The Accuracy of the Unique Visitor Count

An excellent blog post on Estimating the Effects of Cookie-Deletion is timely and welcome, given the relative degree of contention around the Unique Visitor (UV) definition.

The chart above is not gospel, and you should not be running around saying that all websites have 100% human-visitor inflation. That isn’t what Angie is saying. Angie has offered up something valuable: a pretty simple model for estimating UV inflation.

What Angie is arguing here that the effect of cookie deletion on your unique visitor to human estimate will depend severely on the use of your website and the inherent habits of its audience.

Let’s assume that there’s a fanatical group of humans that visits your website. Let’s also assume that within that fanatical group, there are a set of behaviors and attributes that tend to cluster. For instance, it just might so happen that this cluster is really into your content or service: say, toddler nutrition. Now, let’s assume that the content is delivered with a unique spin that’s appealing – let’s say, constant references to failed, esoteric memes. Let’s also assume, then, that primary audience are concerned fathers about their toddler’s health and foodies. Given actuarial tables and education (perhaps derived from Quantcast), we might go so far as saying that these guys are aged 33 to 39, and have online tenures that date around 1998 – which happened to be during one of the cookie deletion hysteria eras.

As a result, a higher proportion of these guys tend to delete cookies, which might lead the poor sap who runs the site to conclude that his monthly unique visitor figure of 1000 means ‘1000 people’, when in reality, he’s has a following of 200 to 400 actual flesh and bone humans.

I’ve slipped a link in there.

Online tenure is predictive of many online behaviors – but buried within that number is the year that somebody ‘came online’. What that year says about them and their underlining habits is important, and it can be an extension of the model.

There are predictors of cookie-deletion and cookie-suppression. They’re not 100% accurate. But taking the principle of customer centricity to heart, and thinking about the likelyhood of your audience to delete, they are welcome grains of salt to the UV metric.