For humans · Act

Prove the action moved the number.

Every action is tracked against the metric it was meant to move, and impact is verified by the same pipeline that calculates your actuals. Nobody marks their own homework.

PlatformOverview Metric TreesMap + Measure Business ModelsMap + Measure Root Cause AnalysisProve Metric OwnershipAct Action PlansAct

Verified ImpactAct

Strategy ExecutionAct

Prove the action moved the metric. Then the model learns.

Nobody marks their own homework. Verified against the metric

Actions are tracked against the metric they were meant to move and the impact is measured by the same pipeline as your actuals.

Learns from behaviour change. Not query counts

What worked strengthens the causal model; what did not gets discounted. Recommendations sharpen over time.

Your day starts with what needs you. The daily briefing

What moved, why, and whether yesterday's action worked, personalised to what you own.

Nobody marks their own homework

Every vendor promises insights to action; almost none can tell you whether the action worked, because the only record is what people said in the retro. Here, actions link to metrics, and when the metric moves, the action's impact is measured against it by the same pipeline that calculates your actuals. The verification is observed, not self-reported, which is what makes it worth anything.

Fix checkout flow

Verified · +£32k

Linked to Revenue · measured by the actuals pipeline

Pause underperforming ad sets

Measuring…

Most tools learn from queries. This one learns from behaviour change.

KPI Tree tracks the actions people actually took, not what they said they did, and which of them moved their metric. That verified outcome history tunes the causal model, re-weighting the drivers that prove out when someone owns them, and sharpening every future recommendation. It is also the asset no ad-hoc AI analysis can build: a stateless agent forgets each answer as the chat closes, while the tree accumulates proof of which levers move which numbers. That history also feeds the plan: initiatives get funded against drivers with a proven track record. The longer you run the loop, the smarter it gets.

Driver

Conversion rate

23%

Granger-causal · lag 3d · q < 0.05

Outcome · 58% contribution

Revenue

15%

Targets that track themselves

Metric goals auto-complete when the metric hits the target, and progress bars show how close every goal is in real time. Nobody updates a status field, because the data is the status, and a status nobody can fudge is the quiet foundation of the whole verification story.

Your day starts with what needs you

Turn on the daily briefing and every morning each person gets a personalised read: what moved, what needs attention, what is overdue, generated from the same causal model, ownership and outcome history that powers everything else. The attention strip puts the items needing action first. This is what a personalised action plan looks like in practice: not a dashboard to interpret, a list with your name on it.

Personalised Action Plans

KPI Tree app · 08:00

Good morning, Sarah. Tuesday briefing: revenue is tracking £31.5k/day behind target, down 13.1% MTD. Three things need you.

Conversion is the driver, not traffic. The £31.5k/day gap traces to checkout conversion. Sessions are flat.

Liverpool Street down 40.9% MTD. £26.9k against £45.5k in February, your single biggest drag. You are Accountable.

Checkout fix verified: +£32k. Impact confirmed against the causal baseline, 14 days on.

Open briefingView my metrics

Engagement measured in actions taken, not queries run

The engagement heatmap shows who is viewing which metrics, who is taking action, and who needs a nudge, feeding straight back into the accountability loop. Query counts flatter tools; actions taken and impact verified measure whether behaviour is actually changing. Data culture stops being a slogan and becomes something you can see and manage.

The full loop

Metric moves. The named Accountable owner is notified with the driver context attached. The action is tracked against the metric it was meant to move. The impact is verified. Then the model learns and the loop starts again. Every vendor claims this sentence; this is the mechanism. And the same loop runs a tier up: an objective drifts, the initiative funded against it is tracked, and its impact is verified against the metric it targets.

Strategy Execution

Understanding changes behaviour. Dashboards don't.

People change when they see the system, see their place in it, and see what moves when they do, and when the number has their name on it. That is the discipline this product exists for: connecting data intelligence to human behaviour change.

Common questions

What counts as verified?

An action is verified against the movement of the metric it targets, measured by the same pipeline as your actuals and shown alongside every other tracked driver. The same applies at the strategic tier: initiatives are measured against the metrics they target, and key results complete when the metric hits target.

Can impact be attributed to one action?

Impact is shown against the metric alongside every other tracked action and driver, so attribution stays honest rather than overclaimed.

How does the system learn from outcomes?

Verified outcomes feed back into the causal model, strengthening the drivers that actually move metrics when someone owns them. The learning signal is observed behaviour change, not query activity.

Where do goals live?

On the metrics themselves, with self-tracking targets and end-of-period missed-target triggers that notify the owner. And when you run objectives and key results, they live on the same tree, with outcome read from the same data. See Strategy Execution.

Metric Trees

Restart the loop.

Personalised Action Plans

The plan that starts the day.

Strategy Execution

The same verification, applied to objectives and initiatives.

Canopy

The same loop, for agents.

See it on your own metrics.

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