Baremetrics Cancellation Insights 2.0 - Cancellation feedback with email surveys & revenue reports

When we launched the first version of Cancellation Insights last year, we knew we were on to something special. We've taken a practice like a cancellation survey, which people typically hack together in-house and then do almost nothing with the data, and tied it back to real, actionable insights. Which missing features are costing you the most customers? Are your infrastructure efforts reducing churn? Which competitors are costing you the most money? Cancellation Insights can track all of that and more! Previously, to start getting those insights, you needed to install code in your app. But no more! Today, we've released email cancellations surveys! We'll automatically email users who cancel with an in-email survey to gather more data on why they cancelled! You can be up and running with a simple toggle of a switch. We can now also collect those same insights for trial cancellations as well. This is all in addition to the other cancellation tools that were already part of Cancellation Insights, like an in-app cancellation form, automated followups, graphs showing revenue breakdowns and more! Get started: https://baremetrics.com/features...

Love cancellation insights, this is great!

This deserves more upvotes. Speaking as a user (who occasionally churns) of 25 SaaS apps at work, I am willing to give feedback...but you need to make it easy for me. I would answer these Baremetrics questions. Increasing your churn feedback sample size has some under-rated benefits. If you take a less standardized approach than Baremetrics has here (e.g asking ex users to type out their rationales)... you will end up with a barbell of responses at each extreme. On one end the soft-hearted who don't want to offend you (they will cite budget) or maniacs who want to overreact to every minor issue. Some research I worked on (https://appealie.com/saas-review...) highlights the problem of small sample sizes - your results can be distorted just by mix of personality types. My sense is Baremetrics approach here would get you a better distribution than less convenient methods.