How JP Analytics turns news audience behavior into market intelligence

JP Analytics started from a simple commercial question: if a company sends traffic into the Jovem Pan ecosystem, what can it learn after the click? In traditional media, the answer often stops at access volume. This product was designed to go further and show what kind of content that company's audience consumes inside JP.

The system uses a custom script, jpevents.js, installed on client sites and on Jovem Pan domains. When a user leaves the client's environment and enters an article, column, or page on JP, the journey stays stitched through identifiers such as cid, eid, sid, and aid. The secure /r redirect controls the handoff to allowed domains and preserves attribution without depending on a fragile chain of loose query parameters.

That foundation supports two uses at once. For Jovem Pan, the platform shows how visitors move through the ecosystem, which topics attract attention, which columns retain audience, and how long people spend consuming content. For client companies, the product becomes a behavioral research layer: they can send traffic to JP and understand whether their base cares more about economy, politics, agriculture, technology, sports, or other editorial segments.

The platform was built with Next.js, Prisma, and Postgres on Neon. The admin dashboard consolidates events, sessions, pages, average time, average scroll, topics, columns, and geographic distribution. It also supports email open pixels and API keys, which lets JP connect its intelligence layer to campaigns and external customer bases.

The most interesting piece is the AI layer. The system can turn content events into interest profiles, surfacing behavior patterns, editorial signals, and opportunities for new products. That moves analytics out of passive reporting. JP Analytics becomes an intelligence product: it helps Jovem Pan understand its audience better and helps companies learn what their own base wants to consume inside a trusted news environment.