When it comes to web analytics and, specifically Google Analytics, Brian Clifton is at the top. The book is the second edition, but it is so much more than just an update of the first book – it’s almost a complete re-write. So much has happened since the first book came out. You can read all about it over on Brian’s blog.

Full disclosure: I was lucky enough to read the book before it was published and offer feedback and comments. I’ve never done that before, but what an excellent learning experience!

So, let’s get down to the book. The name of it says “advanced” but you don’t need to be an advanced user of Google Analytics to get a lot out of it. It takes you from the very basics of what web analytics is, how to get started with both web analytics and Google Analytics all the way to advanced topics and techniques.

Great content for higher education website owners.

Google Analytics vs. Urchin. There’s an entire section in the book about Urchin – the differences between Urchin and Google Analytics and how to choose which one fits best with  your organization. There have been a lot of questions lately about which one is better and what the differences are. This section spells out everything.

On Data accuracy. There is a large section that goes into data accuracy and implications. There is a great part about data misinterpretation that’s essential for newbies and really a good reminder for everyone. This section makes me think of when we deliver reports to leadership. We’re asked all the time why numbers don’t tie out, why unique visitors doesn’t mean “people,” why we don’t want to show hard numbers. This section has some great answers to those questions and much, much more. Photocopy the section and leave it on your boss’s chair.

Reports and implementation. The middle of the book goes into reporting and correct implementation. It’s here you’ll get the nitty-gritty of what each report means and tips about how to implement correctly – including advanced implementation techniques. There is an entire chapter dedicated to “best practices configuration.”

Key performance indicators and real-world tasks. My favorite part of the book talks about key performance indicators and goes into KPIs by job function – the marketer, the webmaster, the content provider, etc. This is where most people get stuck with web analytics – what do I measure? I have all this data to look at and I’m not sure where to start. Start here.

A couple of great KPIs that caught my attention:

• % Brand Engagement – # of visits with branded search terms + # of direct visits/total visits from search engines+total # of directs
• Conversion Quality Index – % goal conversions from referrer X/% visits from referrer X

From the book,

… the conversion quality index (CQI) is all about measuring how well targeted your campaigns are at driving conversion on your website.

Monetizing a non-e-commerce site. One of the things that is the key to getting leadership buy-in and getting things done is the ability to monetize as much as possible. Monetizing a non-e-commerce site is a section we should use to “kick it up a notch.” It talks about assigning goal values and enabling e-commerce reporting for our non-e-commerce sites.

I’ve been experimenting with the first technique (assigning goal values) and using average page value ($Index). This is a great metric that shows if a specific page is generating conversions. For example, if you go to the content report and sort by $Index, it will help you prioritize pages. What specific pages are contributing the most to your conversions?

But what if our *goal* is offsite? In higher education there are so many instances of our *goals* happening off our website. Sometimes they are on a sub-site within our domain. Sometimes they are on a completely different domain – a third party vendor or the like. How do we track to the conversion? There is a section explaining how to do it.

Those are just highlights. There’s so much more.

<update>

One thing I forgot to mention, which should have been front and center and I apologize , is the fact that this book goes deep into all the added functionality of GA in recent months and since the first edition of this book came out a couple years ago. The one new feature of GA that I’m most impressed with is their Intelligence section. I’ve worked with other tools that you can set up alerts with (if our visit rate goes below X, notify me), but I’ve never worked with a tool that will basically do that for you. Of course you can set up custom alerts, but this is different. It alerts you when things are *out of the norm* automatically. We’ve found this so useful. Brian goes into describing exactly how that happens how GA knows something is out of the norm – or how they describe – is a *significant change*.

</update>

For those of us in higher education who use Google Analytics, this should be required reading. Everyone knows that “web analyst” just doesn’t exist in higher education. We’re all jacks of all trades. This is probably the biggest reason this book is so relevant to us. Heard of a technique but just aren’t sure how to go about actually doing it? Brian talks about a technique and then steps you through how to implement it. It does tend to get a bit technical in parts, but for us, that’s a good thing.

As attendees started asking question during and after the session, a theme developed – a theme that we are all familar with and is consistently brought up (especially in higher education).

Common Theme - I/we have issues with our analytics set up/implementation/limitations – what do we do? Help!

I am confident, especially in higher education, that *every* web site has issues with 1 or both of the following:

1. The web analytics implementation.
2. Web site or system limitations that, in turn, limit the potential of the web analytics tool.

