Services / Analytics
Product Analytics
& Metrics
Most products collect plenty of data and learn very little from it. The gap is rarely tooling — it's instrumentation designed around questions nobody actually asks.
We set up analytics that answer specific product questions: where users drop off, which features drive retention, and whether the last release helped or hurt.
“We have dashboards. Why can't we answer basic questions?”
Because the events were named inconsistently, key steps were never tracked, and nobody agreed what 'active user' means. Analytics work is mostly definition work — the tooling is the easy part.
Instrumentation & Tracking Plan
A documented tracking plan agreed before implementation — consistent event naming, defined properties, and a clear owner.
- Event taxonomy and naming conventions
- Identity resolution across devices
- Data validation to catch broken events early
Dashboards & Reporting
Dashboards built around decisions rather than vanity metrics, so the team looks at the same numbers and means the same thing by them.
- Activation, retention, and engagement views
- Funnel and drop-off analysis
- Cohort comparison over time
Experimentation
A/B testing infrastructure with proper statistical rigor — sample size calculated up front, and results read at the end rather than peeked at daily until they look good. We set up assignment logic, exposure logging, and guardrail metrics so a test can't quietly harm a part of the product nobody was watching, and we document each result so past experiments inform future ones instead of being forgotten.
Insight & Recommendations
We don't stop at charts. We interpret what the data shows, flag where it's ambiguous, and translate it into a prioritized list of product changes worth making. Readouts are written for product decisions rather than for their own sake — every insight ties back to a specific question raised at the start of the engagement, with a clear recommendation attached.
Explore our capabilities
Instrumentation & Tracking Plan
A documented tracking plan agreed before implementation — consistent event naming, defined properties, and a clear owner.
- Event taxonomy and naming conventions
- Identity resolution across devices
- Data validation to catch broken events early
Dashboards & Reporting
Dashboards built around decisions rather than vanity metrics, so the team looks at the same numbers and means the same thing by them.
- Activation, retention, and engagement views
- Funnel and drop-off analysis
- Cohort comparison over time
Experimentation
A/B testing infrastructure with proper statistical rigor — sample size calculated up front, and results read at the end rather than peeked at daily until they look good.
Insight & Recommendations
We don't stop at charts. We interpret what the data shows, flag where it's ambiguous, and translate it into a prioritized list of product changes worth making.
Analytics services
Everything below is in scope for a typical engagement — we scope precisely against your situation before quoting.
You own the tracking plan, dashboards, and configuration outright — nothing is locked to us.
- Tracking plan design — a documented event taxonomy tied to the questions you need answered.
- Implementation — instrumentation across web, iOS, and Android with validation.
- Analytics platform setup — Mixpanel, Amplitude, PostHog, or GA4 configured properly.
- Dashboard development — activation, retention, funnel, and cohort views.
- A/B testing infrastructure — assignment, exposure logging, and statistical analysis.
- Analytics audits — finding and fixing broken or misleading tracking in an existing setup.
- Insight reporting — regular readouts translating data into recommended product changes.
Measurement
Decisions backed by evidence
Instrumentation you can trust, dashboards people actually open, and experiments that settle arguments.
Frequently
Asked
Questions
Mixpanel, Amplitude, PostHog, GA4, and Segment for routing. We recommend based on your scale, budget, and privacy requirements.
Yes — an analytics audit is a common starting point. Most setups suffer from inconsistent naming, missing events, and identity resolution problems, all of which are fixable.
Consent management, data minimization, and anonymization where possible. We build with GDPR and CCPA obligations in mind rather than treating them as an afterthought.
A documented specification of every event, its properties, and when it fires — the reference that keeps analytics consistent as multiple people work on the product over time.
We build the infrastructure and can run experiments end to end, or hand over a working setup with guidance so your team runs them independently.
Instrumentation typically takes two to three weeks. Meaningful behavioral patterns usually need four to six weeks of collection, longer for low-traffic products.
Product analytics covers event instrumentation, dashboards, funnel analysis, and experimentation infrastructure, the tooling that turns user behavior into evidence for product decisions.
We instrument based on the specific product decisions you need evidence for, rather than tracking everything by default, which usually produces noisy dashboards nobody uses.
Yes. Experimentation infrastructure is part of our product analytics work, so product decisions can be validated with real user data rather than opinion.
Evidence over opinion
Analytics done properly settles arguments that would otherwise be won by whoever is most senior in the room. That's the real value — not the dashboards themselves, but consistently better decisions about what to build…
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