Your CRM, loyalty programme and order history often contain signals of churn long before a customer actually leaves.
We analyse those patterns and build predictive models to identify who is at risk, what predicts churn and when you still have time to act.
But CRM data alone rarely tells the whole story.
Three sources and one diagnosis.
Your customer data
CRM, transactions, and customer behaviour show us what happened and which patterns predict churn. But if the reason was never captured, no model can magically find it.
What your own people say
KAMs, Sales and Customer Support know things that never reach the database. Their perspective is valuable but inevitably shaped by the part of the customer relationship they see.
What customers say (but not always do)
Sometimes you simply have to ask. Instead of long surveys answered mainly by people with strong opinions or unusual patience, we use short, targeted research to fill the gaps the other sources can't explain.
Enqoa combines all three into a fast, pragmatic, cost-efficient diagnostic.
The result is a way to identify at-risk customers and take action while they're still customers.
How ENQOA performs churn rate analysis
Churn rate analysis is the work of measuring how many customers you lose over a period, finding out which customers are at risk next, and explaining why. Most companies only do the first part. ENQOA does all three in a fixed-scope diagnostic, typically over four to eight weeks.
1. Define and measure churn
We agree on one churn definition with you (cancelled, downgraded, or silently inactive), reconcile customer IDs across billing, CRM and product systems, and calculate churn rate by segment, cohort and point in the customer lifecycle. This shows where the loss is concentrated, not just how large it is.
2. Find the signals that predict churn
Using your transaction, usage and support history, we identify the behaviours and events that precede cancellation, such as falling order frequency, a spike in support contacts or a downgrade before renewal, and build a model that scores every current customer on likelihood to leave.
3. Explain the why
Data shows what happened, not why. We interview the people who talk to customers every day, then run short targeted research with churned and at-risk customers to confirm or reject the explanations.
4. Turn it into action
You get a ranked list of at-risk customers, the reasons behind each pattern, and retention actions prioritised by expected effect, with owners, so the work starts while they are still customers.