So how easy is it actually to compare AWstats with any js based method which generally at least can ignore the above mentioned groups of “fake visitors”?

I too am very interested in this subject and have just run part 1 of a test to try to quantify the delta between GA and others that measure traffic in similar ways.

How many page-views are you really getting? Part 1 – The WordPress Stats Test

http://www.synaptici.com/2010/how-many-page-views-are-you-really-getting-part-1-the-wordpress-stats-test/

Hope this is of interest and would love your feedback.

Cheers,

Gil

I agree that the most significant use of Google Analytics is through trend analysis and that’s what it has always been pushed as however I’m guessing that you yourself look at specific clicks and specific keywords and specific in-bound traffic from referral sites?

This is the interesting thing. All web analytics were once used as trend analysis only however now I don’t think they are.

I know I make business decisions every day that in some part rely on Google Analytics.

The question doesn’t get asked often enough as to whether or not those decisions could be misguided by relying on these statistics.

I see this quite regularly however it is usually only single figure percentage points of difference between click tracking systems and Google Analytics referrals.

Coming from the other side of the coin myself (working as a publisher) I would be saying to you that GA is not always 100% correct, that we track every click and that web statistics themselves are an imperfect science.

That holds true for a discrepancy of a few percentage points however it doesn’t work with the level of difference you’re looking at.

You need to ask a few questions, both of yourself and your advertisers;

- Is the click tracking system being used changing where the referral appears to be coming from? This could account for the difference in your stats.

- Are some of the clicks coming from bots, not real humans? Some publishers don’t proactively remove bot traffic from click reports although respectable publishers will.

- Finally, is your Google Analytics accurate? For instance are you certain you have the code on all pages.

Hopefully that helps.

I’d suggest putting in a secondary web statistics program onto your site and compare the results for a month to check that the statistics match.

We’ve got a product site over at http://www.epiphanyrisknetwork.com, we’re currently disputing the data we are receiving from a major advertising site, versus the hits recorded at our end by Google Analytics.

The advertising site vendor argues that they are pushing 2-300 hits per month in our direction, yet Google Analytics has recorded only 6 of these hits arriving from their site in the same time period. Obviously, that is a significant discrepancy.

We’re not expecting tens of thousands of hits, as we’re offering risk management software to the enterprise market, but it is the variance between their figures and ours that is confusing here.

The ad site is a major industry player – a site with a mass of daily news articles, white papers, and other big advertisers on their site (professional services etc). They state their webstats for users etc online, and appear very credible.

However, we can’t seem to reconcile how they can report a click through rate from their site to ours that is 40-50 times greater than we are seeing in our Google Analytics stats?

One of the challenges with web advertising is the lack of an independent body to oversee published stats. The newspaper industry is audited, and can published figures that are referenced against the auditing body. However, most websites can say whatever they want, and how can it be verified?

In this instance, the ad site is very credible, and has been around for a long time. So how do we account for the difference between their figures and ours?

Be very interested in your views?

My guess is that the 50% level might come up where there is a blanked assumption that does not correlate between the two systems e.g. something like a report on one analytics tool that includes uniques matched across different sub-domains when they are treated as separate on another.