Self-hosted customer journey analytics.

Knowing who someone is

A visitor browses anonymously, signs up two weeks later, then uses your product from a phone. Lyraflow ties all of that to one person, and reads their history back stitched.

If two people share a device, each event is attributed to whoever was signed in at that moment — not to whoever used it last.

Asking who did what

Build a filter tree in the browser — traits, context, lifecycle bounds, and behaviours with predicates of their own — and read it back in plain English before you trust it. Clicking a person shows what is known about them: where they are, what they arrived through, and the traits identify() has set.

A saved segment's count is the server's cache from its last evaluation, shown with the instant it was taken and never passed off as current. Each run is a real scan, so nothing re-evaluates itself just because you opened a list.

Where people stop

A funnel is a list of events in order, over a window you choose: how many people reached each step, and how many did not go on. It is drawn as a flow rather than a bar chart, so the people who leave are a band that peels away and stays visible instead of a gap you have to infer.

A step can be marked optional, and the people who skipped it still count at every step after it — without that, one detour through a help page reads as a drop-off. The window is how long someone has to finish, and it is part of the funnel rather than part of the question you ask this time.

Whether they came back

Of the people who did one thing in a period, how many came back and did another in the periods after it. Both events are yours to pick, and each takes a condition — so on a site where every navigation is one event name, “viewed the home page, then came back and registered” is a question you can actually ask.

A period that had not finished when the grid ran shows a dash, never a zero, and the screen says how many cells are waiting. Reporting an unfinished week as nought draws a collapse in the newest cohorts, in exactly the corner a reader scans for a trend.

How a number splits

An event over time, broken down by a property it carries or by a column of the event itself. Events with no value there are kept as their own series rather than dropped, so the parts still add up to the whole and a number here can be checked against the feed.

Each value gets its own panel, and every panel is drawn on one shared scale — so a series ten times another’s size looks it. Ten at most; anything smaller is summed into one labelled series and the screen says how many values that was.

The event can be narrowed before it is split, with the same conditions a funnel step and a retention grid take. Without that, a site whose every navigation is one event name can only chart “a page was viewed”; with it, the registration page, by referrer is one chart. A half-written condition blocks the run rather than being quietly dropped, because a filter that silently disappears answers a wider question than the one on screen.

One person, end to end

Every id ever bound to someone — the ids you sent and the devices they were seen on — with when they were first and last seen, what they have done, and the traits they carry. Reachable from a feed row, from a segment’s member list, and by searching when you have no id in hand at all.

It reads the same stitched identity every other screen reads, so a person who looks like two people here looks like two people in a funnel as well. That is the point of showing it: identity problems are much easier to see on one person than in an aggregate.