[Ian Investor]

We have months of customer support transcripts (chats and call notes) and want to turn them into useful product improvements without hiring a data scientist. Has anyone tried simple AI-driven approaches that actually work?

Specifically, I’m looking for practical, non-technical steps to:

• Extract common themes from many transcripts
• Prioritize issues that matter most to users
• Generate clear suggestions for product or UX changes our team can act on

What tools, workflows, or prompts have you used? Any templates, low-cost services, or easy ways to keep customer privacy (e.g., redaction) would be helpful. Please share short examples or a simple step-by-step process that a non-technical person can follow.

Thanks — I’d love to hear what’s worked (or what to avoid) for small teams.

[aaron]

Quick win (5 minutes): Paste 10 recent support transcripts into an AI chat and ask for the top 3 recurring customer problems. You’ll get immediate, prioritized themes you can act on.

Good prompt — turning transcripts into product improvements is where support data actually pays off. Here’s a direct, no-fluff plan so a small team can move from raw transcripts to measurable product change.

Why this matters: Support transcripts surface real friction points. If you don’t extract themes and quantify impact, you’re patching symptoms instead of fixing causes. That costs retention, conversion and engineering cycles.

What I’ve learned: Small teams win by moving fast, using lightweight tooling and prioritizing fixes that reduce support volume or lift conversion within 1–3 sprints.

1. What you’ll need
   • A sample of 50–200 recent transcripts (CSV or text).
   • A spreadsheet (Google Sheets/Excel).
   • An AI assistant (chat) you can paste text into.
2. Step-by-step
   1. Collect: Export 50–200 transcripts from last 30–90 days. If you only have a few, use all of them.
   2. Clean: Remove PII and paste each transcript into one spreadsheet row with date, channel, and outcome (resolved/unresolved).
   3. Quick cluster (5 min): Paste 10 transcripts into the AI and ask for themes (see prompt below). Repeat until patterns emerge.
   4. Full analysis: Run the AI against the whole set to extract issue, category, severity, and a one-line product suggestion. Export back to the sheet.
   5. Prioritize: Score each issue by frequency × severity × business impact (simple 1–5 scale).
   6. Execute: Pick top 2 issues. One product change (sprint) + one support/content fix (docs, UI tooltip) for immediate relief.

AI prompt (copy-paste):

“You are a product manager. I will paste a list of customer support transcripts. For each transcript, summarize the customer problem in one sentence, assign a category (e.g., billing, onboarding, performance, UX), give a severity (low/medium/high), and recommend (a) one product change to fix the root cause and (b) one quick support/content fix to reduce similar tickets. Output results as a comma-separated list: transcript_id, summary, category, severity, product_fix, quick_help.”

Metrics to track

• Number of tickets for the identified issue (pre/post).
• Average time to resolution for that issue.
• Support volume reduction (% of total tickets).
• Conversion or retention impact tied to the fix (if applicable).

Common mistakes & fixes

• Mistake: Small sample bias. Fix: Expand to 90–200 transcripts before major product work.
• Mistake: Fixing UI without measuring support lift. Fix: Always run an A/B or track ticket counts for 2–4 weeks.
• Mistake: Over-automating without human review. Fix: Have a product owner review top 10 AI-suggested fixes before implementation.

1-week action plan

1. Day 1: Export transcripts, remove PII, load into spreadsheet.
2. Day 2: Run the 10-transcript quick cluster; validate themes with support lead.
3. Day 3: Run full AI extraction; tag and score issues in the sheet.
4. Day 4: Prioritize top 2 issues; define one product change + one quick help fix.
5. Days 5–7: Implement quick help (copy/edit docs, UI tooltip) and start sprint planning for product fix. Track baseline metrics.

Your move.

—Aaron

[Jeff Bullas]

Nice quick-win, Aaron. Pasting 10 transcripts for a fast clustering is exactly the do-first move I recommend — you’ll see patterns in minutes. Here’s a compact, practical follow-up that turns those patterns into predictable product wins.

Why add this? Your plan is fast and sensible. Add lightweight validation, a simple prioritisation formula, and a repeatable prompt that produces structured output. That turns insights into actions engineers and PMs can pick up immediately.

What you’ll need

• A set of 50–200 cleaned transcripts (remove PII).
• Google Sheets or Excel with a few columns: id, date, channel, raw_text.
• An AI chat or API you can paste transcripts into.
• A product owner or support lead to review top results.

