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Funnel Analysis
Funnel Analysis reads conversion and leakage across the full lifecycle, from audience through engagement, meetings, opportunities, wins, customers, and renewals, against the funnel, ICP, persona, messaging, campaign, and process logic that was actually running. It returns the likely cause, its confidence, and the specific change worth testing.
Marketing
Prospecting
Where is the funnel actually breaking, and why?
You can see the funnel move in any of the three columns. The real test is whether the read stops at the number that changed or actually reaches the cohort, the version and the layer of decision that caused it.
01 | The Current Way
02 | AI Added On
03 | AI-Native
The weekly or monthly review
Dashboards, then debate
Someone assembles funnel numbers from the CRM, marketing automation, BI and spreadsheets, and the room argues attribution before the meeting ends.
Faster chart, same debate
It summarises the same dashboards quicker and flags what moved, but the room still has to argue out why, same as before.
Cause, named before the debate
It checks state transitions, cohorts, and which version of the funnel, ICP, persona, and messaging logic was actually live, and names the likely cause before the review even starts.
When a stage's conversion moves materially
Nobody notices till the review
A stage's conversion can drift for weeks before anyone outside the next scheduled review even sees that it moved.
An alert with no reason
It flags the drop the day it happens, but the anomaly alert still can't say whether ICP, message, routing or state quality caused it.
Traced to the moved layer
It checks which cohort, and which version of that logic, was running against it, and names the layer that actually moved, before the next scheduled review even starts.
Quarterly planning
Whoever argues loudest
Planning splits budget and attention between channels and stages based on whoever's read of the data is most convincing that quarter.
A dashboard, still unranked
A cleaner summary makes the debate faster, but it still doesn't rank which constraint is costing the most before the room decides.
Ranked by commercial cost
It ranks the stage-level constraints by their cost to conversion, so planning starts from what's actually limiting growth.
Proposing and testing a change
Change first, check never
A team changes a message, a stage gate, or a routing rule on instinct, then rarely goes back to test whether it actually worked.
Same suggestion, no test
A generic recommendation to "optimise messaging" arrives, but nothing in it proposes a specific test or says how to check whether it held.
A tested change, approved first
It proposes the specific piece of logic or state to test, a person approves it, and it runs as a cohort or canary before it's adopted everywhere.
After the intervention runs
The fix is never revisited
Once a change ships, nobody schedules the follow-up that would confirm it actually fixed the conversion problem it targeted.
Same weighting, next quarter
A bolted-on summary reports the new numbers next quarter without connecting them back to the specific change meant to explain them.
The test becomes the lesson
The outcome feeds back into the logic itself, sharpening what the organisation believes actually drives conversion, with RevOps setting the update as automatic or reviewed.
It reads Account State against the company's funnel, ICP, persona, messaging, campaign, and sales-process logic, and returns the affected cohort, likely cause, confidence, and a proposed upgrade, delivered to leaders as the decision, with Ops able to trace the reasoning behind it.
It speeds up allocation decisions and cuts the analyst and meeting time spent reconciling dashboards, and lifts conversion by fixing the actual systemic constraint instead of adding more campaign activity on top of it.
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 →Personas
Segments
Funnel Definitions
Territory Map
Channel Selection
Engagement Threshold
Daily Brief
Sales Alerts
Content Generation
Upgrade Messaging
Upgrade Value Map
Upgrade Attribution
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→Funnel Analysis names its confidence alongside every cause it proposes, so a weak or coincidental read shows up as low confidence rather than a firm verdict. It checks which cohort, and which version of the funnel and messaging logic, was actually live when a stage moved before naming that as the driver, and a person approves the causal read before any material system change follows.
Funnel Analysis names the layer where the leak actually sits, whether that's ICP, messaging, routing, qualification, or a customer success process, rather than stopping at the observation that conversion fell. Because it reads account, prospect, deal and customer state together, the diagnosis follows the funnel across Marketing, Prospecting, Sales, and Customer Success, and a person in the owning team still approves and tests the specific change before it applies.
Funnel Analysis reads the funnel's condition as it stands now. It checks Account State plus the version of funnel, ICP, persona, and messaging logic that was actually running against each cohort, so a persona's conversion drop shows up against the campaign or process version that produced it.
Funnel Analysis's diagnosis does improve, because every tested change becomes another observed experiment that feeds back into how it reads cause next time. When a fix that looked right doesn't move conversion, or a change nobody expected to matter turns out to explain most of it, that evidence sharpens the organisation's funnel and attribution logic, and RevOps sets that upgrade as automatic or held for review before it applies to the next diagnosis.
Finding funnel leakage means locating exactly which stage and which cohort is losing more than it should. A dip in the overall conversion number doesn't say where the leak actually is: Funnel Analysis compares stage-to-stage movement by segment against the state and logic versions that were live at the time, and returns the affected cohort, the likely cause, and a confidence level rather than a single blended funnel number.
Diagnosing why conversion changed means checking which cohort moved and which version of the process or messaging logic was actually running against it. Funnel Analysis compares the affected segment against the logic that was live during that window, states its confidence in the likely cause, and proposes the specific change to test before anyone acts on it.