Website Analytics and Experimentation

Website Analytics and Experimentation for Smarter Product Decisions

I help teams instrument meaningful metrics, interpret behavior patterns, and run focused UX experiments that improve outcomes over time without turning analytics into noise.

How I Help

  • Define event models that reflect real user intent and product goals.
  • Build practical dashboards for decision-making, not vanity metrics.
  • Prioritize experiments based on impact, confidence, and implementation cost.
  • Add privacy-aware measurement boundaries so experimentation stays usable for European teams.
  • Feed learnings back into design and roadmap decisions.

Fixed-scope starting point

Growth Signal Sprint

SaaS, marketing sites, ecommerce, and expert-led businesses with traffic but unclear product priorities.

Timeline
3-5 weeks
Budget frame
Starting from EUR12k
Event model and dashboard requirements
Friction review across high-intent journeys
Experiment backlog and AI-search visibility recommendations

Combines analytics, CRO UX, data-driven design, and AI-search visibility into one focused growth loop.

Relevant Proof

Green Filter Decision Support

Research-led sustainability product work focused on helping users understand recommendations and act on decision-support cues.

  • Connected user research to product framing and prioritization.
  • Explored in-context guidance patterns for shopping and finance decisions.
  • Translated behavior-change goals into clearer interaction patterns.
View Example

Related Work

Green Filter App

Research and product framing work that connected user insight to feature prioritization and decision behavior.

WiFi.ee

Flow optimization opportunities across acquisition, activation, and retention touchpoints.

FAQ

Can you help if we already have analytics tools installed?

Yes. I can audit your current setup, clean up noisy tracking, and align instrumentation with product decisions.

Do you focus on business metrics or UX metrics?

Both. The strongest decisions connect user behavior quality with commercial outcomes.

How often should experimentation run?

Continuously in small focused cycles. The goal is a steady evidence loop, not occasional big rewrites.

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