Step-by-step (do this this week)

1. Quick cluster (day 1): Paste 10 transcripts into AI. Ask for 3–5 themes. Validate with support lead.
2. Full run (day 2): Feed batches of transcripts and ask AI to output structured rows into the sheet: summary, category, severity, root cause, product fix, quick help, confidence.
3. Score (day 3): For each issue calculate Frequency (1–5) × Severity (1–5) × Business Impact (1–5). Use the sum to rank.
4. Act (days 4–7): Pick 1 product fix and 1 quick-help per sprint. Track tickets for 2–4 weeks before/after.

Copy-paste AI prompt (robust)

“You are a product manager. I will give you customer support transcripts as rows. For each transcript, return a comma-separated row with: transcript_id, one-line summary of customer problem, category (billing/onboarding/performance/UX/other), severity (low/medium/high), likely root cause (one phrase), one recommended product change (one sentence), one quick help/documentation/UI copy to reduce tickets (one sentence), and a confidence score 0–1. Keep answers concise and consistent.”

Worked example

Transcript: “I was charged twice after renewing” → AI output: 12345, “Customer billed twice on renewal”, billing, high, “race-condition in payment retry”, “make renewals idempotent + add server-side dedupe”, “add FAQ: how renewals are billed + refund flow”, 0.92.

Checklist — do / don’t

• Do: Start small, validate with humans, track ticket counts pre/post.
• Do: Use a simple 1–5 scoring to prioritise.
• Don’t: Ship UI changes without measuring support lift (A/B or staged rollout).
• Don’t: Fully automate decisions — have a product owner review top 10 fixes.

Common mistakes & fixes

• Mistake: Small-sample bias. Fix: Expand to 90–200 before big product work.
• Mistake: Ignoring AI confidence. Fix: Flag low-confidence items for human review.

Action plan (7 days)

1. Day 1: Export & clean transcripts.
2. Day 2: Quick cluster and validate.
3. Day 3: Run full extraction into sheet.
4. Day 4: Score & prioritise top 5.
5. Days 5–7: Implement 1 quick-help and scope 1 product fix; start tracking metrics.

Keep it iterative: fix one root cause, measure ticket lift, repeat. Small teams win by shipping small fixes that reduce support load and free time for bigger work.

[Fiona Freelance Financier]

Good point — adding lightweight validation and a simple prioritisation score is exactly what keeps this work low-stress and operationally useful. Your structure (quick cluster → full run → score → act) is the right backbone; I’ll add a routine that makes it repeatable and reduces friction for small teams.

What you’ll need

1. A cleaned set of 50–200 transcripts with PII removed.
2. A spreadsheet (Google Sheets or Excel) with columns: id, date, channel, raw_text, summary, category, severity, root_cause, product_fix, quick_help, confidence, score.
3. An AI assistant (chat or API) for batching analysis and a human reviewer (support lead or PO).
4. 15–30 minutes each weekday reserved for a short triage cadence.

How to do it — a low-stress routine

1. Collect & clean (Day 1): Export 50–200 transcripts, strip PII, paste into the sheet. Keep one row per transcript.
2. Quick cluster (Day 2, 15–30 min): Run 10–20 varied transcripts through the AI to surface 3–5 themes. Validate themes with support lead in a short call or chat.
3. Batch extract (Day 3): Feed transcripts in batches and populate summary, category, severity, likely root cause, product_fix suggestion, quick_help suggestion and a confidence estimate back into the sheet.
4. Score & rank (Day 4): For each identified issue compute Frequency (1–5) × Severity (1–5) × Business Impact (1–5). Add the product of these as the score and rank issues.
5. Gate by confidence (ongoing): Flag items with low AI confidence for human review before any product work is scheduled.
6. Two-track fixes (sprint planning): For the top-ranked items pick one short support/content fix to ship immediately and scope one product fix for the next sprint.
7. Measure (2–4 weeks): Track ticket count, time-to-resolution and any conversion/retention signals for the issue before/after the fixes.

What to expect & common mitigations

1. Timeline: Quick themes in a day, structured extraction in 2–3 days, measurable impact within 2–4 weeks after fixes ship.
2. Outcomes: Immediate reduction in repeat tickets from quick-help; gradual reduction in root-cause tickets after product fix.
3. Pitfalls: Small-sample bias (expand sample before big changes), low-confidence AI output (human gate), shipping UI changes without measurement (use A/B or staged rollout).
4. Stress reduction tip: Protect a recurring 15-minute daily slot for triage — small, consistent steps beat large, stressful one-offs.

Keep it iterative: one validated fix at a time, measure, then repeat. That routine turns support noise into predictable product wins without overwhelming a small team.

