Content CreationbeginnerFree

Turn customer support tickets into case study drafts and testimonial requests

Mine support tickets for customer wins, before-and-after moments, and praise, then turn them into case study drafts and approval-ready testimonial asks.

What you will have

Create a repeatable pipeline that turns support conversations into case study candidates, draft stories, and customer-friendly testimonial requests.

Setup time

1-2 hours

Time saved

5-8 hours/month vs. manually searching for customer story candidates

Estimated cost

$20 to $100 per month

Tools used

5 tools

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.

Step-by-step workflow

Run the workflow

This workflow is fully available. Follow the steps below to build the system from start to finish.

Define what counts as a story-worthy ticket

30 min

Create a simple scoring checklist before reviewing any tickets. Good candidates include clear before-and-after moments, solved high-friction problems, praise from the customer, measurable improvement, expansion signals, or repeated usage success. Exclude tickets that are unresolved, angry, legally sensitive, or too technical to explain externally.

Output

A clear case study scoring checklist for support-ticket review.

Airtable

Pro tip

Do not chase only glowing praise. The best case studies often start with frustration and end with resolution.

Export recent support tickets with customer context

30-60 min

Export the last 60-90 days of Zendesk tickets from customers who are active, retained, renewed, or expanded. Include ticket subject, customer name, company, ticket body, resolution status, tags, CS owner, and customer satisfaction rating if available. Add relevant account context from HubSpot so you can prioritize important customers.

Output

Support ticket export with customer and account context ready for analysis.

ZendeskHubSpot

Pro tip

Start with resolved tickets only. Unresolved issues can become stories later, but they are risky testimonial candidates now.

Use Claude to identify case study candidates

45 min

Paste batches of ticket data into Claude and ask it to score each customer based on story potential. Have it identify the problem, resolution, proof angle, customer quote candidates, and whether the story needs customer success validation before outreach.

Output

Ranked list of customer story candidates with suggested angles.

ClaudeZendesk

Pro tip

Ask Claude to include a risk score. Some tickets look like wins but include sensitive operational details that should not be used publicly.

Prompt template

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.

Create a case study candidate tracker

30-45 min

Move the ranked candidates into Airtable. Add fields for customer, account owner, story angle, proof strength, risk level, permission status, CS validation status, draft link, testimonial request sent, and next step. This becomes your lightweight customer marketing pipeline.

Output

A case study and testimonial candidate pipeline organized by priority and approval status.

Airtable

Pro tip

Add a 'do not contact yet' status. Customer success teams need a way to protect sensitive accounts without killing the entire idea.

Draft the case study narrative from ticket evidence

30 min per candidate

For each approved candidate, use Claude to create a short case study draft. Keep it honest: use the ticket evidence for the problem and resolution, then clearly mark any missing details that require a customer interview. The draft should include headline, summary, challenge, solution, result, quote placeholders, and follow-up questions.

Output

First-pass case study drafts based on real support conversations.

ClaudeAirtable

Pro tip

Mark unknowns visibly. A draft with clear gaps is useful. A draft that invents missing metrics is dangerous.

Prompt template

Draft a short B2B case study from this support-ticket evidence.

Customer context:
{{customer_context}}

Ticket evidence:
{{ticket_evidence}}

Known outcome:
{{known_outcome}}

Rules:
- Do not invent metrics or quotes
- Use plain, customer-friendly language
- Clearly mark missing information as [NEEDS VALIDATION]
- Keep the draft concise

Return:
1. Working headline
2. 2-sentence summary
3. Challenge section
4. Solution section
5. Result section
6. Quote placeholders based on ticket language
7. Follow-up interview questions

Generate testimonial request emails

20 min

Use Claude to draft a friendly testimonial or case study request email for each customer. The email should reference the resolved issue in a respectful way, explain why their story may help peers, and make the ask easy. Include a lighter option: approve a short quote instead of committing to a full case study.

Output

Customer-friendly testimonial request emails ready for CS or marketing to send.

ClaudeHubSpot

Pro tip

Give customers a low-friction yes. Many will approve a short quote even if they will not commit to a full case study interview.

Prompt template

Write a testimonial or case study request email for this customer.

Customer: {{customer_name}}
Company: {{company_name}}
Resolved issue or win: {{resolved_issue}}
Proposed story angle: {{story_angle}}
Sender: {{sender_name_and_role}}
Relationship context: {{relationship_context}}

Create:
1. A warm email asking for permission
2. A shorter fallback ask for a one-line quote
3. A subject line

Tone: respectful, specific, low-pressure. Do not sound like a mass marketing request.

Polish approved drafts into publishable assets

45-90 min per approved story

Once the customer approves or provides additional detail, use Claude and Grammarly to polish the case study into your preferred format. Create a short website version, a sales proof snippet, and a social post from the same story so the work becomes reusable across channels.

Output

Final case study copy, testimonial snippet, and social proof asset.

ClaudeGrammarly

Pro tip

One customer story should become at least three assets: long-form proof, sales snippet, and social proof. Otherwise you are underusing the approval effort.

Prompt template

Turn this approved customer story into reusable marketing assets.

Approved story details:
{{approved_case_study}}

Customer-approved quote:
{{approved_quote}}

Create:
1. Website case study version
2. 100-word sales proof snippet
3. LinkedIn post
4. Email nurture snippet
5. One-slide sales deck proof block

Do not add claims beyond what the customer approved.

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.

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