Goals and A/B tests

Events

An event is something a reader did that you want to count: a signup, a download, a click on a pricing link. Send one of two ways.

Mark an element; a click on it sends the event:

<a href="/files/report.pdf" data-us-event="download" data-us-label="annual report">Download the report</a>

data-us-label is optional and tells events of one name apart. A link's destination host is sent with the event, so outbound clicks show where readers went.

Or call it from your own code:

usherstats.track('signup', { label: 'pricing page', value: 1 });

value is a number and defaults to 1. usherstats.track always exists once the snippet has loaded, and does nothing in a browser that sends Global Privacy Control.

Never put personal data in an event's name or label: no email addresses, names or account ids.

Goals

A goal is an event (or a page) you have named as a goal in the site's settings. Goals appear in the People and Behaviour views, with the number of sessions that reached each one, their sources, and the paths that led there.

A/B tests

Mark the variants in your page. Each session is assigned one variant, which is shown while the others are hidden:

<div data-exp="hero-headline" data-variants="a,b">
  <h2 data-v="a">Analytics that count people</h2>
  <h2 data-v="b" hidden>Know who reads your site</h2>
</div>

data-exp names the experiment; data-variants lists the variants; each child with data-v is one variant.
A session is assigned by hashing it with the experiment's name, so the same tab always sees the same variant and nothing is stored to remember it. The same experiment marked in several places shows the same variant in each.
A browser that sends Global Privacy Control is shown the first variant and is not counted.

Give every variant but the first the hidden attribute, as above, so a page shows one variant before the script runs; the script then shows the session's variant, hides the rest, and sets data-variant on the block.

The Experiments part of the Behaviour view shows, for each variant, the sessions that saw it, how many reached the goal you choose, the rate, and whether the difference between the two largest variants is statistically significant (a two-proportion test). Only People sessions count: bots and your own team never enter an experiment's results.

Because a session is one tab, a person who opens a second tab is a second session and may see the other variant. That dilutes a difference rather than creating one.