Turn subscription cancel requests into saved subscribers with AI

Subscription brands lose a large share of subscribers through support: a customer writes in to cancel, waits in a queue, and gets either a generic retention script or a cancellation with no attempt to understand why. Many of those customers only needed a skip, a pause, a different frequency or a product swap. Agents spend hours on routine subscription changes, and the reasons people leave stay buried in tickets nobody analyzes.

1. Connect Yuma to Shopify and your subscription app (Recharge, Skio, Loop and others) so the agent can see each subscription and take actions like skip, pause, reschedule, change frequency, swap products, apply a discount or cancel.
2. Connect Claude to Yuma through the Yuma MCP and run the subscription save prompts (https://docs.google.com/document/d/1eYvYQIpuocXBp7hGha6IQ6YzzPF8aAefFUBFimC2SY8/edit?usp=sharing) on the last 90 days of cancellation requests. Group them by reason and count how many could have been solved with a change instead of a cancel.
3. Build a save-offer map with one offer per reason: too much product gets a skip or a longer frequency, price gets a one-time discount, travel gets a pause, taste or fit gets a swap, a move gets an address update.
4. Write a cancellation playbook in the Yuma Handbook. Ask for the reason if the customer hasn't given one, make the single most relevant offer, and apply it instantly in the subscription app. If the customer declines or repeats the request, cancel right away and confirm it.
5. Add tests before you publish, including a customer who firmly says "cancel now" (the agent must cancel without pushing back), an upset customer, and a refund request on a recent charge.
6. Roll out on part of your volume first and read transcripts daily during the first week.
7. Measure every week with the reporting prompt: save rate by reason, which offers get accepted, and how many saved subscribers are still active 30 and 60 days later.
8. Adjust the offer map based on what actually retains subscribers, update the Handbook and rerun the tests.

Cancel requests get an answer and an action in the same conversation, at any hour, instead of a queue and a generic script. Customers who only needed a skip, pause or swap stay subscribed, agents stop spending their day on routine subscription changes, and the brand finally gets clean data on why subscribers leave, by reason and by product.

Make one offer per conversation and match it to the stated reason. Keep discounts one-time and capped so saves don't quietly erode margin. Track whether saved subscribers are still active after 30 and 60 days, not just whether they accepted the offer in the moment. Keep the reason categories stable so you can compare month to month. Run steps 2 to 7 on your own tickets with a 30-day free Yuma trial (https://yuma.ai/support-ai).

Clear cancellation requests must always be honored, and the flow has to follow the auto-renewal and consumer protection rules in each market you sell in. Available actions depend on the subscription app; each platform supports a different set of changes. A save that cancels again next month inflates the save rate, which is why the 30 and 60-day check matters. Heavy discounting can retain subscribers at a loss.