Using analytics to understand visitor retention
Our cohort analysis graphs are fairly simple, the y-axis shows what percentage
of users come back and respond to an event while the x-axis is the data for a
cohort. So for example with the “Bad retention” graph, all users that came to
the site on the 31st, only ~7.3% showed up the next day, and ~5.7% showed up 2
days later, so on and so forth.
This means users come to the site and rarely ever come back possibly telling
you, it’s probably not that interesting or very valuable to use after the
first day of use. Super viral applications generally show trends like this
because they have great flow and viral mechanisms but they do not last.
While it could be better by getting a higher percentage as the highest is
between 25-35%, this site shows promise as the cohorts (each line) fish tails
upwards connoting some stickiness to the site or reason to come back even as
much as 8 days later. Providing more value to the product could help the
cohorts in the 2-5 day positions.
While it’s not the epitome of good retention, the graph says a lot about what
is going on. The site appears to be sticky for 3-4 days and brings nearly half
its visiting users back sometimes. Additionally, some of the cohorts are
actually flat and retain the same percentage the following days which also
fairly impressive as most cohorts would show a decline eventually.
The best would be if it fish tailed like the previous graph we talked about
Mixpanel offers retention analysis like this for any
features of your site not just if a user comes back. For example, we know what
% of people come back to look at our demos and we think this is one of the
most valuable tools we can offer for business insight for startups and