The first two questions, answered side by side: who was here, and how long they actually stayed. Four independent cards, each fetching its own data so the fast ones appear first.
| Card | What it asks |
|---|---|
| 01 Who was here | Five mutually exclusive population counts, plus a distinct-visitor estimate and totals for the human half |
| 02 How long they actually stayed | The four clocks as an average and a median over human sessions that have beacon data, and the log-derived figure over all human sessions |
| 03 Sessions over time | One range facet by hour with the five populations nested inside each bucket |
| 04 Top pages | The paths, re-run when you switch between humans, everything and bots |
It is the only place you will see the industry number and the honest number next to each other on your own traffic. On most sites the gap is large enough to be worth a second look.
Every count on this page splits into five mutually exclusive buckets, and they are the same five everywhere in the panel:
Figure 1 — the population ramp. Declared and AI crawlers are bots and are counted as bots; they are kept visually apart from evasive traffic because conflating the two is what makes other tools useless here.
Each chart and each stat block carries a visible caption naming what it counts — and it is a paragraph rather than a tooltip, so it survives being screenshotted. An unlabelled number is treated as a lie.
- Absent stays absent. A metric with no data renders as a dash, never as zero. A session with no beacon shows a log span and three dashes, because nothing measured the other three.
- The timing card covers a smaller population than the count card, deliberately — only sessions with beacon data can have the three measured clocks — and the caption says so.
- The distinct-visitor figure is approximate above about a hundred, and its caption says that too.
- Every timestamp renders as
mm/dd/yyyy hh:mm:ssin the timezone you set under Display; Solr stores everything in UTC.
Top pages has a humans/everything/bots toggle for a reason: a top-pages table that silently includes crawler traffic is the most common way an analytics dashboard misleads somebody about what their audience reads.
Clicking any value anywhere scopes the whole dashboard — see filters and facets.