Web analytics tools heavily overstate the number of unique visitors your website has. The degree of error ranges anywhere between a few dozen percent to a few hundred percent. Here we discuss the variety of causes for this erroneous unique visitor count.
The use of ad-blockers, particularly by younger users, significantly increased in recent years. In many European countries, ad-block usage is above 20%. Many of these ad-blockers have the ability to block web analytics trackers. Some ad-blockers block analytics tracking by default, others make it very easy to enable it.
The margin of error this creates varies according to your user base. One experiment established that Google Analytics script was blocked in 11% of cases.
Nearly one out of five internet users use in-private browsing. With regards to analytics tracking, this means only temporary session cookies and no permanent cookies to identify unique visitors will be placed. When visitors come back to your site they will be considered a new visitor thus inflating your unique visitor count. However, their session information and hits will be registered correctly.
It is hard to find statistics on cookie deletion behaviour. Yet when I ask in the many analytics courses that I teach whether participants delete cookies I find that without exception several of them do at varying frequencies (anywhere between 6 weeks to several months). If I were to stick a number on it I’d probably pick 20%. Visitors who delete cookies obviously heavily distort your unique visitor count since they will be counted as a new visitor every time they come back to your site after having deleted their cookies.
Multi-device & browser usage
Users who use more than one device to visit your site will have a tracking cookie on each device and will therefore be counted as multiple unique visitors. The question is, how many of your visitors do use multiple devices. The only way to prove this is by making visitors log in to your site every time they visit. You will then be able to record a unique user id instead of a cookie id.
In the example on the right taken from a site where users are always logged in, we see that about 30% uses more than one device to visit the site. If no unique user id would have been captured this would mean the unique visitor count would be inflated by 30%. On your site, the same thing happens although maybe to a lesser degree (the example was taken from a site to which visitors return very frequently).
When users use different browsers on the same device to visit your site the same principle applies. They will have a cookie in each browser will leads to inflation of the unique visitor count.
Clearly, inflation is far more likely to happen than under-reporting. Yet, it is virtually impossible to quote a generic inflation factor.
In the example on the right from a site where users are always logged in, we confronted the number of unique user id’s with the number of unique cookies in Google Analytics. The result: 93.681 unique user id’s matched to 312.577 users or unique cookies.
The real number of unique visitors was overstated by more than a factor 3! With some visitors equaling as many as dozens to hundreds of unique cookies!
So…be warned…unique visitor does not equal unique human being…far from!