Best for
- B2B teams that struggle to find customer stories
- Customer marketing teams looking for testimonial candidates
- Startups with support conversations but few formal case studies
Mine support tickets for customer wins, before-and-after moments, and praise, then turn them into case study drafts and approval-ready testimonial asks.
Create a repeatable pipeline that turns support conversations into case study candidates, draft stories, and customer-friendly testimonial requests.
Quick view
Check the audience, core tools, and access before you start the detailed steps.
Best for
Core tools
5 tools used across the workflow.
Access
Preview the open steps, then unlock the remaining implementation details and prompts.
Why this works
The best customer stories often appear first in support and success conversations, not formal marketing interviews. Tickets reveal the problem, the friction, the resolution, and sometimes the customer's own words of appreciation. This workflow turns those raw moments into a structured story pipeline without asking marketers to manually read hundreds of conversations.
Expected results
Story candidates found
5-15/month
Support tickets often contain multiple resolved customer problems, but only a subset will be strong and safe enough for marketing use.
Time saved
5-8 hours/month
AI reduces manual ticket reading and initial story drafting, while humans still validate customer suitability and permission.
Asset output
1-3 publishable stories/month
Not every candidate will approve, but a structured pipeline increases the odds of finding usable proof regularly.
Proof quality
Customer-language based
The workflow starts from real support conversations, so the resulting stories are grounded in actual problems and resolutions.
Step-by-step workflow
The first 3 steps are open. Pro unlocks the remaining steps, copy-paste prompts, pro tips, tool-by-tool setup guidance, and implementation details.
30 min
30 min
Create a simple scoring checklist before reviewing any tickets. Work in airtable and capture the concrete operating details: source page, target query, search intent, audience question, evidence, draft URL, internal links, and review status. Keep the work narrow: complete step 1, record the decision or asset created, and avoid changing unrelated parts of the workflow. Before moving on, check that the prompt includes the actual inputs, the requested output format, and clear rules against invented facts; the expected handoff is A clear case study scoring checklist for support-ticket review.
A clear case study scoring checklist for support-ticket review.
Do not chase only glowing praise. The best case studies often start with frustration and end with resolution.
30-60 min
30-60 min
Export the last 60-90 days of Zendesk tickets from customers who are active, retained, renewed, or expanded. Work in zendesk and hubspot and capture the concrete operating details: source page, target query, search intent, audience question, evidence, draft URL, internal links, and review status. Keep the work narrow: complete step 2, record the decision or asset created, and avoid changing unrelated parts of the workflow. Before moving on, check that the prompt includes the actual inputs, the requested output format, and clear rules against invented facts; the expected handoff is Support ticket export with customer and account context ready for analysis.
Support ticket export with customer and account context ready for analysis.
Start with resolved tickets only. Unresolved issues can become stories later, but they are risky testimonial candidates now.
45 min
45 min
Paste batches of ticket data into Claude and ask it to score each customer based on story potential. Work in claude and zendesk and capture the concrete operating details: source page, target query, search intent, audience question, evidence, draft URL, internal links, and review status. When you use the prompt template attached to this step, paste the real source material, name the expected output sections, and ask the model to flag uncertainty instead of filling gaps. Before moving on, check that the prompt includes the actual inputs, the requested output format, and clear rules against invented facts; the expected handoff is Ranked list of customer story candidates with suggested angles.
Ranked list of customer story candidates with suggested angles.
Ask Claude to include a risk score. Some tickets look like wins but include sensitive operational details that should not be used publicly.
Pro workflow preview
Previewing 3 of 7 steps
Get the remaining 4 steps, copy-paste prompts, pro tips, tool-by-tool setup guidance, and ongoing workflow and prompt releases.
$9/month
Analyze these support tickets and identify potential customer story or testimonial candidates.
Tickets:
{{support_ticket_export}}
Scoring criteria:
{{story_scoring_checklist}}
For each promising candidate, return:
1. Customer/company
2. Story potential score from 1-5
3. Problem before resolution
4. Resolution or outcome
5. Possible case study angle
6. Exact customer phrases that may be usable as quote inspiration
7. Risk level: low, medium, or high
8. What needs validation from CS before outreach
Do not invent outcomes. Only use what is supported by the ticket text.Related workflows
Continue with workflows that share a similar GTM motion, category, or tool stack.
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A weekly social proof system that captures customer praise, organizes it by theme, and generates ready-to-share posts and graphics.
Create customer-friendly changelog updates from Jira or Linear tickets with benefits, screenshots, release notes, and sales-ready summaries.