Hi @Mathieub, 

Welcome to the Community! More than happy to help! 

Do you mind clarifying what you mean by ‘at a certain moment and then do that in a year’? If I’m understanding you correctly, to analyze the church risks of your customers, I think you’ll want to take advantage of our predictive analytics especially our CLV. To figure out the church risks, I would create a segment like this screenshot below and then export this segment as a CSV and check the boxes, especially ‘Churn Risk Prediction’, as outlined in our documentation. 

The export of your segment will give you a lot of data for further analysis, especially estimating the number of returning customers. Our documentation above will give you an equation to find out the number of customers in this segment predicted to return. To lower your average churn risk, we suggest focusing your marketing efforts on retaining customers after their first purchase! 

Thanks for participating in the Community! 

-Taylor 

​@Taylor Tarpley 

Has the “Churn Risk Prediction” property been removed from the available properties available to use in segment definitions and CSV property exports? 

the article you mentioned states something that I don’t find to be true anymore which is:

“Exporting CLV data can allow you to further analyze and predict the behavior of different groups of customers. In addition to your CLV and predictive analytics values, you'll be able to export Churn Risk Prediction. Churn risk will be exported into your CSV as a number between 0 and 1.”

I see each profile shows me predictive analytics, including “Churn Risk prediction” which tells me that my client has enough data to determine predictive analytics. However, When I try to export a segment via CSV, there are no property options to select for export that has anything to do with “Churn Risk”. The only predictive properties I have options to select for export or in segments definitions are the following:

• Historic Customer Lifetime Value
• Historic Number Of Orders
• Average Order Value
• Average Days Between Orders
• Predicted Number Of Orders!-->
• Predicted Customer Lifetime Value!-->
• Expected Date Of Next Order
• Total Customer Lifetime Value
• Predicted Gender