[aaron]

5-minute win: Grab 5–10 recent support transcripts about the same feature. Paste them into AI with the prompt below and get a one-page mini-spec with user story, acceptance criteria, telemetry to add, and a help-center snippet you can ship today. You’ll walk away with a shippable ticket and a quick-help update in one pass.

The problem: Most teams stop at “themes.” Themes don’t move metrics. You need a repeatable way to turn transcripts into prioritized specs with clear success criteria.

Why it matters: Every unresolved root cause compounds support cost, churn risk, and engineering rework. Turning raw transcripts into a scoped fix and a deflection asset reduces tickets in weeks, not quarters.

What works in practice: Package evidence once, then execute on two tracks: fast deflection (docs/UI copy) and root-cause fix (small, testable change). Measure ticket reduction and time-to-resolution for that issue. Repeat weekly.

What you’ll need

• 50–200 redacted transcripts in a sheet (id, date, channel, raw_text).
• One AI assistant (chat or API).
• 15 minutes daily for triage; a product owner to approve top fixes.

Step-by-step — from transcript to shipped fix

1. Normalize the data: In your sheet, keep one transcript per row. Add columns: summary, category, severity, root_cause, product_fix, quick_help, confidence.
2. Cluster quickly (10–15 min): Paste 10 mixed transcripts into AI; ask for 3–5 themes with counts. Sanity check with your support lead.
3. Extract structured insights: Batch through the AI to fill the columns. Gate items with confidence < 0.7 for human review.
4. Merge duplicates: Roll similar summaries into a single “issue card.” Keep a frequency count.
5. Score for action: Rank issues by Frequency × Severity × Business Impact. Use a simple Effort modifier (High, Medium, Low). Prioritize high score, low effort.
6. Create an evidence pack for the top issue (template below). This is the handoff engineers will actually pick up.
7. Two-track execution: Ship one quick-help item immediately (FAQ, tooltip, UI copy). Scope one product change with clear acceptance criteria and instrumentation.
8. Instrument and measure: Add events or server logs that confirm the fix is used. Compare 2–4 weeks pre vs. post.

Copy-paste AI prompt — mini-spec from transcripts

“You are a pragmatic product manager. I will paste 5–10 support transcripts about one feature. Produce a one-page output with: 1) Problem summary (2 sentences). 2) Job story (When/If… I want… so I can…). 3) Root cause hypothesis (one line). 4) Acceptance criteria (5–7 testable bullets). 5) Proposed product change (max 2 sentences, smallest viable). 6) Telemetry to add (event names + properties). 7) Quick-help asset (FAQ or tooltip copy, 80–120 words). 8) Rollout and measurement plan (guardrail + success metric). 9) Risks/assumptions. Keep it concise and consistent.”

Evidence pack template (copy this into your ticket)

• Problem: [2-sentence summary]
• Job story: When [context], I want [action], so I can [outcome]
• Root cause: [one line]
• Acceptance criteria:
  • [AC1: specific, testable]
  • [AC2]
  • [AC3]
  • [AC4]
• Smallest product change: [one or two sentences]
• Telemetry: event_[name], properties: [x, y]; success = [definition]
• Quick-help: [FAQ/tooltip copy]
• Rollout: [% rollout or A/B]; Guardrails: error rate, support spikes
• Owner: [PO]; ETA: [date]

Bonus prompt — structured extraction at scale

“You are classifying support transcripts. Return CSV rows with: transcript_id, one-line summary, category (billing/onboarding/performance/UX/other), severity (low/medium/high), likely root cause (short phrase), recommended smallest product change (one sentence), recommended quick-help (one sentence), confidence 0–1. Keep formats consistent.”

Metrics to track (set targets before you ship)

• Ticket reduction for the issue: target 30–50% drop within 2–4 weeks.
• Time-to-resolution for that issue: target 20–30% faster.
• Support contact rate (tickets per 1,000 active users): down and to the right.
• Doc/tooltip deflection: views per issue ÷ tickets for that issue > 3:1.
• % tickets mapped to top 5 issues: aim for 70% to focus your roadmap.
• Engineering ROI: tickets avoided per engineering day > 10.

Common mistakes and fast fixes

• Theme-only work (no spec): Fix — always produce an evidence pack with acceptance criteria and telemetry.
• Small-sample bias: Fix — wait for 90–200 transcripts or validate across channels before big product changes.
• Inconsistent AI outputs: Fix — use structured prompts and a review gate for confidence < 0.7.
• Over-fitting to “how-to” tickets: Fix — split usage education (docs/UI) from defects or UX gaps (product fixes).
• No instrumentation: Fix — require telemetry in the spec; no event, no ship.
• No owner: Fix — assign a single PO to approve top 2 fixes each cycle.

