Propensity Assessment
Propensity Assessment continuously weighs each account's live signals, including how its engagement is trending, against fit and what has actually converted before, then returns a ranked priority with the evidence behind it and the next action to take, so reps spend today on the accounts that deserve it.
Sales
What decides which account gets attention today?
What changes across the three columns isn't which signals get counted, engagement among them, it's whether anyone reconciles what they mean and says what to do about it, or hands back a faster number.
01 | The Current Way
02 | AI Added On
03 | AI-Native
Daily prioritisation
A stale list, re-sorted by feel
The rep opens a lead-score list built weeks ago, blind to how engagement or anything else has moved since, and reorders it by instinct before the first call.
Quicker, still one flat number
AI blends signals, engagement included, into one number faster, but doesn't say why an account sits where it does or what's actually driving it.
Ranked, with the reason why
The rep opens a list built from what's changed today, fit, engagement and goals weighed together, each account carrying the evidence behind its place on it.
A signal fires mid-day
The change goes unnoticed
Nobody's watching between review cycles, so a new signal, a pricing-page visit, a content download, a shift in reply rate, sits unseen until the next manual pass.
A number ticks, unexplained
The score shifts when the model next re-runs, but nothing tells the rep which signal moved it, why, or whether to act now.
Moved, and why, in the moment
The account's prospect state updates the moment the signal fires, carrying the evidence, engagement or otherwise, and the recommended next action with it.
Engagement cools while other signals keep firing
Cooling missed under a rising total
Content clicks and page views keep the activity total climbing, so nobody notices that meeting engagement and reply rates across the account have quietly cooled.
Rigid rules, no context
A bolted-on score runs on rigid if-this-then-that rules, with no sense of what each signal means in the context of this account, so a cooling account can read the same as a warming one.
Trend named, weighed against the rest
The account's engagement state and trend are read on their own terms, strengthening, weakening or changing, then weighed against fit and goals so a cooling account doesn't outrank one that's actually moving.
A manager checks the list
No record to check
A manager can see whether the rep worked the list, not why it was ordered that way or what the engagement and signal evidence behind it looked like.
Nothing to interrogate
The number arrives without the reasoning behind it, so a manager still has to reconstruct what the evidence meant before backing or overriding the call.
Open to inspect and override
Managers and RevOps see the weighting and evidence, engagement included, behind every ranked account, and can flag an exception or override the call.
When the account converts, or doesn't
The same rules, missed again
An account the list ranked low, its engagement flat on paper, converts anyway, and the scoring rules stay exactly as they were.
A score that never learns
Combined once and left running, it doesn't change how much weight engagement or any other signal carries next time, because this account was missed.
The weighting updates for everyone
When a rising engagement trend turns out to actually predict a meeting, or matters less than assumed, every rep's ranking reflects that from the next run on.
It reads each account's current state, including how its engagement is trending, against your signals, ICP, persona and goals, then ranks priority with why-now evidence and a recommended action inside the rep's briefing, and every call traces back to the evidence behind it.
The result is more pipeline per rep and per action, more productive follow-up and campaign spend, and earlier intervention, with less time lost to manual triage, research and reassembling the same evidence across systems.
One system that understands, decides, acts and learns.
Every GTM signal flows through an AI-native operating layer into a system that runs on the surfaces your team already uses.
Explore the GTM System →Account & Contact State
Buying Signals
Territory Coverage
Propensity
Lead & Account Routing
Sequence Selection
Meeting Prep
CRM Updates
Alerts & Escalation
Upgrade Targeting
Upgrade Messaging
Upgrade Sequences
The GTM teams that learn fastest will win.
Build yours a system that learns. An advantage competitors cannot buy back: years of success and failure, codified.
Frequently Asked Questions
AI-native GTM Systems didn't exist two years ago - here are the questions everyone wants answered.
Talk to Us→Propensity Assessment replaces a score built once and left alone with a priority that is re-checked whenever an account's evidence, including how its engagement is trending, changes. A lead score measures fit against a profile; propensity weighs live signals, engagement and goals against what has actually converted before, so the accounts at the top of the list are worth attention today.
Propensity Assessment shows its confidence and the evidence behind every ranked account, engagement included. When a high activity count masks a cooling trend, or two signals disagree, the reasoning behind that specific call stays visible, and managers or RevOps can inspect the weighting, flag an exception, or override it, so a wrong read gets caught rather than acted on blind.
Propensity Assessment re-evaluates every account continuously. A new signal, a shift in an account's engagement, or a change in goals moves its priority the moment it happens, and the rep sees the evidence behind the move, engagement included, rather than a number from the last batch run.
Propensity Assessment tracks what happens after an account is prioritised, then feeds that back into the logic that predicted it. When a rising engagement trend, or any other signal, turns out to reliably lead to a meeting or pipeline, or matters less than assumed, that becomes a proposed change to how much weight it carries next time. RevOps configures whether that weighting change applies automatically or waits for approval, and every rep's prioritisation reflects it either way.
ICP fit and propensity answer different questions. Fit asks whether an account matches the profile of companies that buy. Propensity asks whether this account is worth attention right now, weighing that fit against live signals, including engagement, and what has actually converted before. A good-fit account with strong engagement can still sit low until the rest of the evidence says it is actually in motion.
Engagement is evidence of actual attention, opens, clicks, meetings, content activity and event attendance, tracked across channels for an account. Intent is a broader signal, often drawn from outside the relationship entirely, such as third-party research activity. Propensity Assessment reads an account's own engagement trend as one input alongside fit, signals and goals, rather than treating either as a standalone score.