Question
How to score deals when you have no history to learn from.
A model trained on your historical outcomes needs historical outcomes. A new product, a new segment or a young company has none, which is precisely when a score you can define yourself beats one that has to be learned.
The cold start problem.
Every model that learns from your history needs enough of it. Launch a new product into a new ICP and the history that exists describes a different business selling a different thing to different people.
HubSpot documents this about its own predictive score: it needs a critical mass of closed deals before predictions stabilize. The same constraint applies to any learned model, however large the training set behind it.
Where learned models are weakest.
A new product with no closed deals. A new segment where the buying process is different. A young company with thirty closed deals total. A pivot that made last year history misleading rather than useful.
These are not edge cases. They describe most companies under fifty people and every company launching something.
What works instead.
Define the criteria yourself, from what you believe a good deal looks like in this motion. Which roles have to be involved, which process steps have to start, how fast a real buyer replies.
You have something the model does not: you know what you are selling and to whom, before any of it has happened.
Then check it as evidence arrives. Ten closed deals is not a training set and it is enough to tell you whether your criteria are pointing the right way.
Why the weights have to be yours.
A score you can define is available on day one. A score that has to be learned arrives after the quarter you needed it.
Kaypo starts from weights you set, so it is useful before you have any history at all. As closed deals accumulate, the back test runs them against your own outcomes and tells you which of your assumptions held. You adjust, and it gets sharper every month.
The result goes to each rep in Slack every morning with the reason beside every number, from the first week rather than the second quarter.
Questions
Can you score deals without historical data?
Deals can be scored without historical data, if the model is one you define rather than one that has to be learned. Set the criteria from what you know about the motion, then test them against outcomes as closed deals accumulate.
How much closed deal history does deal scoring need?
A learned model typically needs a critical mass before predictions stabilize. A model with weights you set is useful immediately and improves as history arrives.
What is the best deal scoring approach for a new product?
One where you define the buyer criteria, required roles and milestones yourself. Historical baselines cannot help with a motion that has no history.
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.
$99 a month, cancel any time.. Everyone on the team. No per seat charge. Connect a CRM and it runs. No implementation fee. 14 day trial. No card.
Related reading
Can you change how your deal score is calculated
Most deal scores cannot be opened or adjusted. Why that matters the first time one is wrong about your business, and what an editable model gives you instead.
Two ways to configure a deal score, and what each one lets you change
Some tools let you define milestones and roles. Some let you set the numbers. The difference decides what you can do when the score is wrong.
Kaypo vs Backstory
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.
Why that automation you built never actually fires
Silent failure is the defining risk of GTM automation. The three usual causes and how to verify one is working.
Reviewed September 20, 2026 against the product as it behaves today.