Capture Launch Signals
Collect feedback by source, category, and sentiment so it informs the debrief.
What signals are
Signals are quotes, comments, support tickets, DMs, replies, and observations from real people during and after the launch. Each one is a small data point that, in aggregate, tells you what landed and what did not.
Sources
- Product Hunt comments and DMs
- X/Twitter replies and quote-tweets
- LinkedIn comments
- Reddit and Hacker News threads
- Customer support tickets and emails
- Sales calls and demo notes
- Community channels (Slack, Discord)
Categories
- Positioning — how people described what you do.
- Pricing — reactions to plans and pricing.
- Product — feature requests, bug reports, UX friction.
- Onboarding — where new users got stuck.
- Comparison — what they compared you to.
- Praise — quotes worth turning into testimonials.
Sentiment
Tag each signal as positive, neutral, or negative. Sentiment is about the person's reaction, not your interpretation. Negative signals are usually the most useful — they show you the next thing to fix.
Turning feedback into themes
After the launch, group signals into themes — three to seven recurring patterns that span sources. Themes feed directly into the debrief's "feedback themes" section.
When AI summarization is available
When an AI provider is configured, LaunchRoom can suggest themes across your signals and surface representative quotes. AI summarization requires configuration — until then, the workspace shows a clear "AI not configured" notice instead of pretending to summarize.
Manual grouping by category and sentiment
Without AI, group signals manually using the category and sentiment filters. Export the list, talk through it with the team, and write the themes into the debrief. The signals data is real either way — only the synthesis step changes.
Still need help?
Reach a real person at LaunchRoom. We do not pretend to have an SLA we cannot keep — replies are written by humans.