What drives a win
The factors that separate won deals from lost ones, ranked by how much they matter, each traceable to the deals behind it.
AI Product Studio · Revenue and retention
AI win/loss analysis. Learn why customers buy, struggle, and leave.
Your CRM, your help desk, and your churn records already hold the answers. We use AI to read every won and lost deal, every support ticket, and every customer who left — to find what drives a win, where customers are in pain, and why they go — and assess what it would take to put AI to work on it, with the value at stake stated up front.
Why start here
The reason in the dropdown is rarely the real reason.
Loss reasons and cancellation reasons are picked from a short list, in a hurry, at the end. The real story is spread across three systems: the notes and emails on each deal, the tickets your customers file when something hurts, and the trail an account leaves before it cancels. No one has time to read all of it.
AI can. Read together, those records show what wins a deal, which customer pain points keep coming back, and which of them lead to churn — often long before the customer says so. That turns the systems you already run into evidence for where revenue can grow and where it is quietly leaving.
Who it’s for
CEOs, boards, and the leaders of sales, customer success, and support who have years of deals, tickets, and cancellations on record and a hunch about what they mean — and want evidence before they change their market, their product, or their teams.
Where we look
Three records, one customer story.
CRM
Won and lost deals, with the notes, emails, and activity behind them: who you sell to, when, against whom, with which product, and how.
Help desk
Every ticket and conversation: the problems customers hit, how often, how long they take to fix, and which ones keep coming back.
Churn
The accounts that left or shrank, and what happened to them in the months before — usage, tickets, and conversations included.
What you get
Answers your teams can act on.
Customer pain points
The problems customers hit most, ranked by how many customers they touch, what they cost to support, and how closely they track churn.
Why customers leave
The early signals that come before churn, and the revenue at stake in the accounts showing them now.
A feasibility assessment
What AI could do across sales, support, and retention, whether your data is ready for it, and what it would cost and return — before you commit the budget.
How we do it
Connect, read, check, decide.
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Connect and clean
Pull deals, tickets, and churn records together, link them to the same customers, and fix the gaps that would skew the analysis.
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Read everything with AI
AI classifies what drove each deal, what each ticket was really about, and what preceded each departure — from the records themselves.
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Check it against the record
Every finding is traced back to the deals, tickets, and accounts it came from and reviewed by a person, so a pattern is evidence, not a hallucination.
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Assess feasibility
Whether your data can support AI going forward — deal scoring, churn-risk alerts, ticket triage — and what it would take to build.
Value creation
Where the value comes from.
Measured against your pipeline, support load, and retention as they stand before we start. How we measure it.
Win rate
More of the deals you already work, won, by doing more of what wins.
Retention
Revenue kept by fixing the pain points that drive customers away, and catching at-risk accounts early.
Cost to serve
Fewer repeat tickets once the problems behind them are fixed at the root.