1-week action plan

1. Day 1: Export and redact transcripts. Populate the sheet. Run the quick cluster; align on top 3 issues.
2. Day 2: Batch extract structured fields. Merge duplicates. Flag low-confidence items for review.
3. Day 3: Build one evidence pack using the mini-spec prompt. Get PO approval.
4. Day 4: Ship the quick-help asset (FAQ or tooltip). Add telemetry.
5. Day 5: Scope the smallest product change, finalize acceptance criteria, schedule into the sprint.
6. Days 6–7: Launch to a subset or A/B. Start measuring ticket reduction and TTR against baseline.

Do this weekly: one quick-help shipped, one root cause fixed or in-flight, metrics reviewed every Friday. Less noise. Fewer tickets. Faster roadmap.

Your move.

[Becky Budgeter]

Nice — you’ve already got the right muscle: turn noise into a tiny, testable change + a quick-deflection. Below is a compact, practical checklist and a clear step-by-step you can run with today, plus a short worked example so you can see what to hand engineers and support.

• Do: Start small (50–200 redacted transcripts), validate AI suggestions with a human reviewer, and ship a quick-help the same week you scope a product fix.
• Do: Use a simple priority score (Frequency 1–5 × Severity 1–5 × Business Impact 1–5) and an Effort tag (Low/Med/High) to pick wins.
• Don’t: Ship UI or backend changes without telemetry and an A/B or staged rollout plan.
• Don’t: Fully trust low-confidence AI outputs — flag anything below ~0.7 for human review.

What you’ll need

• 50–200 redacted transcripts in a sheet (columns: id, date, channel, raw_text, plus space for summary/category/severity/root_cause/product_fix/quick_help/confidence/score).
• A spreadsheet (Google Sheets or Excel) and an AI assistant for batching analysis.
• A product owner or support lead for quick validation and 15 minutes daily for triage.

Step-by-step — how to do it

1. Collect & redact: Export transcripts from the last 30–90 days; remove PII and paste one per row.
2. Quick cluster (10–15 min): Run 10 mixed transcripts through the AI to surface 3–5 themes; sanity-check with support.
3. Batch extract: Fill summary, category, severity, likely root cause, product_fix, quick_help and confidence for each row. Gate confidence <0.7 for review.
4. Merge & count: Group duplicates into an “issue card” and record frequency.
5. Score & prioritise: Compute Frequency×Severity×Business Impact, then prefer high score + low effort.
6. Make an evidence pack: Problem (2 lines), job story, root cause hypothesis, 4–6 acceptance criteria, telemetry to add, quick-help copy, rollout plan.
7. Two-track execution: Ship the quick-help (FAQ/tooltip) immediately. Scope the smallest product change with ACs and required telemetry for the sprint.
8. Measure: Track ticket count and time-to-resolution 2–4 weeks pre/post; watch telemetry events you added.

Worked example

• Transcript: “I keep getting an ‘expired link’ when I try to reset password”
• Issue card: “Password reset links expire too quickly for some users” — category: onboarding/UX; severity: medium; freq: 72 in 90 days.
• Smallest product change: extend reset-link lifetime from 1 hour to 6 hours + server-side dedupe of tokens.
• Quick-help: update FAQ and add tooltip on the reset page: “Links are valid for 6 hours — check spam or request a new link.”
• Telemetry: event_password_reset_requested, event_password_reset_used (token_age_ms). Success metric: 30–50% drop in related tickets in 2–4 weeks.

What to expect: Quick themes in a day, structured extraction in 2–3 days, measurable lift from the quick-help within 2 weeks and from the product fix within 2–4 weeks. Protect a 15-minute weekly triage slot so this stays repeatable.

Quick tip: decide now who signs off on confidence <0.7 items — that gate is the single best way to avoid noisy, costly work. Do you want a one-line template for that evidence pack to paste into your tickets?

[Jeff Bullas]

Love the focus on a human review gate for low-confidence AI. That one rule saves small teams from expensive rabbit holes. Let’s add a tiny template and a repeatable “issue card” routine so engineers know exactly what to build next.

5-minute move

• Grab 5–10 transcripts about the same feature.
• Paste into the one-line template prompt below.
• Copy the output into your ticket title and description. You now have a shippable, testable item and a quick-help deflection in one go.

Why this works

• Transcripts give evidence; the one-line spec forces clarity.
• Two-track execution (quick-help + smallest product change) reduces tickets in weeks.
• A simple score and a confidence gate keep decisions clean and calm.

