A 4-dimension lead scoring framework for B2B teams
Most B2B lead scoring is a single number. The score lands in HubSpot or Salesforce, sales sees an “82,” and the question of whether to call that lead has to be answered from a number with no shape.
A working lead scoring framework doesn’t collapse the answer that way. It separates four different questions into four different scores, then composes them into a tier system that tells reps not just how hot the lead is, but what kind of hot. A lead with high engagement and low fit is a curious researcher; a lead with high fit and low engagement is an account you should be marketing to, not pitching. A single number can’t represent that distinction. A framework can.
This is the framework we use at kenbun, lifted from a decade of running scoring inside HubSpot, Salesforce, and Marketo. It’s four dimensions, weighted equally to start, calibrated against your actual closed-won data over time. The shape generalizes to most B2B SaaS businesses; the specific weights are yours to tune.
Why four dimensions, not two or seven
HubSpot’s modern lead scoring tool exposes two dimensions: Engagement and Fit. That’s a real improvement over the legacy single-property HubSpot Score, but two isn’t enough.
The reason is that “Fit” mashes two different questions into one. Profile fit is about the person: their title, seniority, function, and decision-making authority. Account fit is about the company: industry, size, tech stack, growth signals, geography. These behave differently as your market shifts. If you raise prices and start selling upmarket, account fit changes long before profile fit does. If you launch a new product targeting a different buyer persona, profile fit shifts but account fit might not. Collapsing them obscures which side of the model is drifting.
The fourth dimension, Deal Context, lives mostly outside the contact-level conversation. It’s the question of whether there’s already an open sales motion attached to this lead’s account. A lead at a high-fit company with high engagement and an active opportunity at a late stage is a different play than the same lead at a company with no open opp. Most scoring frameworks ignore deal context and hand reps two leads that look identical on paper but should get very different treatment.
Why not seven dimensions? Because at some point you stop measuring distinct things and start splitting hairs that don’t change the action. Four is the smallest set we’ve found that holds up across business changes without dropping a question that matters.
Dimension 1: Engagement
What is the lead doing right now?
Engagement scores recent, lead-side activity: opens, views, downloads, replies, demo requests, in-product activity if you have it. The single most important rule here is that engagement scoring should weight event quality, not event count. A whitepaper download means hours of digestion and warrants weeks of warmth. A pricing page view means thirty seconds of curiosity and decays in days. Treating those the same is how good leads get buried under low-intent noise.
The signals that matter most:
- High-intent actions: demo requests, pricing calculator use, contact-form submission with a real message, free-trial signup
- Quality page visits: pricing, demo, comparison, integrations pages (not the homepage)
- Recency in last 7 days: weighted heavier than activity from a month ago
- Engagement with sales-driven assets: replies to sequences, meeting accepts, document opens
What to score down or ignore:
- Pure pageview counts without considering page quality
- Newsletter clicks at face value (loyalty is not intent)
- Single-page sessions of under 30 seconds
- Anonymous bot or tool traffic that hits your site but doesn’t leave a real fingerprint
A common starting weight: 0–25 points across engagement, with the bulk concentrated on high-intent actions and recency. Your weights should drift over time toward whatever your closed-won data tells you actually predicts wins.
Dimension 2: Profile Fit
Is this the right person to sell to?
Profile fit scores the human attached to the contact record: title, seniority, function, decision-making authority, geographic location, and any signals about budget or timeline that come through forms or sales notes.
The signals that matter most:
- Title and seniority: Director-level or above, in a role that maps to your buyer persona
- Function: RevOps, Marketing Ops, Demand Gen, or whatever role buys your product
- Decision authority signals: mentions of budget, evaluation timelines, vendor comparisons, signing authority
- Email domain credibility: real company email, not a free-tier address
What to score down:
- Junior titles working a build-vs-buy evaluation that won’t surface to procurement for nine months
- Functions that aren’t your buyer (engineers reading docs, marketers researching competitive landscape)
- Personal email addresses on enterprise products
The Profile Fit dimension is the one that drifts most with positioning changes. Every time you sharpen your ICP, refine your message, or move into a new buyer persona, your profile fit rules need a refresh. A scoring system that doesn’t surface “your profile fit rules haven’t been updated since you launched the new product” is a system that’s quietly decaying.
Dimension 3: Account Fit
Is this the right company to sell to?
Account Fit scores the firmographic, technographic, and growth signals attached to the lead’s company. This is the dimension that benefits most from third-party data enrichment if you have a budget for it (Clearbit, ZoomInfo, HG Insights, Apollo, etc.), but a lot of the signal you need is already in HubSpot if you’re capturing it.