I want to reiterate that we *all* have issues with 1 or both of the above (hopefully more of #2 than of #1).  :)

First and foremost, take care of #1 above. Make sure your implementation is as sound as possible.

1. Make sure all your pages are tagged (use the WASP Firefox plug-in to help).
2. Make sure your Google Analytics account number is unique (from other colleges or units within your university). This might seem obvious, but we’ve found instances of duplicate account numbers on college or unit web sites within the same university. This obviously depends on your school.
3. Create some basic filters to make your reports as clean as possible.
4. In the words of Linda Bustos, Don’t do stupid things.

Secondly, data collection on the web is imperfect. Numbers will never, ever tie. In higher education, we have web sites that are glorified portals, use archaeic application systems, and, in the case of large universities, are fragmented and siloed. We have many limitations.

There are limitations that every web site deals with as well. When IT folks tell you the data is crap because of  deleted cookies, users using different browsers/computers, blocking javascript, etc., etc. … smile and say, “get over it.” There is nothing we can do about these and the data will never be perfect, but that’s ok. That’s why trending is important.

Take a look at this recent post by Avinash Kaushik about being comfortable with incomplete and imperfect data. Read. Heed.

Remember some basic facts …

Fact 1. Web analytics is in its infancy. In higher education, it isn’t even born yet. I’m not a biologist, but I’d say it’s about a zygote.

Fact 2. Web analytics is not cookie-cutter. Each site implementation/set up and site analysis will (and should!) be different.

Fact 3. Especially in higher education, measuring through to the actual conversion (enrollment, registration, membership, etc.) is very hard and, in some instances, impossible.

In higher education, we need to focus on what we *can* do … and there is so much we can do.

Use micro-conversions:

• clicking on “register”
• using the knowledgebase
• watching a video

Look at the data that is screaming at you:

• top landing pages with high bounce rates (find out why!!)
• 404 page referrers (clean them up!)
• top internal search keywords (terminology) you’re not using (or not using prominently!) on your web site

I also think that there are some (yes, rare) instances when we realize that an implementation might be so hosed that we need to blow it up and start over.

There may be many reasons for this, but whatever the reason, the data that we’re getting now is really useless.

Obviously there are many instances where this is *not* a good solution, but depending on your circumstances, it might be time to start over … as painful as that seems. This is my opinion and I do realize that a lot of people disagree with me here, but there you go. I said it.

Whatever the issues (and we *all* have them!), get over it. Find something that *can* be done. You’ll be surprised at how many actionable insights you can get out of your web analytics, even with all our limitations.

The greatest thing about all this? The simple fact that you care about this stuff means you’re way ahead of the game in higher education. That’s fantastic and makes data folks like me smile on the inside. Baby steps, baby steps. Do what you *can* do.

The medium doesn’t matter – emails, newspaper ads, banner ads, search engine paid keywords, TV ads, the list goes on and on.

Let’s take email, for example. Do you send emails to students, applicants, or prospects? Alumni? Faculty? Staff? Do your emails have links back to your Web site? It could be reminders to register for classes, pay a bill, buy football tickets, whatever. If there is a link back to your Web site, that is an email that can be tracked and later, using the data, optimized (improved!).

Ok, so you’ve created a fantastic email (or series of emails) and are ready to send it (them) out.

Wait … hold the phone. Are your call-to-action links tagged? In order to track beyond the click-through you must tag your links. Depending upon your analytics tool, you might do this a bit differently with each one.

For Google Analytics, use their URL builder. Other analytics tools have something similar as well.

Ok. Links tagged. Good. Now cross your fingers and send the email. Depending upon your email provider, you’ll get a lot of analytics about the specific email including open rate, delivery rate, click-through rate, and more. All fantastic stuff.

Beyond the click-through. Let’s say your click-through rate is pretty good. Ok, good start.  But now what? Don’t get me wrong. There is obviously great things you can get from click-throughs. But what next? How do we know if the campaign was truly successful and/or, how do we know how to improve it going forward? After these people get to the Web site … then what are they doing?

Conversions. We all know that, especially in higher education, tracking to the conversion might be difficult. Systems are off-site, too many systems, the list goes on.

If you can track to the conversion (your success event), that’s fantastic. Even if you can’t track to the conversion, if you can track *toward* the conversion, that’s better than nothing.

For instance, if your success event is filling out an application, but the application is in another system that can’t be tracked, you can track the “apply” link (that goes to the application system). So, in this case you can track the click-throughs from the campaign and see how many of those click-throughs led to the “apply” link.

