Analytics and data
Reliable data for better planning and analysis: area managers can see how information is actually used in the field and make better-informed decisions on that basis.
What we measure
Every geo-point, route and QR entry point can be tracked. We measure which routes people view most often, which points they use, where they enter the system (a QR code in the field, the map or search), and where they go next. Location queries, GPX track downloads and entries through the "what’s nearby" feature demonstrate that the system is not simply a digital directory, but a tool for orientation and place-based decision-making.
Our own analytics infrastructure
Analytics run on our own infrastructure (Matomo), hosted on servers in Slovenia rather than by global providers. Usage data remain under the operator’s control and are processed in accordance with data protection legislation.
Transparent figures: people and bots measured separately
A large share of modern web traffic is generated by automated systems: search engines, AI assistants and bots that read content to produce their answers. Most analytics tools combine both types of traffic into a single figure. We keep them separate, reporting human visits and automated visits as distinct metrics. Figures on the Geo Index KAM page come directly from these analytics and are refreshed daily, with the latest refresh date clearly displayed.
The growth in automated visits is a story in itself: content read by AI assistants becomes a source for the answers they provide to users. Standardised point descriptions with clearly assigned data owners are ready for this development.
Reports for informed decisions
We prepare regular reports for municipalities and destinations, including usage by area, point and entry source, comparisons between periods, and recommendations on which content to strengthen. The data can be exported and independently verified.
Live figures
An example of data-driven decision-making
By measuring human and automated visits separately, we found that AI assistants are increasingly reading the platform’s content and using it in their answers. We therefore standardised geo-point descriptions (source, data owner and location) and introduced machine-readable interfaces, making customer content visible through emerging search channels as well as traditional search engines.