- Customer Churn


You can use a churn prevention model to measure customer satisfaction with your products and determine when satisfaction is declining for a specific customer. This way, you will know when you are at risk of losing valuable customers.

In this way, you can concentrate your resources on customers whose satisfaction is declining, and since it is cheaper to only offer benefits to these customers, you can save money by only carrying out campaigns with benefits to these customers.

Requirements for customer churn

In order to make a Churn prevention model, it is imperative that we have free access to your data, since this is what the model is based on. We may have to do a lot of data augmentation, since the data must be structured in such a way that the model can understand what has happened at specific times. If your data is not of a proper quality, then we can also assist you in collecting the data correctly and ensuring that the data can be used for a time-dependent model.

The model will also be enhanced if all the customers you analyze have some form of subscription to your service, as interactions with such products are higher, and thus we will have more historical data to work with.

Technical possibilities regarding customer churn

Using a Churn prevention model, you get an AI / ML model that can predict the probability of a customer leaving your company / canceling a subscription.

Models are usually set up in the cloud, where you access them via a VPN. You can anonymize the data sent back and forth by processing it locally at your location. In cases where sensitive data is involved, the model can be set up locally with you, so that the data does not leave your premises. Maintaining the model and setting up the environments in which the model must run will require a little extra work.

The process regarding customer churn

Q-analytics process when we make a Churn prevention model is:

  • Initial meeting
  • Workshop
  • Data is accessed
  • Model generation
  • Result / handover

Initial meeting

At the introductory meeting, we will discuss the considerations you have and what you are looking for from the model. Along the way, we will provide feedback and advice so that we can understand your needs and the culture of your business. We will also discuss how the model should be integrated into your systems, as this needs to be considered from the beginning.


During the workshop, we focus on the technical aspects of model generation and its integration with your systems. We have already determined scope and are now focusing on how to implement it. Also, we will go over your data to see what exactly needs to be done. We will get a review of it so that any questions we have can be clarified.

Data is accessed

We get access to the data here so that we can build the model for you. We normally handle data processing in our office, but if your data is so sensitive that it cannot leave your premises, we can easily agree to generate the models with you.

Model generation

Based on the workshop instructions, we generate the model. Additionally, we produce graphs and reports that explain how the model performs so that you know exactly what you're getting.

Result / handover

We hand over the model to you and help you integrate it into your systems as well as present you with the results.