How are you applying AI to identify early warning signs that a customer might be considering leaving?
I would love to see any options to identify at risk customers with AI. As I do not know, and very curious.
Hi @VBright2025 
One effective way to predict customer churn is by analyzing how users behave over time. It focuses on clear, measurable data such as how often someone logs in and how long they stay active during each session.
When a customer’s behavior changes noticeably, such as logging in less often these shifts can act as early warning signs of potential churn. This early insight gives your team a chance to step in and re-engage the customer before the relationship is lost.
To apply this in practice, you can start by exporting behavioral data, like login frequency and session duration, into CSV files. This data can then be uploaded to a tool like Claude, which helps analyze trends, identify unusual patterns, and flag users who may be at risk.
We haven’t used AI in this way (yet), but you could use it to read through client communication and perform sentiment analysis to see if there is a change in the way the client is communicating. That way, you can be alerted as soon as a change is detected and then work on addressing the issue in that moment instead of waiting for the client to leave.
@DanielleGriffin!! Great example of leveraging AI for timely, actionable insights. 