Customer Exit Survey. Find out why customers leave — while you can still act on it.
A structured churn research study measuring departure reasons, satisfaction at exit, competitive alternatives chosen, and win-back conditions. More rigorous than a cancellation form dropdown. Designed to surface the patterns behind churn, not just the last-moment explanation.
Primary churn reason
Why did the customer leave? Not the first answer that comes to mind (usually price or switching to a competitor), but the underlying driver — what made them start looking for an alternative in the first place.
Satisfaction at exit
How satisfied were they with the product or service overall at the point of cancellation? A customer who leaves satisfied is a potential win-back. A customer who leaves dissatisfied has a specific grievance that may apply to other customers still on your books.
Warning signals
Was there a specific event, experience, or product change that triggered the departure? Identifying the triggering event is often more actionable than the stated reason.
Competitor destination
What did they switch to? Understanding which alternatives are winning your churned customers tells you who you're actually competing against and what those alternatives are doing better.
Win-back conditions
What would need to change for them to come back? This is the most direct input for product roadmap decisions tied to retention.
Trigger on cancelations
The exit survey is most accurate when sent at the moment of cancellation — or within 24 hours. The further from the cancellation event, the more rationalized (and less accurate) the stated reason becomes.
Keep it short
Exit surveys have lower response rates than satisfaction surveys — customers who've already left have less motivation to help you. The template is designed to be completable in under 3 minutes.
Collect responses automatically
Embed the survey link in your cancellation confirmation email or offboarding flow. Share directly with churned customers via your CRM.
Read your results
Churn reason distribution, satisfaction-at-exit scores, competitor destination frequency, win-back condition categories. Track churn reason patterns over time to detect product or service issues before they show up in aggregate revenue data.
Simple pricing. No surprise invoices.
Common questions
When should I send the exit survey?
At the moment of cancellation, or within 24 hours. The closer to the cancellation event, the more accurate the stated reason. Response rates also drop sharply after 48 hours — churned customers move on quickly. Embedding the survey in the cancellation confirmation is the most effective placement.
What's a typical response rate for exit surveys?
5–20%, depending on the relationship and the length of the survey. Customers who cancelled due to dissatisfaction are more likely to respond (they have something to say) than customers who cancelled because of budget or business change. Keep the survey to 6–8 questions and under 3 minutes to maximize completion.
Should I ask about price separately from the primary churn reason?
Price is the most commonly stated churn reason — but it's often not the real reason. Customers say price because it's the easiest answer to give, not because it was the actual driver. A better approach: include price as one option in the primary reason list, but follow it with "Was there a specific experience that made you decide to look for an alternative?" This surfaces the triggering event that often precedes the price justification.
How is a customer exit survey different from an exit interview?
An exit interview is a 1-on-1 conversation — rich qualitative data but limited scale. An exit survey is quantitative and scalable — you can survey every churned customer and analyze patterns across hundreds or thousands of responses. The two complement each other: exit interviews generate hypotheses about churn drivers; exit surveys measure how common each driver is across the full churned population.
Can I use this data to build a churn prediction model?
Yes. Labeled churn reason data (this customer left because of X) is the training input for churn prediction models. Over time, you can correlate churn reason labels with product usage patterns and customer attributes to identify at-risk customers before they reach the cancellation stage.