The right fit
SaaS products that have users but limited visibility into how those users actually use the product
E-commerce businesses making decisions based on incomplete or incorrectly implemented tracking
Companies that have analytics tools in place but no one trusts the numbers
Product teams that need to move from intuition-based decisions to evidence-based ones
Scope of work
Our process
Question Definition
Before touching any tool, we document the specific questions the analytics implementation needs to answer. Every tracking decision is made in reference to these questions.
Audit of Current State
Review of existing tracking — what fires, what it sends, where it goes, whether it is correct. Most implementations have significant gaps and errors by this stage.
Taxonomy Design
Event names, property structures, and user identification logic designed as a system rather than added incrementally. This is the foundation everything else is built on.
Implementation
Tracking code, GTM configuration, and analytics tool setup. Validated against real user sessions before sign-off.
Dashboard and Reporting Setup
The dashboards and reports that answer the questions defined in step one, configured so the people who need the data can access it without engineering involvement.
Technology stack
Outcomes
Tracking implementation you can trust — verified against real sessions, not assumed to be correct
An event taxonomy that new engineers can understand without a 30-minute explanation
Funnel analysis that shows you where users drop off and what happens immediately before they do
Dashboards that answer questions your team actually has, updated automatically
An A/B testing framework that lets you validate product changes with statistical confidence
Frequently asked
Our analytics numbers do not match between tools. Why?
Different tools measure differently — session definitions, attribution windows, bot filtering, and sampling rates all vary. We document what each tool measures and why the numbers differ, so you know which source to use for which question.
Do we need a data warehouse?
It depends on your data volume and the questions you need to answer. Event analytics tools handle most product analytics questions well. A data warehouse becomes necessary when you need to combine product data with financial, operational, or customer data for reporting.
How long does an analytics implementation take?
An audit and remediation engagement typically takes 2 to 4 weeks. A ground-up implementation with taxonomy design, tracking, and dashboards takes 4 to 8 weeks depending on product complexity.
Can you work with our existing data team?
Yes. We frequently work alongside internal data analysts or data engineers — handling the implementation and instrumentation while the internal team focuses on analysis and reporting.