UX Analytics
& dashboard
Building a UX analytics strategy and live Power BI dashboard to make user behaviour visible — enabling data-informed design decisions across the Myzel platform.
Making user behaviour visible
Design decisions on Myzel were being made without behavioural data — based on intuition, stakeholder feedback, and usability testing alone. As the platform scaled across modules and markets, the gap between what we assumed users were doing and what they were actually doing became a risk.
I built a UX analytics strategy from scratch — researching frameworks, evaluating tooling, and delivering a live Power BI dashboard that gives the team visibility into real feature adoption and usage patterns.
Designing blind in a complex B2B platform
Standard consumer analytics frameworks don't apply to domain-specific B2B tools where users are professional operators following structured workflows. We needed a UX analytics approach built for how Myzel is actually used — not borrowed from e-commerce or social media contexts.
Strategy, tooling, and delivery
I owned this end to end — from the initial framework research through to the live dashboard presented to management. This was a solo initiative, self-directed alongside the main design work, requiring me to learn new tools under time pressure.
- Analytics framework design
- Matomo audit and configuration
- Power BI dashboard build
- Figma target state design
- Management presentation and demo
- Matomo analytics
- Power BI
- NNG / IDF / Google frameworks
- Figma
Building the framework
Before touching any tooling, I researched UX analytics frameworks from NNG, IDF, and Google — adapting them to the specific context of a B2B safety platform with professional users following structured workflows.
Defined the right metrics for Myzel — feature adoption rates, session patterns, workflow completion, and device/browser coverage. Deliberately excluded vanity metrics that wouldn't drive design decisions.
Audited our existing Matomo configuration, corrected tracking gaps, and extracted the data needed. Learned Power BI under time pressure to build the dashboard — designing the target state in Figma first, then building toward it.
Presented the live dashboard to the management team — showing real feature adoption data across all modules for the first time. The dashboard has been in active use since.
What the dashboard tracks
The live Power BI dashboard gives the team visibility into user behaviour across the platform — filterable by tenant, workflow, and month.
Adoption tracked across all four live modules — showing which features are being used, at what rate, and how that changes over time.
Daily and peak visitor counts across the platform and per module — with month-wise filters to identify usage patterns and seasonal variation.
OS, browser, and device data — critical for a platform used both in office and on the factory floor, where device diversity is high.
All data filterable by tenant — enabling comparison between different customer types and identifying where specific user groups need design attention.
What zero-to-one requires
"You can't design for behaviour you can't see. The dashboard didn't just give us data — it changed how we talk about design decisions."
Building this from scratch — framework, tooling, and delivery — was the most technically challenging solo project I've undertaken. Learning Power BI under time pressure while maintaining design output elsewhere required careful prioritisation. The result is a dashboard the team uses in real product conversations, which is the only metric that matters.