Product Analytics & Experimentation
Know what users do, then test what helps them.
A product can attract sign-ups while hiding the step where people lose interest. We connect user actions to meaningful outcomes, repair unreliable tracking and help you decide whether a proposed change deserves a wider release.
DIRECT ANSWER
Product analytics and experimentation connect behavioral events, funnel and cohort analysis, and controlled tests to decisions about an existing product.
Best for
- Product teams with unclear activation or retention
- Teams introducing reliable A/B testing and feature flags
Consider another approach
- Warehouse consolidation or financial reporting alone
Start with a product question
Pick a decision: where onboarding breaks, which feature predicts a useful first session, or whether a new checkout improves completed orders. We define the eligible population, observation window and outcome together. Reporting infrastructure belongs with Data Engineering; this engagement owns the behavior recorded inside the product and the decisions made from it.
Make an event mean the same thing everywhere
We audit the existing analytics, document event names and properties, reconcile anonymous and signed-in journeys, and instrument the agreed paths. Sensitive fields stay out of events by design. Acceptance checks cover duplicate events, missing events, consent changes and repeated sessions. The deliverable includes an event dictionary, implementation changes and a traceable test record.
Design the test before looking at the result
For a controlled experiment we specify a hypothesis, assignment unit, primary outcome, guardrail metrics and stopping approach. Feature flags support gradual rollout and rollback. We check whether traffic and expected effect make a meaningful test feasible. A team with little traffic may learn more from UX Audit sessions than from an inconclusive A/B test.
Illustrative acceptance: an invitation flow
Consider a hypothetical workspace product changing its invitation screen. The event sequence distinguishes an invitation sent from a colleague who actually joins. Test accounts and repeated clicks do not inflate activation. Both variants retain their assigned users, and a simulated tracking failure blocks analysis. This verifies measurement and controls; it does not claim a conversion increase.
Leave a repeatable analysis practice
You receive the tracking plan, funnel and cohort definitions, experiment configuration and a written release recommendation with uncertainty. We walk your team through rerunning the analysis and identifying broken instrumentation after a release. Bring access to the product, existing analytics, consent requirements and a decision owner. Ongoing experiments are agreed separately, with your accounts and implementation under your control.
Questions before you start
Only if the current tool cannot answer the agreed question within your operational and privacy constraints. We first assess whether its instrumentation and definitions can be repaired.
No. A valid experiment may show no clear effect. The deliverable is dependable measurement and a justified decision, including a recommendation to collect more evidence or stop.
It can include conversion, but also activation, repeated use, retention and feature adoption. We define the product outcome before choosing the experiment.
Your named product owner retains the decision. We provide the evidence, practical implications and rollout recommendation, and implement agreed changes within scope.
Scope your analytics and experiments
Describe the product, the decision you cannot make and the analytics you already use. Approximate traffic is helpful; personal event records are not needed in the first message.