There’s a moment every RevOps and marketing ops practitioner knows: the dashboard says 47 accounts are “surging.” But which ones actually matter?
The tool says “high intent.” But high intent for what? Based on what? Compared to what baseline?
When signals pile up without context, they stop being signals and start being noise. The goal here isn’t to convince you that intent data is useful — you already know that. It’s to draw a clear line between collecting signals vs. actually understanding what they mean.
The single-score problem
Lead scores and intent scores that compress everything into one number feel useful. They’re not.
A score of 87 out of 100 tells you almost nothing: Why 87? What’s driving it? Is it fit, intent, engagement, or some weighted combination that nobody on the team actually understands?
Think about it like going to the doctor. A physician who hands you a single “health score” and sends you home isn’t being efficient — they’re being unhelpful. A full panel of results tells you what to watch, what to act on now, and what can wait. Account scoring should work the same way.
What real analysis looks like
Moving from “probably in-market” to statistically modeled probability means combining multiple dimensions — not picking one and hoping for the best.
- Fit: Does this account match your ICP? That includes firmographic, technographic, and psychographic signals.
- Intent: What are they researching, at the keyword level — and how recently?
- Buying stage: Where are they in the journey? Awareness, Consideration, Decision, Purchase?
- Engagement: Have they interacted with your ads, your website, your emails?
These dimensions together produce a picture of an account. Any one of them alone produces a hunch.
Malbek put this into practice with a five-tier signal classification framework — ranging from False Flags all the way through to First-Party Fingerprints — that helped their team cut through alert fatigue and prioritize with real precision. Accounts that reached the Purchase stage proved 29x more likely to create opportunities within three months.
That’s the difference between a team working a list and a team working the right list.
Why the intelligence layer is what most platforms skip
Collecting signals is a solved problem. Dozens of tools do it.
Converting those signals into actionable intelligence is where most platforms stop short. That conversion requires models that learn, adapt, and account for historical performance patterns — specifically, your historical performance patterns, for your ICP.
The best models improve over time as they see which signals actually preceded closed-won deals in your market. They’re not applying generic rules. They’re applying your data back to your pipeline.
6sense takes this further with multi-dimensional predictive models that combine fit, intent, and engagement signals, along with predictions built on historical performance. And because the inputs are cleansed, matched, standardized, and taxonomized by default, you’re not building intelligence on top of dirty data.
What this changes for practitioners
When prioritization is model-driven rather than gut-driven, the downstream effects are real and measurable.
BDRs spend time on accounts that convert — not accounts that look interesting. And when scoring is explainable (“this account is trending because they searched these three keywords and two new stakeholders joined the buying committee”), reps know what to say when they reach out.
Consider what PTC experienced after making this shift: 1,200 net-new high-intent accounts surfaced that didn’t exist anywhere in Salesforce. Those weren’t accounts their team had overlooked — they were accounts they had no way of knowing about. The result was $18M in net-new pipeline in four months.
Ivanti saw a 154% increase in win rate year-over-year, along with a 93% BDR adoption rate. That adoption number matters more than it might seem. Tools don’t get 93% adoption unless reps actually trust what the tool is telling them.
Moving account scores from ‘probably’ to probability
The shift from “probably in-market” to statistically modeled probability isn’t just semantic. It’s the difference between a team that acts with confidence and one that hedges every decision, hoping they guessed right.
Signals are everywhere. Intelligence is the filter.
If your current scoring model can’t tell your BDRs why an account is worth calling, it may be generating activity — but it’s not generating clarity. And in a market where every rep’s time is a finite resource, clarity is the whole game.
Ready to see 6sense in action?
