I recently watched a sales rep named Marcus blow off an inbound lead with a lead score of 94 out of a possible 100.
We were doing pipeline review and his manager pulled up the lead score column to ask why he hadn't touched the hottest MQL in the queue.
Marcus had spent his time on a 61 instead.
The 61 was a Series B company nobody had heard of, a lower-level contact, fewer page views. On paper it was a worse lead in every measurable way. His manager asked him to explain why, and Marcus pulled up LinkedIn. The 94's founder had posted three weeks earlier about a hiring freeze. Their last funding round was 2021. Glassdoor was a hazmat zone. The 61, on the other hand, had just hired a VP of RevOps, their CEO was posting twice a week about scaling go-to-market, and they'd quietly added Gong and Clay to their stack, which you could see in their job listings.
The manager insisted Marcus reach to the 94, despite what his research indicated. He closed the 61 that quarter. The 94 never returned a call.
Here's the thing about that pipeline meeting: the model wasn't wrong about the data it had. It was wrong about the data it didn't have. Marcus qualified that deal the way every good rep qualifies a deal: by reading the company. The score qualified it the way software reads a record: by counting things. And the gap between reading and counting is the entire reason your best reps quietly ignore the number you spent six weeks building a calculation for.
For the first time, we can close that gap inside HubSpot. Not with a better scoring model, but with no scoring model at all.
What Traditional Lead Scoring Measures (and Misses)
Pull up any lead scoring model and you'll find some version of the same ingredients.
Title keywords
Company size and industry
Revenue and headcount
Page views (especially pricing and demo pages)
Email opens and clicks
Form fills and content downloads.
Add it up, weight it, and you get a number.
Every one of those inputs is a proxy. A form fill is a proxy for interest. A pricing page view is a proxy for intent. A "VP" in the title is a proxy for authority. None of them is the thing you care about. And yet, we built scoring models around them.
And to be clear, that wasn't a mistake. Proxies were the best we had. You cannot build a deterministic rule that reads a founder's LinkedIn post and infers a hiring freeze. You cannot write a workflow filter for "their last funding round was four years ago and the energy feels off." So we counted email opens, because email opens are countable, and sometimes that count was correlated with buying interest.
Most teams are still optimizing the count. They tune weights, add decay, A/B test thresholds, polishing a measurement system built entirely on surface signals. The model is only as good as the signals feeding it, and the signals have always been a compromise.
That's the gap. Your scoring model measures what's easy to count. Your reps act on what's hard to read. The deals that matter live in the second category.
What Smart Properties Do
HubSpot Smart Properties are CRM fields filled by AI instead of by a human, a form fill, or an enrichment tool. You write a prompt, point it at a data source, and the Breeze Data Agent generates the value and writes it to the record.
When you create a Smart Property, you choose where the agent looks: Web research (the open web), Company website (their site specifically), Property data (other fields and associated records already in HubSpot), or Call transcripts (your recorded conversations). For the first time, a CRM field can be populated by something reading unstructured data and reasoning about it. Which is the exact move your reps take manually.
Two caveats before you get excited:
They are not real-time. A Smart Property fills when you tell it to. That can be on demand, on record creation, or on a schedule via workflow. It is not watching LinkedIn for you. Think of it as a research assistant you dispatch, not a live feed. If a company's situation changes the day after you fill the property, the property doesn't know until you tell HubSpot to refill it.
They cost credits. Every fill burns HubSpot Credits, and at scale that's a number with dollars attached. We're going to spend a whole section on credits later in this post, because it's the difference between a clever enrichment strategy and a $5,000 surprise on your invoice.
Four Smart Property Prompts That Beat Your Lead Score
These are written for the Breeze Data Agent and built to be copy-paste-ready. Before I share the actual prompts, here is one prompting rule I follow, and you should too.
Pin the output to exact allowed values. The agent will happily write you a paragraph when you wanted one word. So tell it the only acceptable outputs and forbid everything else. Pair every rating property with a companion "Reasoning" Smart Property that captures the why in plain language, so reps get a verdict they can act on and an explanation they can argue with. Set the field type to match: single-line text or a dropdown for the rating, multi-line text for the reasoning.
Now for the fun part.