Web-Pop
Explore segments, purchase journeys and unusual events.
This public demo uses a fixed sample dataset. Explore who buys, where visitors drop off and which events look unusual. Queries run when requested; the dataset is not a live feed from your website.
Demo
Figures come from the fixed demo dataset. Segments can overlap. The funnel counts users completing the steps within a session; percentages are relative to the first step.
Buyers, high-intent users, engaged users, bounce traffic, mobile, organic — ready in milliseconds, not after a nightly job.
Page view → add to cart → purchase, in order, per session. Where your visitors drop off, and what converts.
Purchases that are unusually high for their traffic source. Statistical signals for review.
Unusual page-view timing can flag possible automation. These signals do not establish a visitor’s identity.
The most common event sequences — and the rarest, which are usually the interesting ones.
When your traffic and revenue actually happen, hour by hour.
The technology behind Web-Pop is patent pending. The demo exposes results such as counts, segments and statistical outliers.
Read-only, JSON in and out. No write operations, no configuration — the demo is a fixed snapshot of one dataset.
| Method | Endpoint | Returns |
|---|---|---|
| GET | /summary | Events, users, sessions, purchases, buyers, bounce rate, average order value |
| GET | /segments | Named audience segments with user counts |
| GET | /funnel | Purchase journey steps: users, conversion %, drop-off % |
| GET | /anomalies?z=3&limit=20 | Revenue outliers with opaque ids and confidence scores |
| GET | /bots?z=3&limit=20 | Automated visitors with opaque ids and timing stats |
| GET | /patterns?n=3&k=10 | Most frequent and rarest event sequences |
| GET | /peak-hours | Event volume by hour of day |
| GET | /health | Service status and uptime |
Feedback
Web-Pop