Who actually uses these products,
and what made them stay.

The Google Analytics Marketing Analyst

A digital marketer, marketing analyst, or growth lead who uses Google Analytics as their primary source of truth for website performance. They lived in Universal Analytics for years — they knew where every report was, how sessions worked, and what their bounce rate meant. Then GA4 happened. The interface changed, the data model changed, sessions became events, and reports they relied on disappeared or moved. They're learning GA4 because they have to, not because they wanted to. They are adapting their expertise to a tool that feels like it was rebuilt for data engineers, not marketers.

“Not a single dramatic moment — more like a Tuesday at 3pm when they realized they hadn't thought about the GA4 interface is unintuitive — reports that took one click in UA now require custom explorations in two weeks.”

The Google Analytics Marketing Manager

A marketing manager or digital marketer at a company of 10–200 people who is responsible for understanding how the website is performing and why. They are not a data person. They've been through the GA4 migration and have not recovered emotionally. They know enough to navigate the interface but not enough to build custom reports without three tabs of documentation open. They check analytics several times a week and leave most sessions with more questions than answers.

“The VP of Marketing wants to know if the new landing page is performing better than the old one.”

The FullStory Behavioral Analytics PM

A senior product manager, digital experience lead, or data-savvy UX researcher at a company of 200–5,000 people where FullStory was purchased as a platform — not a point tool. They use it to answer questions that neither analytics dashboards nor individual session recordings can answer alone: what does the full behavioral pattern look like for users who churn? Where in the enterprise checkout flow do users consistently struggle? Which UI elements are generating frustration signals at scale? They work with data. They also watch sessions. Both inform the decision.

“The shift was quiet.”