Win-Loss Theme Synthesizer
Synthesize the themes across your wins and losses — the reasons that actually recur — and cross-check them against the competitor reviews that confirm them.
Win-loss themes confirmed by competitor reviews — not just memorable deals
'We win on cost' confirmed by
Datadog pricing reviews. UX gap real.
| Theme | Recurrence | Reviews |
|---|---|---|
| We win on cost | High | Confirmed |
| They win on UX | Medium | Confirmed |
| Integration parity | Low | Not in reviews |
Overview
Take a pasted set of win and loss notes, extract the recurring themes, and corroborate them against competitor review patterns (TrustRadius) and the accounts' technographic reality — so the win-loss narrative reflects structural patterns confirmed by external data, not one-off anecdotes.
Use cases
Win-loss that product believes
When a loss theme is corroborated by third-party competitor reviews, product can't wave it away as anecdote. The external evidence makes the feedback actionable.
Separate signal from the loud deal
One painful loss skews everyone's sense of why deals are lost. Theme synthesis across all notes plus review corroboration finds the pattern that actually recurs.
View workflow prompt
# Win-Loss Theme Synthesizer
## Parameters
- `{{win_loss_notes}}` *(required)* — Pasted win and loss notes (deal, outcome, stated reason, competitor). Example: `Acme - Won - displaced Datadog on cost; Beta - Lost - Datadog UX preferred`
- `{{competitor}}` *(required)* — The primary competitor whose reviews corroborate themes. Example: `Datadog`
## Purpose
Synthesize the recurring themes in {{win_loss_notes}} and corroborate each against {{competitor}} review patterns — so PMM ships a win-loss narrative backed by external evidence, not a handful of memorable deals.
## Process
1. **Extract themes** — read {{win_loss_notes}}; cluster wins and losses into recurring themes (cost, UX, integration, support).
2. **Corroborate via reviews** — `get_product_reviews` for {{competitor}}; check whether the loss themes (e.g., 'their UX is better') and win themes (e.g., 'we beat them on cost') show up in third-party reviews.
3. **Technographic context** — `company_technographic` on the win/loss accounts to see whether outcomes correlate with stack signatures.
4. **Rank by recurrence + corroboration** — themes that recur in notes AND appear in reviews are the high-confidence ones.
5. **Narrative** — write the win-loss narrative around the corroborated themes, flagging which need PMM/product action.
## Output Format
Markdown with:
- `# Win-Loss Theme Synthesis`
- `## Win Themes` (table: theme | recurrence | review corroboration)
- `## Loss Themes` (same structure)
- `## High-Confidence Themes` (recurring + corroborated)
- `## Recommended Actions` (PMM vs. product)
- `## Citations`
## Quality Checklist
- Themes are extracted from the notes, not invented
- Review corroboration cites `get_product_reviews`
- High-confidence themes meet both recurrence and corroboration bars
- Small-sample themes are flagged as low-confidence