Win-Loss Reviews
Ask the buyers, not the algorithm
Structured interviews with the people who chose you and the people who didn't, then wired directly into your AI visibility strategy, so you're building content around real objections instead of inferred ones.
How it works
01
Selection
We work with you to choose the deals that will actually teach you something: recent, material, and spread across wins and losses. Then we handle outreach so your reps aren't asking their own lost deals for feedback.
02
Analysis
Structured interviews run by someone who isn't emotionally invested in the answer. We look for the pattern across deals, not the anecdote inside one.
03
Report
What buyers actually said, what it means for your narrative, and what to do about it, including which story pillars it should change.
Why this sits inside an AI visibility company
When you lose a deal, the buyer usually tells you a reason. When an AI model recommends a competitor, it doesn't tell you anything.
Put the two together and they explain each other. The objection your buyers keep raising is almost always the topic where a competitor out-cites you. Win-loss findings feed straight into your Story Pillars, so the content you publish answers the thing that's actually costing you deals.
That's a loop no dashboard can run and no agency can close.
Start with one. Or run it quarterly.
What you get
Full interview transcripts and recordings
Pattern analysis across the deal set
Narrative recommendations tied to specific objections
Story Pillar updates pushed into the platform