A Working Model of How Your Business Actually Creates Value.

Goals worth setting depend on metrics worth measuring. We build the data model first, then the goals. Without it, OKRs are slogans with numbers attached.

Why Most Metrics Work Fails

Most organisations have dashboards, not a data model. Numbers are reported. Trends are reviewed. Nobody can explain how one metric causes another. When something moves, the conversation becomes a guess. When something needs investment, the choice becomes a preference. The numbers describe the business without explaining it.

Dashboards instead of models
Metrics without causal logic
Lagging signals everywhere
Vanity metrics get attention
Decisions become preferences

What a Real Metrics Model is

A metrics model is a causal argument. It works backwards from the outcomes you care about through the customer, product and operational layers that produce them, with metrics attached as leading indicators or lagging confirmations. It tells you where to look when numbers move, and which lever to pull when you need them to.

Causal logic, not a dashboard
Outcomes connected to drivers
Leading and lagging indicators
Navigable top to front line
Built to be acted on

Strategic Metrics & Health Metrics

Two streams live in the same model. Strategic metrics tell you whether the bets are landing. Health metrics tell you the business is operating well regardless of strategy. The strategic stream tells you whether you are winning. The health stream tells you whether you can keep playing. Both matter.

Strategic metrics confirm bets
Health metrics protect the base
Both live in one model
Neither starves the other
Honest picture of the business

How We Build the Model

We start with the strategic argument. What outcomes matter, what drives them, what assumptions are being made. We work down through customer, product and operational layers, naming the metrics that confirm or challenge the logic. Then we test the model against real data and rebuild where it does not hold.

Start from the strategic argument
Map drivers, not dashboards
Name leading indicators explicitly
Test the model against data
Rebuild where logic fails

Why This Comes Before OKRs

OKRs anchored in a real metrics model are operational. OKRs set without that grounding are aspirational. Key results become measurable because the metric exists, the data is trusted, and the causal logic connects the result to the outcome. Skip this layer and the OKRs that follow will not survive contact with the business.

Goals depend on metrics
Metrics depend on a model
The model anchors the OKRs
Without it, OKRs drift
Architecture before ambition

Why the Experience Matters

A metrics model is only useful if the people using it trust it and can navigate it. We build the model with the team that will operate it, not for them. The result is shared language, shared logic and the kind of operational confidence that turns review meetings from theatre into honest conversation.

The team builds the model
Shared language across functions
Trust replaces interpretation
Reviews become honest
Capability stays in the room

What You Walk Away With

A documented metrics model covering the strategic and health streams of the business. Named owners. Defined data sources. A working diagram navigable from top of the business to the front line. And the capability inside the team to evolve the model as the business changes.

A documented metrics model
Named owners and sources
Strategic and health streams
Navigable from top to front line
Capability to evolve it

Who This Is For

Metrics model development at ZOKRI is built for organisations where the strategic and operational complexity has outgrown a dashboard. Scaling tech, mid-market and enterprise, PE portfolio companies under value-creation plans, and organisations preparing to roll out or reset OKRs.

Scaling tech, 100 to 1,000+
Mid-market and enterprise
PE portfolio under value plans
Pre-OKR rollout or reset
Multi-product or multi-geography

What Happens Next?

A 45-minute diagnostic call. We ask how your metrics work today, where the numbers are trusted and where they are not, and what decisions are getting stuck because the logic underneath is unclear. You leave with a clearer view of where the model is missing.