The signals that matter most:
- Company size: in your target headcount or revenue band
- Industry and vertical: matches a vertical you’ve closed in before
- Tech stack: uses platforms your product integrates with or replaces (this is often the strongest predictor for B2B SaaS)
- Growth signals: recent funding, hiring spikes, product launches, leadership changes
- Geography: in a region you can serve with current sales coverage
Account Fit is also the dimension where negative scoring matters most. If a lead’s company has 5 employees and your ICP is 100–500, that’s not zero points; it’s a meaningful negative. If they’re in a country you can’t sell to without compliance review, same thing. Without negative scoring on Account Fit, your model can only ever go up, and everyone eventually scores high enough to look hot.
Dimension 4: Deal Context
Is there an open sales motion already, and what’s its shape?
Deal Context is the dimension most scoring frameworks skip, and it’s the one that changes the action most dramatically. The same lead profile means very different things at different deal stages.
The signals that matter most:
- Open opportunity attached: active opp on the contact’s account, especially mid-funnel
- Opportunity stage and age: mid-funnel and recent reads as warm; old and stalled reads as cold
- Recency of last sales touch: touched within the last 14 days vs untouched for 60
- Multi-threading: two or more contacts engaged from the same account
A lead at an account with an open Stage 3 opportunity that hasn’t been touched in three weeks is a different play than the same lead at an account with no open opp. The first is a save-the-deal motion; the second is a fresh outbound.
Deal Context is what lets you sort the queue not just by lead temperature, but by play type. Reps working renewals filter differently than reps working new business. The same scoring framework can power both queues if Deal Context is a dimension instead of an afterthought.
How the four dimensions interact
The output of the framework isn’t four scores. It’s a matrix that pairs dimensions and tells you what kind of action each combination warrants.
Think of it as a 2×2 (with two more dimensions layered in):
| High Fit | Low Fit | |
|---|---|---|
| High Engagement | Hot lead, route to AE, work this week | Researcher / competitor, deprioritize |
| Low Engagement | Marketing target, nurture, don’t pitch yet | Pass / disqualify |
That’s just the engagement × profile fit slice. Account fit and deal context modify each cell:
- Within “High Fit / High Engagement,” a lead at an account with an open mid-funnel opportunity goes to the AE who owns the deal, not to the next SDR in rotation
- Within “High Engagement / Low Fit,” a lead at a strategic-tier account (high account fit, low profile fit) goes to marketing for an account-based play, not the disqualify pile
The math underneath this is straightforward; the practical effect is that your queue stops being a single sorted list and starts being a set of plays.
Calibrating the framework against your data
The framework above starts at 25/25/25/25 weights across the four dimensions. That’s a defensible default, not the right answer for your business.
To find the right answer, run a quarterly calibration:
- Pull every closed-won deal from the last two quarters
- Score each one retrospectively across the four dimensions
- Compare the average dimension scores of wins to the average scores of your overall MQL pool
- If wins consistently score higher on Account Fit than on Engagement (typical for high-ACV enterprise sales), rebalance the dimension caps to weight Account Fit more
- If wins score evenly across dimensions, your weights are already calibrated
The metric you’re trying to optimize is conversion lift by score band: do leads in your top decile actually convert at higher rates than leads in your bottom decile? If they do, the framework is working. If they don’t, the framework is producing numbers that aren’t predictive, and the weights need tuning before you trust the score for any quota-affecting decision.
What this framework doesn’t replace
The framework is a starting point, not a finished system. The gaps below are also the most common reasons HubSpot lead scoring stops working within six months of going live; the framework gives you the structure, but you still have to instrument the parts native scoring leaves out.
- Per-event half-life decay. Different signals age at different speeds; a whitepaper download warrants weeks of warmth, a pricing page view warrants days. The framework above scores a moment in time. Decay is what keeps it useful as time passes.
- Conversion lift validation. The framework gives you a structure; it doesn’t tell you whether the structure is working. That requires regular comparison of actual conversion rates by score band.
- A built-in audit trail. The score is auditable in principle, but you have to instrument it. Sales teams won’t trust a number they can’t see the rules behind.
These are the gaps kenbun fills. The four-dimension framework above is how kenbun scores out of the box. Per-event half-life decay is built in. Calibration tooling compares scores against your actual closed-won data on demand. Every score arrives with the rules and events behind it visible on the lead.
Want to see this running on your data? Book a 30-minute call and we’ll walk through the four-dimension framework on your actual HubSpot leads, with the score breakdown per dimension and an honest read on where native scoring is leaving conversion on the table.