win-loss-analysislisted

# Win-Loss Analysis Turn scattered deal outcomes into the few patterns that explain most of your wins and losses — and the fixes worth making. ## When to use When deals are closing below target and the reasons aren't clear, the same kinds of deals keep slipping away, or before changing the pitch, packaging, or process — so the change targets a real pattern, not a guess. ## Before you start 1. **Read the brand/product context first.** Pull ICP, positioning, and competitors from `.agents/product-marketing.md` (or `.agents/aaj-brand.md`). If none exists, ask the user for the essentials. 2. **Gather inputs:** a list of recent closed deals — won and lost — each with outcome, segment, competitor (if any), and the stage a lost deal died at. If reasons aren't recorded, run quick win-loss interviews (3–4 questions: why us / why not / who else / what tipped it). 3. **Confirm the objective:** the few patterns driving outcomes, and the fixes they imply. If there aren't enough closed deals to see a pattern, say so and gather more first. ## Method Most wins and losses trace to a handful of repeated causes, not unique stories. Code outcomes into a small, consistent set of reasons, rank them by frequency *and* revenue weight, and separate what you can fix (pitch, process, packaging) from market reality (no budget, bad fit). Patterns usually differ by segment and competitor — read those cuts before concluding. ## Workflow 1. **Assemble the set.** Label each deal won/lost, segment, co