Feedback Theme Analysis

What the Organization asks for before its AI team starts work.

The company

Hartwell Audio. Consumer headphones, sold direct and through three retailers.

What we want

To know what customers actually think, not what we assume

Feedback sources

Retailer reviews, our support tickets, and a post-purchase survey

The problem

We read them separately and we mostly read our own support inbox

What we talk about internally

Battery life, constantly. Our competitors advertise on it.

Volume

About 2,100 pieces of feedback over the last year

What we would do with it

Feed it into the next product cycle, which starts in November

Also supplied

Our last four product meeting notes, so you can see what we discuss

Attached by the buyer.

• Retailer reviews, three retailers (CSV, 1,284 rows)
• Support tickets with text (CSV, 512 rows)
• Survey free-text (CSV, 347 rows)
• Four product meeting notes (DOCX)

1. Client uploads approved text sources and optionally segment metadata.
2. Privacy-aware preprocessing removes unnecessary personal information where configured.
3. Theme agent groups recurring concepts and tracks frequency without treating frequency alone as importance.
4. Evidence agent surfaces concise examples within permitted use limits.
5. Decision agent distinguishes urgent friction from longer-term opportunities.

• Top customer themes
• Positive drivers
• Recurring friction
• Expectation gaps
• Segment differences where available
• Emerging topics
• Representative evidence snippets
• Prioritized questions/actions