Our Manifesto
Insight without action is just noise
Everyone has dashboards. Nobody has answers. We believe two things are missing: a way to see cause and effect across your entire business, and a reason for every person to care.
Part 1
The problem everyone can feel but nobody can see
Where traditional BI stops
Your tools show you what happened. Then they leave you on your own.
Every BI platform follows the same playbook: collect data, build dashboards, let people explore. The latest generation adds AI agents and natural language, but the gap is the same. Revenue is down. Is that a lagging indicator of something fixable, or a leading indicator of something worse? No dashboard, chatbot, or AI agent can tell you. They show every metric at the same level, with no hierarchy and no direction.
The gap between “I can see the problem” and “I’m taking action” is where value dies. Teams spend months optimising a metric that turns out to be irrelevant, or worse, inversely correlated with the goal they actually care about. Traditional BI was never designed to close this gap.
Everyone has metrics. Nobody has a model. And without a model, you're optimising in the dark.
Context overload
Your dashboards are honest. The lie is what they leave out.
The marketing dashboard accurately shows campaign performance. The sales dashboard correctly reports pipeline velocity. Operations faithfully tracks support volume. Individually, they’re all telling the truth.
What none of them show is how these things cause changes in each other. To trace a chain from a marketing campaign to a spike in support calls three months later, someone has to learn three departments’ definitions, hold three mental models simultaneously, and figure out the interactions themselves. That’s not analysis. That’s an unreasonable cognitive load. And it’s why the real answers always arrive too late.
The hidden chain
A story that plays out every quarter in every growing company
This isn’t a hypothetical. It’s what happens when your metrics can show you what changed, but not what caused the change. By the time someone traces the chain, the moment to act has already passed.
Not just a smarter interface
AI chatbots give you answers. They don’t change how people think.
“How many orders did we get last week?” gets you a number. It doesn’t tell you why orders changed, what caused the change, or who needs to act. Natural language makes it faster to consume information, but it doesn’t change how people understand the business.
We still teach children mental arithmetic even though they have a calculator in their pocket. Not because the calculator is wrong, but because working through the logic builds an intuition no shortcut can replace. You learn to sense when an answer doesn’t add up.
AI agents need context to do good work. So do humans. An agent without context hallucinates. A person without context guesses. Chatbots spoon-feed answers, but nobody ever changed how they work because an assistant told them a number. People change when they can see the system, see their place in it, and see what moves when they do.
As AI becomes more agentic, this matters more, not less. The humans in the loop need the critical thinking to know when to trust the output and when to challenge it. That’s why we see natural language as one component of changing human behaviour, not the whole answer.
Dashboards
See the data
AI Chatbots
Ask about the data
Metric Trees
Change how you act
BI tools have never thought about human behaviour. Performance tools have never had causal data. We built the system that connects both.
Part 2
Stop building dashboards. Start building shared understanding.
A different mental model
Your business is a system. Start treating it like one.
Your business isn’t a set of departments each producing their own metrics. It’s a living system where every input flows through interconnected processes to create outputs. That system has logic. And it can be engineered. Not quarterly, in a planning cycle. Persistently, as the foundation everything else builds on.
A metric tree is a causal model
Your North Star metric sits at the top. Below it, every component and influence is decomposed layer by layer until you reach the metrics your teams control day to day. Each relationship has a direction and a strength: not “these two metrics moved at the same time” but “a change in this input produces a measurable effect on this output.”
The Five Whys, pre-answered
Instead of asking “why?” five times and waiting for five specialists to respond, you trace the tree downward. SQLs are down because qualification rate dropped. Qualification rate dropped because the new campaign attracted a different buyer profile. The chain of cause and effect is already there. Built once, navigable by anyone.
A model people actually internalise
The difference between a dashboard and a mental model is whether someone can close their laptop and still explain how the business works. When people navigate a metric tree, they absorb the logic of the system. They stop asking “what happened?” and start asking “what should I do?” That shift in question is where behaviour change begins.
Understand
Make it effortless to see what needs to happen
People can’t act on what they don’t understand. A metric tree replaces the cognitive load of cross-referencing dashboards with a single, navigable model of how your business works.
Reviews become investigations
When a metric moves, you trace the tree. Was it a component that shifted? An influence that changed? A dimension that drifted? The tree gives you an investigation framework, not a debate forum.
Planning becomes precise
Want 50% revenue growth? The tree shows exactly which levers need to move: not "we need more leads" but "MQLs need to increase 30%, which historically drives a 20% increase in SQLs."
Forecasting becomes logical
Build forecasts from the inputs your teams actually control (activities, spend, headcount) and let the tree relationships propagate upward. You know exactly which assumptions drive the outcome.
Understanding becomes instinct
Dashboards are opened and closed. A metric tree is internalised. When someone navigates cause and effect repeatedly, they build an intuition for how the business works. They stop waiting for reports and start anticipating problems. That is the foundation behavioural science tells us is required before anyone genuinely changes how they work.
Care
People act differently when they can see their impact
Thomas Gilbert, widely regarded as the founder of human performance technology, proved that environmental factors (information, tools, incentives) drive roughly 75% of performance outcomes. Individual capability accounts for the rest. Yet most companies invest in talent and training while starving people of the one thing that matters most: a clear line of sight from their work to the outcome.
When a support team lead can see that their resolution time feeds directly into customer retention, which feeds into net revenue retention, something shifts. It’s no longer a number on a screen they were told to watch. It’s their number, and they can trace exactly what it means for the business.
But visibility alone isn’t enough. The hard part is what happens next. Most platforms stop at “here’s your number.” They don’t push specific actions to the person who needs to take them. They don’t track whether those actions actually moved the needle. And they can’t tell you whether someone really turned the dial or just claimed they did. That last mile, from insight to verified impact, is where every other system falls short.
This is also how you get alignment that survives contact with reality. Most companies negotiate brittle plans that collapse when assumptions change. When everyone shares the same model of how the business works, a wrong assumption isn’t a crisis. It’s a shared update. The organisation adapts instead of fragmenting.
From dashboard to dial-turning
Joe says he made an impact. But did he really? Most tools can’t answer that. KPI Tree tracks every action against the metric it was meant to move. If the dial turned, you can see it. If it didn’t, you know that too. Actions get taken, tracked, and rewarded based on real outcomes, not self-reported activity.
Understanding creates clarity. Ownership creates accountability. Tracking creates proof. Together, they close the loop.
A new approach
Two disciplines that have never existed in the same product
KPI Tree was born from the conviction that data intelligence and human performance are not separate problems. They are two halves of the same challenge. OKRs try to bridge the gap but reset every quarter, creating strategy debt: goals without a persistent model. We built the system that connects understanding to ownership to action, persistently, not quarterly.
Data Intelligence
Understand what’s happening
Causal models that decompose your North Star into every lever. Root cause analysis that traces a change to its origin in seconds. The structural model of how your business actually creates value, navigable by anyone.
Human Performance
Care enough to act
RACI ownership that gives every metric a name. Line of sight that connects individual work to company outcomes. The performance system that makes every person’s impact visible: not just to leadership, but to themselves.
