Why are customers leaving, and what can you do about it?
For a subscription product, paid newsletter or service with recurring customers. Work through it in order: first find out why people leave, then catch the warning signs earlier, then decide what to do when someone cancels. Each workflow keeps its own evidence label, and most results here are self-reported, so treat them as ideas to test, not promises.
7 workflows, picked and ordered by Founder Workflows. The notes are ours; each workflow's own page has the full details, sources and limits. Updated October 20, 2026.
Start here. Before fixing anything, pull every cancellation reason into one place and rank the themes by the revenue they cost. It works in a spreadsheet, and the author suggests bringing in AI once you have about 50 responses. Written by the maker of a churn tool, and no results are claimed.
A solo founder's step-by-step method for pulling cancellation reasons from Stripe, surveys and support tickets into one place, tagging them by theme, and ranking themes by how much revenue they cost.
If you run surveys, this tags every open-text answer with themes and a churn signal using a cheap model, then emails a weekly summary. It is documented step by step (about two hours to build), but its churn rules are untested guesses, so check a sample by hand.
A step-by-step build that tags each open-text survey response with themes, sentiment and a churn signal using a cheap Claude model, alerts on risky ones, and emails a weekly summary of themes.
Already using product analytics? Let Claude Code look through your events and interview notes to explain a drop in payments or where new users stall. One person's walkthrough with no data shown, so confirm any numbers in PostHog itself.
A product person connects Claude Code to PostHog and a folder of company context and interview transcripts, then asks it to explain a revenue dip, a funnel drop and user segments.
Many cancellations start in the first days. This developer replaced three basic emails with sequences based on what each user had done, and reports trial-to-paid rising from 12% to 22% and monthly churn falling from 8% to 4.8% over six weeks. There was no control group, and the post gives two different starting figures.
A developer says a SaaS with only welcome, reset and receipt emails lost users after a day, and a Claude Code command planned and built six lifecycle sequences that check app state between sends.
Spot customers who were active and then went quiet, send them a re-engagement flow, and hold back a group that gets nothing so you can tell whether it worked. The only workflow here measured against a control group. It is a vendor case study from a large retailer, so copy the method rather than expect the numbers.
A Danish retailer used a vendor's explainable AI to find paid loyalty members who had been active and then went dormant, sent them a personalised flow, and compared them with a group that received no flow.
Turn each cancellation into a short brief from billing, support and account history while it is still fresh. A vendor example built for bigger teams with Zendesk and Salesforce; a solo founder can do the same by hand with an AI assistant.
A Tines example workflow fires on a Stripe cancellation, lets an AI agent look up the customer's support tickets and account history, and posts a structured churn brief to Slack.
Try to win people back with a tailored offer and a short email sequence. The founder says several emails recovered about twice as many customers as one, but gives no numbers behind it and sells the tool.
A founder describes a tool where a Stripe cancellation triggers an AI strategist that builds a personalised retention offer and a multi-step email sequence, and reports multi-step sequences doubled recovery.