Which early behaviors mark the users who stay?

Early behaviors predicted who stayed, the models improved retention prediction by 23%, and the team acted on the recommendations.

• Setting: Consulting client (details confidential)

• My role: Research and analytics lead

• Data: Product usage data

• Methods: Stakeholder scoping, metric definition, predictive modeling

• Outcome: +23% retention prediction

The question

A client wanted to know why some users kept using its product while others drifted away, and what the product team could do early on to change that.

What I did

I scoped the question with stakeholders and agreed on what to measure in the product data. I then separated behaviors that came before users stayed from behaviors that only accompanied staying, because only the first kind gives a team something to act on early. Finally, I built predictive models of retention around those early signals.

What changed

The models improved retention prediction by 23%. I turned the results into recommendations for product and non-technical stakeholders, and they acted on them.

What it shows

The useful part was translating a broad worry about churn into a measurable question, and then into specific decisions a team could make.