- Employee Churn


The churn prevention model measures employee satisfaction and warns you when satisfaction is declining, so you are aware when you are in danger of losing valuable employees.

Using a churn prevention strategy, your business will have a greater opportunity to retain valuable employees, allowing you to counteract the problems that arise when employees change jobs.

Requirements for employee 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. We can also assist you in collecting proper data to make a time-dependent model if the data is not of the proper quality.

Technical possibilities for employee churn

With a Churn prevention program, you will have an AI / ML model which predicts which of your employees have the greatest possibility of changing jobs.

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 employee churn

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

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

Initial meeting

We will discuss the considerations you have and what you hope to gain from the model during the introductory meeting. We will provide input and advice along the way so we can understand your needs and the culture of your business. Also, we will discuss how the model should be integrated into your systems, as this needs to be taken into account from the beginning.


At the workshop, we discuss both how to develop models and how to make them available, whereby we are concentrating on how we can make this happen. We have determined our scope of work before the workshop, and are now planning out how we can implement it in your systems. 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 instructions we agreed on for the workshop, we develop the model. Additionally, we produce graphs and reports that illustrate how the model performs so that you have a clear understanding of what it is you receive.

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.