on the other hand data on services or products used, as well as the agreed conditions.
Transaction data can be taken from customer history and provide many important starting points for analyzing the likelihood of churn. In addition to the purchase and order history, the important transaction data also includes data such as payment methods and reminders as well as returns and cancellations
Customer relationship data is probably the most important group of data for the successful implementation of churn prediction. Possible sources are, on the one hand, customer service that provides data about which customers complain about which products and how often. Feedback data can provide information about which products or services customers have difficulties with and which features are more and which are less important to them.
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