feedback-analysislisted

# Feedback Analysis At-scale feedback theming using the theme + sentiment + trend framework: cluster raw verbatims into recurring themes, score each theme's sentiment and volume, track how each moves over time, then rank by priority so the roadmap reflects what the data actually says — not the loudest anecdote. Use this for volume; use synthesize-research for the depth of a handful of interviews. **Grounded in:** *Continuous Discovery Habits* — Teresa Torres: theme at scale and weight by frequency and impact, not loudness. **Go deeper (The Product Channel):** [How Do You Learn from Users](https://sidsaladi.substack.com/p/week-7-how-do-you-learn-from-users) ## When to use this - You exported 800 support tickets / app reviews / NPS comments and need themes, not a wall of text. - Leadership asks "what are the top 5 things customers are unhappy about, and is it getting better or worse?" - You want to compare this quarter's feedback to last quarter and surface what's rising or fading. - You need to rank issues by a defensible signal (volume × sentiment × trend), not by who shouted loudest in Slack. - You're triaging a spike — a bad release, a pricing change, a viral complaint — and need to size it fast. ## Before you start (gather these) - **The raw feedback** — pasted text, CSV, or a connector pull (Intercom/Zendesk/App Store/Typeform/Delighted). One comment per row is ideal. If a connector is available, you may pull from it; otherwise work entirely from pasted data. - **A ti