UX & Data Analytics at De Innovatiespotter

Context

De Innovatiespotter maps innovative companies in the Netherlands using data. Its users have very different relationships to data: some live in dashboards all day, others open one a few times a month.

During my internship I work on understanding how users actually engage with the platform and its content — and on making both easier to find and easier to use.

Problem

Three connected questions: how do users actually engage with the platform and where do they struggle; how visible is the company's content in search — including AI-driven discovery; and how can clients get the insights they need without digging through raw data themselves.

My role

I combine two perspectives that are often separated: behavioural analytics (what users actually do) and user research (what they are trying to achieve). I run the analyses, build the dashboards, and translate findings into recommendations.

Data & research material

• User engagement and web analytics data describing how visitors move through and use the platform.
• Search and discoverability signals from SEO and GEO (AI-driven discoverability) work.
• Dashboards and visualisations built for clients to support data-informed decisions.

Approach

I start from behaviour: analysing engagement and web analytics data to see where users succeed, stall, or leave. Those patterns generate hypotheses about user intent, which then feed concrete improvements to the experience.

In parallel, the SEO and GEO work treats discoverability as part of UX — optimising not only for search engines but for AI assistants that increasingly mediate how people find information.

The client-facing side turns all of this into dashboards and visualisations that support data-informed decisions.

Key decisions

• Reading engagement data together with user needs rather than in isolation — behaviour shows what happens, research explains why.
• Framing recommendations in product language (what to change and why) instead of analytics language (what the data says).

Results & insights

Challenges & limitations

• Web analytics is behaviour without intent — interpreting it responsibly requires restraint and triangulation.
• Optimising for AI-driven discovery (GEO) is a moving target with few established best practices yet.

What I learned

This internship is where my interest in the intersection of UX and data became concrete. I learned how much persuasive power an analysis gains when it is paired with a user story, and how to communicate findings to stakeholders who need decisions, not statistics.