What if visitor counts are inflated

There has been some research recently suggesting that monthly unique visitor counts for a website are inflated by 2 to 4 times.  This means that if your web analytics tool is reporting 1.6m visitors for the month, the actual number of people who visited your website is between 400k and 800k.  Details of this research can be found in a press release from Scout Analytics with similar numbers found for any website using Google/DoubleClick Ad Planner.

Ignoring the methodologies used to calculate this and whether the findings are correct or not, the question I wanted to discuss was – if visitor counts are inflated, does it matter??

First of all, the absolute numbers.  Your web analytics tool says you had 1.5m visitors.  Maybe you only had 0.5m.  To me, this doesn’t matter.  If you are a publisher who is focussed still on the number of eyeballs that view your content for selling to advertisers, then yes, you would like to report the higher number.  But in terms of web analysis, the actual number of visitors to your website doesn’t matter, it is the trend over time that matters and with the level of visitor inflation remaining consistent, this trend should still hold true.

What about frequency of visit, whether the average number of visits per visitor or the proportion of visitor who make 1 visit, 2-3 visits, 4-6 visits or 7+ visits?  Well if visitor counts are inflated then these numbers are very inaccurate.  Let’s look at the data for Feb ’10 for very.co.uk, the new online department store in the UK, from Ad Planner.

Ad Planner claims there were 3.1m unique visitors based on cookies for very.co.uk in Feb, 1.2m actual unique visitors to the website with these people having made 4.6m visits.  First of all, the suggestion here is that the visitor count for very.co.uk was inflated by 2.6 times in Feb (but we are still ignoring whether this is accurate or not).  The interesting thing however is that the average number of visits per visitor could be either 1.48 or 3.83 depending on which visitor count is accurate (assuming either is).  That is a big difference.  Just imagine what the difference is for those proportions too.  And all this is the type of difference that would mean you should have very different business strategies.  Visitor counts being inflated may just matter after all…

I was lucky enough to be at a presentation by Avinash recently where a big topic of discussion was campaign attribution.  One of the points he raised was that if the number of visits to conversion for an ecommerce website is 1 or 2, visitor based attribution is fairly irrelevant.  It is only when the visitor makes multiple visits prior to making the purchase that visitor based campaign attribution becomes relevant.  But if visitor counts are inflated, the reported number of visits to conversion is very likely to be under reported and suddenly the behaviour of your website visitors is quite different to what you may think it is.

So visitor level campaign attribution could be important after all, based on the logic from Avinash, whatever the data for visits to conversion may say.  Well yes but no.  The idea of visitor counts being inflated is due to visitors using multiple devices to access a website and also some level of cookie deletion.  And what it means when it comes to visitor level campaign attribution is that you are only recording a proportion of the visits that led to that conversion.

It would mean that whatever campaign attribution method you may use – last click (can we now say this is generally agreed to be less useful), first click, even weighting, proportional weighting – well they all only count some of the visits leading to the conversion so the data and the conclusions drawn from the data are incorrect.  The conversion for the visitor may be recorded on their 2nd visit, the first being via a generic search term and the second being via an affiliate.  Simplistic example but this would still lead to various combinations of value assigned to the different campaigns depending on the attribution model.

What might be missing (if visitor counts are inflated) are those other visits by that visitor prior to the purchase – with these visits maybe coming via an organic generic search term, a link on twitter and also those two visits from paid brand search terms.  What all this might just possibly mean is that the data that is being used to determine budget allocation for the next year based on the carefully researched campaign attribution method just might not be that useful after all.

All of this is of course just hypothetical.  Various claims have been made that visitor counts are inflated but it doesn’t appear yet that this is universally agreed.  Personally I can imagine that as people use their work computer, home computer and mobile phone to access websites, that reported visitor counts are a little higher than actual fact.  And if they are, the above are a few ways in which the data that is being relied upon to make business decisions may be a little flawed, meaning those decisions that are being made might end up being flawed as well.  And this matters.

This post was originally published on AussieWebAnalyst on 10th Mar ’10

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