This playbook was originally published on G2 AI blueprints.
What problem does this solve?
Support tags count tickets by category, but they can't show that a spike in "shipping issues" is really one SKU, one catalog edit or one fulfillment partner. Early signals look like noise: a couple of customers mention the same odd problem, the tickets get tagged and closed, and the pattern stays invisible until it shows up in refunds, chargebacks and reviews. By then hundreds of orders can be affected.
How does it work?
- Treat a small, unusual cluster as a hypothesis, not noise. Two or three tickets about the same missing item, wrong variant or damaged component are enough to start.
- Connect Claude to Yuma through the Yuma MCP so Claude can search and read your ticket transcripts in plain language.
- Run the root-cause audit prompts. Ask Claude to scan every transcript from the last few days, find every mention of the issue (including paraphrases and other languages), count them and group them by product, SKU, order date, fulfillment location and carrier.
- Ask for every matching ticket ID with a one-line quote. Audit a sample yourself and drop false positives before anything leaves the support team.
- Bring the evidence pack to ops, inventory planning or your 3PL: the count, the clusters and example tickets. Let them trace the root cause, whether that's a catalog or bundle edit, a pick-and-pack error or a carrier problem.
- Pull every affected order from Shopify using the SKU and date window you identified.
- Set up a Yuma outbound playbook to message every affected customer over email or SMS before most of them write in. Explain what happened, confirm the fix and add a gesture such as a discount on the next order.
- Add a Handbook playbook so the agent answers new tickets about the issue consistently, then rerun the query weekly to confirm the volume drops.
What's the biggest win?
Support becomes an early-warning system for operations. A pattern that would take weeks to surface through tags and dashboards gets proven in days from a handful of complaints, with ticket-level evidence ops can act on. Affected customers hear about the fix before they notice the problem, which turns a wave of tickets, refunds and bad reviews into a retention moment.
What's required to run this?
Always ask for ticket IDs and quotes, and never forward an AI-generated count without checking a sample. Search for paraphrases and other languages, not just exact keywords. Start with a tight date window, then widen it once the pattern is confirmed. Compare against a baseline period so you can tell a real spike from normal noise. Copy the audit prompts into a Claude project or skill so anyone on the team can rerun them. Try it on your own tickets with a 30-day free Yuma trial.
What are the constraints?
The workflow requires tickets flowing through Yuma with MCP access enabled. Results only cover conversations Yuma has; tickets handled outside it are missed. AI matching can include false positives or miss oddly phrased mentions, so human review stays mandatory. Confirming the root cause still depends on ops, inventory and 3PL data outside the support stack. Very large date windows may need to be split into batches.
The prompts for this playbook
Use these with Claude connected to Yuma through the Yuma MCP. Replace everything in [brackets].
Prompt 1: scan and count
Search every support ticket transcript in Yuma from [start date] to [end date].
Find every ticket where the customer mentions [the issue, e.g. "an item missing from a bundle" / "wrong color received" / "damaged lid"]. Include paraphrases, misspellings and other languages, not only exact keywords.
Return:
1. The total number of matching tickets, and the number of distinct customers.
2. A breakdown by product, SKU or variant, order date, fulfillment location and carrier, where that data is available.
3. A comparison with the same length of time just before [start date], so I can see whether this is a spike or normal volume.
4. Your verdict in two sentences: is this an isolated issue or a pattern worth escalating, and why.
Prompt 2: evidence for audit
List every matching ticket with: ticket ID, date, order number if present, SKU or product if present, and a one-line quote from the customer showing the issue.
Flag any match you are less than confident about as "uncertain" so I can review it first. Do not drop uncertain matches.
Prompt 3: escalation summary
Write a short summary for our ops, inventory and fulfillment team:
- What customers are reporting, in their words (2 or 3 quotes)
- How many tickets and customers, over what period, and how that compares with the baseline
- Which SKUs, dates, locations or carriers the matches cluster around
- The most likely places to look for the root cause, based only on the ticket evidence
- The ticket IDs they can open to verify
Keep it under 200 words. Do not speculate beyond what the tickets show.
Prompt 4: weekly follow-up
Rerun Prompt 1 for the last 7 days. Compare with the week before the fix went live and tell me whether mentions of [the issue] are going down, flat or still rising.
Rules to keep
- Always audit a sample of ticket IDs yourself before sharing numbers outside support.
- Start with a narrow date window, then widen it once the pattern is confirmed.
- Save these prompts in a Claude project or skill so anyone on the team can rerun them.
