For Product Teams
They built a feature. We built a system.
Product analytics tools show you what users do inside your product. They cannot show you how activation connects to retention connects to revenue connects to the rest of the business. KPI Tree maps your north star metric to every product input that drives it, with named ownership, tracked actions, and verified impact across the full business model.
You track dozens of metrics. You can explain none of them.
Your analytics tool shows you what happened. It does not show you why it happened, whether it matters, or what to do about it. That gap is where product teams lose weeks.
Features ship. Nobody proves they worked.
You launch a feature, watch the adoption curve, and call it a success if usage goes up. But did it move the metric you were targeting? Did it improve retention, or just create a temporary engagement spike that faded after two weeks? No analytics tool closes this loop for you.
Metric trees that stop at product analytics
Some tools now offer metric trees. But they scope them to product events only: funnels, cohorts, feature adoption. Your north star does not live in a vacuum. It connects to marketing spend, sales pipeline, customer success, and ultimately the P&L. A tree that only covers product is a branch pretending to be a forest.
Prioritisation by politics, not evidence
Every roadmap review becomes a negotiation. Engineering wants to fix tech debt, design wants to improve onboarding, growth wants viral loops. Without a shared model of how metrics connect and which connections are strongest, the loudest or most senior voice wins. Every quarter.
Model your product as part of the whole business
Start with your north star and decompose it through activation, engagement, retention, and monetisation. Then keep going. Connect product metrics to marketing acquisition, sales conversion, and customer success retention. The tree does not stop where your analytics tool stops. It covers the full system.
- North star at the top, with product inputs as branches and business outcomes as roots
- Break each branch into feature-level inputs: onboarding completion, core action frequency, churn triggers
- Connect product metrics to marketing, sales, and revenue so stakeholders see the full picture
- Custom frameworks: AARRR, growth accounting, or something entirely your own
Prove which inputs actually drive your north star
KPI Tree runs correlation analysis across every node in your tree. Instead of guessing that improving onboarding will reduce churn, you see the statistical relationship between onboarding completion and 30-day retention. Then you prioritise accordingly, with evidence your stakeholders can verify.
- Correlation scores between any two metrics in your tree, updated automatically
- Identify the activation events most strongly linked to long-term retention
- Measure the real downstream effect of feature launches, not vanity adoption numbers
Close the loop on every initiative
Ship a feature. Track it against the metric it was meant to move. Verify whether it worked. This is the feedback loop that every product analytics tool claims but none deliver. KPI Tree tracks actions against metrics and measures impact over time, so "did it work?" has an answer.
- Log initiatives against the specific metric they target
- Compare metric performance before and after launch with statistical rigour
- Show stakeholders verified impact, not self-reported success stories
- Build an evidence base of what actually moves your north star over time
Give every metric in the model a named owner
Shared ownership means no ownership. KPI Tree assigns each metric to a specific person or squad. When activation drops, everyone knows who is watching it and who is responsible for the response. No more waiting until the next all-hands to discover a problem.
- Assign squads or individuals to any node: activation squad owns onboarding, growth squad owns referral
- Owners are notified when their metric crosses a threshold, via Slack, email, WhatsApp, or SMS
- Filter the tree by owner to prepare for squad reviews or one-on-ones
“Product analytics tools show you what users do. Metric tree tools show you what drives what. Neither closes the loop from insight to owner to verified action.”
What product analytics tools leave out
Other tools added metric trees as a feature. We built a system around them. Causal structure, named ownership, tracked actions, and verified impact. Spanning product, marketing, sales, and the P&L.
Whole-business scope, not product-only
Competitor metric trees are scoped to product events. Ours span the entire business: product inputs connect to marketing acquisition, sales conversion, customer success, and financial outcomes. Your north star exists in a business, not just an analytics tool.
Verified impact, not correlation labels
Some tools show correlation coefficients and add a disclaimer that correlation does not imply causation. KPI Tree goes further: track actions against metrics, measure outcomes over time, and build an evidence base of what actually works.
The closed loop nobody else provides
Metric moves. Owner is notified with context. Action is tracked against the metric. Impact is measured weeks later. This is not a tagline. It is the core of how KPI Tree works. No product analytics tool delivers this loop.
Common questions
- Product analytics tools are built for event-level analysis: funnels, cohorts, user flows, feature adoption. KPI Tree sits a layer above. It connects the metrics those tools produce into a causal model that spans the whole business. Your north star does not just depend on product inputs. It depends on marketing, sales, and operations too. KPI Tree models the full system. Product analytics tools model one part of it.
- Some product analytics tools have added metric trees as a visualisation feature scoped to product events. KPI Tree is built around the metric tree as its core. The differences matter: whole-business scope (not just product analytics), named ownership with notifications, actions tracked against metrics, and verified impact over time. Their tree is a view. Ours is a system that drives ownership, pushes actions, and closes the feedback loop.
- Any metric defined in your semantic layer or data warehouse. Product metrics like DAU, activation rate, and feature adoption sit alongside business metrics like revenue per user, expansion rate, CAC, and support ticket volume. The tree models whatever matters to your product and its business context.
- KPI Tree calculates Pearson and Spearman correlations between metrics in your tree using your warehouse data. These calculations run on a schedule you set, so correlation scores stay current. You see both the strength and direction of each relationship, and you can filter by time period to check whether correlations are stable or shifting.
- Yes, and that is the intended setup. KPI Tree reads from your data warehouse, where your analytics tools, CRM, and other systems land their data. Keep using your product analytics tool for event analysis, your BI tool for ad-hoc queries, and KPI Tree for the structural view of how all your metrics connect and who owns each one.
- If your metrics are defined in dbt models or views in your warehouse, that is enough. KPI Tree works with dbt metrics directly. If you have no metric definitions at all, we help you model the initial tree during onboarding. Most product teams already have the data. They just have not structured the relationships between metrics yet.
Related guides
Deep dives into the frameworks and metrics that matter most for product teams.
Metric trees for product teams
Connect every feature, experiment, and initiative to the outcomes that matter
How to run an A/B test with metric trees
Connecting experimentation to the metric tree framework
AARRR pirate metrics and metric trees
Connecting the popular growth framework to a causal model
See the system your analytics tool cannot show you
Book a 30-minute demo. Bring your north star metric and we will map it to the inputs that drive it, across product, marketing, and revenue. One tree. The full picture.
