Question
Almost every deal score on the market is a model you cannot open, cannot adjust and cannot audit. An editable model can be proven against your own closed deals. A proprietary one can only be trusted.
A proprietary model is trained somewhere else, arrives as a number, and cannot be questioned. Vendors describe this as an advantage: it adapts, it learns, you do not have to maintain anything.
An editable model shows its inputs and their weights, and lets you change them. You can see why a deal scored 31 and you can disagree with it.
Every scoring model will eventually be wrong about a deal your team understands better than it does. When that happens with a proprietary model, there is nothing to do. You cannot see what it weighed, you cannot tell it that procurement takes six weeks in your business, and the only recourse is a support ticket.
When it happens with an editable model, you open it, see that security review was worth 18 points, decide that is wrong for your market, and change it.
This is the whole reason scores go unused. A rep who cannot see why a number moved argues with it. A rep who can see the inputs acts on it.
The strongest version of an editable model is one you can test. Take your weights, run them against every deal you have already closed, and see whether they actually separate your wins from your losses.
That is a checkable claim. It either separates them or it does not, and you find out before you ask anybody to trust a number.
A proprietary engine cannot offer this. It can tell you it was trained on a great deal of data, which is a reason to trust it, not evidence that it works on your pipeline.
Every score lists its inputs and exactly what each one contributed. The weights are yours to set. A back test runs them against your own closed deals and reports how cleanly they separate wins from losses.
Then the result goes to each rep in Slack every morning with the reason beside the number, which is the only form in which a score gets used.
Questions
Not for the predictive score. It cannot be inspected or edited. You can build a separate manual score with a calculated property, which you then maintain yourself.
Because they cannot see why a number moved. A score with no visible reason gets argued with, and a score that gets argued with stops being used within a quarter.
Running your scoring weights against deals you have already closed to check whether they separate wins from losses. It turns a score from something you trust into something you have checked.
It arrives in Slack
Every morning, each rep gets one message naming the deals on their own book that moved overnight, biggest mover first, with the reason beside each number and the deals that have gone quiet underneath.
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Related reading
Backstory is enterprise revenue intelligence for CROs, live in two to four weeks. Kaypo scores every deal and posts to Slack the day you connect.
The three ways to score deals in a CRM, what each costs in time and money, and why most home built scoring models stop being maintained within a quarter.
HubSpot ships a predictive deal score with Sales Hub Professional, and its own documentation says not to rely on it. Why that matters.
Vendors argue that per contributor pricing means you do not pay for viewers. Whether that is cheaper depends entirely on the shape of your team.
Reviewed September 20, 2026 against the product as it behaves today.