Other useful nuggets. Let’s forget about conversion for right now, though. Seriously? Yes. Seriously. After the click-through, you can find out:

• The bounce rate of users coming in from that campaign (if you get 1,000 click-throughs and of those 85% of them bounce, there’s something wrong)
• The pages those users visit after coming in from that campaign. Are those users interested in a particular thing? Obviously they’re probably interested in whatever the topic of the campaign was, but look beyond that. Are they visiting a particular *other* page or section in big numbers?
• Time spent on the site of users coming from that campaign. Are they leaving after 30 seconds or staying for a while?
• Loyalty of the users coming from that campaign. Do the visitors from that campaign come back to the site a 2nd, 3rd, 4th time?
• Internal site search keywords the users coming in from that campaign are using (you know I love this one!).

The list goes on.

With Google Analytics advanced segmentation,  with Omniture SiteCatalyst and Discover, with most analytics tools, you can segment out the traffic coming in from specific campaigns and track user behavior. What are users from that particular campaign doing? Why do you care? What if they’re doing *nothing* beyond the click-through? What if they’re doing something very specific to that segment beyond the click-through? Optimize. Improve.

Do you have to look at *all* of the above? Maybe. Maybe not. It depends on the goal of your campaign, the goal of your Web site, etc.  Which one you look at really isn’t the point, though, becuase that will vary.

The important thing is to look beyond the click-through. The click-through is not telling you the whole story. You could be wasting money on a campaign that’s bringing you very low quality traffic. You could be missing a perfect opportunity to improve your campaign for next time.

How?

If someone searched for this blog on Google in the past, the search string would look like this:

http://www.google.com/search?hl=en&q=trending+upward&btnG=Search

Today, it looks like this:

http://www.google.com/#hl=en&q=trending+upward&btnG=Google+Search&aq=f&oq=trending+upward&fp=3WTwdsC3GPc

Notice the difference? The second one uses a hashtag (#) instead of the more familiar “search?.” The problem lies in the fact that nothing after the hashtag is passed through to the analytics tool, so your referrals look like they’re from http://www.google.com instead of the actual search string. This also means that there are no search keywords associated with it. Not good. How do we know how users are searching on Google to find us?

From what I’ve read, this is just a test, but who knows how long it will go on?

According to today’s post on the Clicky blog, this isn’t happening across the board yet. Go to Google right now and search on a keyword. See if it’s happened on your end yet.

What does this mean for us, though? A lot, unfortunately. Since almost 75% of our natural search traffic is from Google, this will be a big deal.

Here’s a quick screen shot from this search traffic this morning. It’s already affected our results:

Not only that, through the Clicky blog, I read this post on Smackdown and it spelled out other implications. According to Smackdown:

… this would affect Firefox plugins that existed (definitely some existing ones would stop working), break some of the rank checking tools out there (they would have to be re-written I’m sure), and even some people asking if it would thwart serps scrapers from using serps for auto page generation (not for long, no).

While those things would definitely be affected in at least the short term, there is a much greater impact from Google switching to AJAX.

All of the issues mentioned involve a very small subset of the webmastering community. What actually breaks if Google makes this switchover, and is in fact broken during any testing they are doing, is much more widespread. Every single analytics package that currently exists, at least as far as being able to track what keywords were searched on to find your site in Google, would no longer function correctly.

I’m hoping that either Avinash Kaushik or Brian Clifton post about this soon. I’m really interested in getting their take as they are both close to Google.

Update: Chatter on Twitter and blogs asks if javascript can be used for a workaround. According to yesterday’s post at SearchEngineLand, Sean from Clicky states:

Yes, Javascript can be used to track the #hashmarks at the end of the URL. This isn’t the problem though. Javascript has a variable called “document.referrer” that contains the URL of the referrer for the page you are currently looking at. Web browsers don’t store the hash in this variable, so there is no way for us to get it.

So, the issue is with browser not passing through the variable (because they don’t store it), not with the analytics software not being able to capture it.

Update 2: Google finally responds. It states that this is a test, but it doesn’t state how long the test will run for nor do they address the implications for web analytics tools.

The ironic part for me is that they mention the change is going to improve the user experience, which is great. In doing so, however, Google may be hindering other websites from improving their user experience. Referring keywords is one big way to help improve the relevance of your own website (thus improving the user experience on your site).