DigiKnow continues to offer our analytics seminars that we started late last year. Since we just had our latest one yesterday, I figured I’d take a few minutes today and blog about the questions that came from our attendees.
Question 1: We moved from AWStats to Google Analytics and our data doesn’t line up to compare year-over year. Can you explain this?
Matching data between various analytics systems is always a tricky thing. Moving from a log based system (AWStats) to a tagging based system (Google Analytics) is even more confusing. Log based systems track every interaction on the website, including image views, PDF downloads, etc. Tagging based systems only track page views, unless specific code is in place to track downloads. If one system is considering PDFs as page views and the other is ignoring them, you will have a discrepancy in your data.
A major area of discrepancy for this attendee was in their traffic sources. Although I don’t remember the exact numbers, AWStats was reporting something like 30% search, 30% direct and 40% referrals whereas Google Analytics was reporting 60% search, 20% direct and 20% referrals and 2% other (I realized these don’t add up). This is significantly different and as it turned out was due to two main factors: First, AWStats was reporting alternative domain names (ex: site is xyz123.com but abc987.com also gets you to the site) as referring sites, dramatically boosting the amount of traffic from referrals (I think it was 15K vs. 5K between systems). Secondly, Google Analytics was tracking banner and paid search campaigns in the “Other” category, whereas AWStats was putting that either within direct traffic, referral or search.
Question 2: On our Ecommerce site, my development team said we can’t use Google Analytics because of the way the pages are structured. Is that true?
This one is a little less obvious since we weren’t looking at the attendee's website, but given what she was describing, it sounded like they were able to get the data into Google Analytics, but it wasn’t displaying anything useful. I’m speculating that, because this was Ecommerce, they were including a transaction ID value in the URL string, so that each visitor’s pages were unique. Visitor #1’s visit to www.xyz123.com/product?id=4564&transid=1 and Visitor #2’s visit to the same page looked like www.xyz123.com/product?id=4564&transid=2. They are the same page, but to Google Analytics, looked completely different. The solution I suggested was to apply a Google Analytics filter to the profile that would strip off the Transaction ID (“transid” in my example) and then the two pages would look the same.
Question 3: To install a tagging based analytics packages, to I have to edit every page on the website?
This one is a little easier and the answer is yes. But what marketers not familiar with HTML and server side scripting don’t know, is how includes work and how easy that can make update every page on the website. An include is a sub-set of the full page HTML that is stored in a separate file for reference by a page. A single include is often used as a footer for the entire website, so a single update can change the entire site, making adding a tracking script just a few minutes of work.
If you have any questions that you’d like to see answered on this blog, please email firstname.lastname@example.org and we’d be glad to help. If you are in the Cleveland area and want to attend one of our seminars, please feel free to email about that too.