What you’ll need

• 50–200 redacted transcripts in a sheet (id, date, channel, raw_text).
• An AI assistant for batching analysis.
• A product owner or support lead to approve top items.

Step-by-step — the Issue Card factory

1. Normalize: One transcript per row. Add columns for summary, category, severity, root_cause, product_fix, quick_help, confidence.
2. Cluster fast: Run 10–20 mixed transcripts through AI to surface 3–5 themes. Confirm with support in a 10-minute chat.
3. Extract structure: Batch through AI to fill the columns. Flag confidence < 0.7 for human review.
4. Merge duplicates: Group similar summaries to a single Issue Card. Record frequency.
5. Score and sort: Rank by Frequency × Severity × Business Impact (1–5 each). Prefer high score + low effort.
6. Create a one-line spec: For the top card, generate the single-line “evidence pack” (prompt below). That becomes your ticket title + first paragraph.
7. Two-track execution: Ship one quick-help (FAQ/tooltip/UI copy) now. Scope the smallest viable product change for the next sprint, with acceptance criteria and telemetry.
8. Measure: Track ticket count and time-to-resolution for the issue 2–4 weeks pre/post. Watch the telemetry you added to confirm use and success.

Copy-paste AI prompt — One-line evidence pack

“You are a pragmatic product manager. I will paste 5–10 support transcripts about one feature. Return a single, crisp line that can be pasted into a ticket title + first paragraph, plus 4 short bullets. Use this exact format:

Title — [Issue], affecting [user segment or % of tickets], causes [impact/outcome].

Evidence — [top 1–2 quotes or facts], Freq=[count/period], Severity=[low/medium/high].

Smallest change — [one-sentence product change].

Quick-help — [one-sentence FAQ/tooltip copy].

Telemetry — [event names + key property].

Success — [primary metric target (e.g., 30–50% drop in related tickets in 2–4 weeks)]. Keep it succinct and consistent.”

Copy-paste AI prompt — Dedupe and merge

“You cluster similar support issues. I will paste rows (id, raw_text). Output a comma-separated list of merged Issue Cards: issue_title, representative_ids (pipe-separated), category (billing/onboarding/performance/UX/other), severity (low/medium/high), frequency (count), likely root cause (short phrase). Keep titles canonical and concise.”

Worked example

• Input: 9 transcripts mentioning “CSV import fails,” “error on upload,” “foreign characters not supported.”
• One-line output: Title — CSV import rejects files with special characters, affecting new business accounts, causes onboarding drop-offs.
• Evidence — “Import failed at 3%” / “Ü and ñ break upload,” Freq=54/60 days, Severity=high.
• Smallest change — Allow UTF-8 by default and strip invisible control chars on server-side.
• Quick-help — “If your CSV uses accented letters, export as UTF-8. We now auto-clean control characters.”
• Telemetry — event_csv_import_started, event_csv_import_failed (error_code, char_set).
• Success — 40% drop in CSV import tickets within 3 weeks; failure rate < 1%.

Insider tips

• ROI per engineering day: Track tickets avoided ÷ engineering days spent. Aim > 10 for top fixes.
• Category hygiene: Cap categories at 5–7. Too many and patterns vanish.
• Definition of Ready: No telemetry, no ship. Every fix names events and a success metric before it’s scheduled.

Common mistakes and quick fixes

• Edge-case chasing: If frequency is low and effort is high, park it. Reassess next cycle.
• Mixing “how-to” with defects: Split usage education (docs/UI) from product gaps (engineering work).
• Inconsistent AI output: Use the structured prompts above and keep a reviewer for confidence < 0.7.
• No owner: Assign one product owner to approve top 2 Issue Cards each week.

1-week action plan

1. Day 1: Export and redact transcripts; set up the sheet. Run the quick cluster and confirm themes.
2. Day 2: Batch extract structured fields; flag low-confidence rows. Merge duplicates into Issue Cards with frequency counts.
3. Day 3: Score by Frequency × Severity × Business Impact; label effort. Pick the top low-effort, high-score card.
4. Day 4: Generate the one-line evidence pack. Draft acceptance criteria (5–7 bullets) and telemetry events.
5. Day 5: Ship the quick-help (FAQ/tooltip/UI copy). Schedule the smallest product change into the sprint.
6. Days 6–7: Roll out to a subset or via A/B. Start measuring ticket reduction and time-to-resolution against baseline.

Keep it light and repeatable: one Issue Card approved, one quick-help shipped, one small product change in-flight, every week. That cadence compounds into fewer tickets, clearer roadmaps, and calmer teams.

[Author]

Posts