Product analytics that answer the real question.
Custom event tracking, funnel and retention analysis, and cohort tooling — built on a clean taxonomy and surfaced through Amplitude, PostHog, or your own warehouse. So when a PM asks how feature X is performing, the answer is a query, not a project.
GA4 tells you traffic. It won't tell you if the feature worked.
That gap is where most product decisions get made on vibes. You can see sessions and bounce rates, but not whether the people who used the new flow actually stuck around, or which cohort retains, or whether the A/B test moved anything real. The fix isn't another marketing dashboard; it's analytics built around your product's events — a clean taxonomy, funnels and cohorts that hold up, and tooling a PM can use without filing a ticket. The result is a team that can finally answer "did it work?" with data instead of a hunch.
You can see traffic, but not the product.
Each symptom has the same root — analytics built for marketing, not for product.
- 01
GA4 can't tell you if a feature is being used
Event tracking built around your product, not page views.
- 02
PMs can't answer 'how is feature X performing?'
Self-serve funnels and adoption views they run themselves.
- 03
Your event data is a tangled mess of inconsistent names
A documented taxonomy and tracking plan, enforced going forward.
- 04
Retention is a single number nobody can break down
Cohort tooling that slices by signup date, plan, or behaviour.
- 05
A/B results are argued over, not measured
An experiment framework with proper stats and guardrails.
The analytics behind a decision you can defend.
From raw events to a question a PM can answer — the work that makes product analytics trustworthy.
Event taxonomy & tracking plan
A documented contract for what you capture and what it means.
Funnel analysis
Where users drop off between steps, broken down by segment.
Cohort retention
Whether the people you acquire actually come back, over time.
Experiment framework
A/B tests with consistent assignment and honest statistics.
Self-serve querying
PMs answering their own questions without a ticket queue.
Clean instrumentation
Events that fire once, named consistently, carrying the right properties.
A raw event becomes a question you can answer.
- Events01
- Tracking plan02
- Model03
- Funnels04
- Cohorts05
- Experiments06
Every funnel, cohort, and experiment traces back to a defined event on the left — so when a PM asks where a number comes from, the answer is the tracking plan, not a guess. That's the difference between data people trust and data they argue about.
Name it once, name it right.
We design the event taxonomy around the questions you actually want answered, then write it down as a tracking plan — the contract between engineering and analytics. No more 'signup' versus 'sign_up' versus 'Signed Up' all meaning slightly different things. Instrumentation you set up deliberately and keep clean as the product grows.
- Events designed around real product questions
- A documented, enforceable tracking plan
- Consistent naming and required properties
- The contract that keeps data clean over time
See where they stall, and whether they stay.
Funnels show exactly where users drop off between steps, broken down by segment so you can see who's stalling and where. Retention shows whether the people you acquire actually come back — day 1, day 7, day 30 — and how that trend is moving. Built on clean events, so the numbers survive scrutiny.
- Step-by-step funnel drop-off by segment
- Day 1 / 7 / 30 retention curves
- Segment comparisons that hold up
- No double-counting, no unreproducible numbers
Compare groups, not averages.
A single retention number hides the story. Cohort tooling lets your PMs compare March signups against April, or users who hit a feature against those who didn't, and watch each group over time. That's how you tell whether a change actually moved the needle or whether you're just looking at seasonality.
- Cohorts by signup date, plan, or channel
- Behavioural cohorts on any event
- Real differences instead of blended averages
- Change attribution you can defend
Ship the change, then actually learn from it.
An experiment framework assigns users to variants consistently, ties assignment to your event stream, and computes the metrics that matter with proper statistics — so a difference is real or it isn't. Guardrails handle the common mistakes: peeking early, ignoring sample size, measuring the wrong thing.
- Consistent variant assignment
- Metrics tied to your event stream
- Honest statistics, not eyeballing
- Guardrails against the classic A/B traps
Built to be trusted — and used by PMs.
Map the questions
We start from what your PMs actually want to know, then work back to the events that would answer it.
Design the taxonomy
We design the event taxonomy and tracking plan before instrumenting, so naming and properties stay consistent.
Instrument & model
Clean event capture, wired into Amplitude, PostHog, or your warehouse, modelled for the analyses you need.
Build funnels, cohorts, experiments
Funnel and retention analyses, cohort tooling, and an A/B framework with proper statistics.
Hand over self-serve
Tooling, docs, and a walkthrough so PMs answer their own questions without a ticket.
Page views stopped answering the question.
GA4 isn't telling you what you need
It's built for marketing — sessions and campaigns — not for whether a feature actually changed behaviour.
PMs can't answer 'how is feature X performing?'
Every product question turns into a request to the data team, and the answer arrives a week too late.
Your event data is a mess
Inconsistent names, duplicate events, missing properties — tracking nobody trusts and nobody can fix alone.
Proven analytics tools, used with discipline.
The things product teams ask first.
Answer the question that actually matters.
Tell us which product questions your team can't answer today and where your event data falls short. We'll build the analytics — a clean taxonomy, funnels and cohorts, experiments, and self-serve tooling — so "did the feature work?" has an answer your PMs can pull themselves.