Let’s cut right to the chase:  Google Analytics (GA) is lying to you.  Huh?  How is GA lying to you, you ask?

Let me illustrate, via screenshot, my beef with my old friend Google Analytics:

Can you believe that?  I know I can’t.  What’s that you say?  You don’t see the lie?  Let’s try that again:

Yes that’s it!  Goal Values are not “optional”!

We all know that Google Analytics is the best thing since sliced bread; what other product could give you so much business intelligence at its price point (free)?   Unfortunately, to collect data with Google Analytics one must only copy and paste the Google Analytics tracking code (GATC) onto their site and that’s where most businesses stop.  To get the most from Google Analytics, code customizations; including events and custom dimensions are probably required.  But most importantly—your company must sit down and answer some tough questions.  Some of these include:

1. Why do we have a website? (Really, you have to answer this.)
2. What do we want people to do when they get to our site?  (Desired outcome.)
3. How do we measure these outcomes?  (Goals in Google Analytics.)
4. How much are these desired outcomes worth to our business? (Goal Value.)

If you need help with the questions above, please visit Avinash Kaushik’s brilliant post on the Digital Marketing and Measurement Model.

Okay, so you’ve answered the questions above, your marketing efforts have clearly defined objectives and you now know your reason for being; it’s time to set up your goals WITH goal values.

Goal Values again?  Yes!  (That’s the theme of this post, remember?)  Ninety percent of the new GA accounts we review as a Google Analytics Certified Partner either do not contain goals or goals are present but goal values are missing.   You must commit to ALWAYS adding a value for your goals.  With values added, two magical things happen.

1.  Per visit goal value is calculated:

What’s Per Visit Goal Value tell me?  Well, at a glance it can tell you:

a. How effective each of your campaigns, sources, affiliates, ads, ad groups, and more are in real dollars!

b. How much you should be bidding for each keyword.  (Really.  Look at per visit value in the goal set view of your keyword reports.)

2.  Page Value  (Content Reports) is calculated:

Page Value tells you how each of your pages perform in the context of helping people convert.  Now this data requires more analysis than Per Visit Goal Value because checkout and home pages will always have a high value, but it can help you understand which middle of the funnel pages perform best and help your visitors to take your desired action.

In summary, Goal values are not optional.  You should always endeavor to understand how much goals add to your bottom line and add the values into GA.  That said; if for some reason you find it too difficult to determine the value of key interactions– any number is better than nothing.  So start with $100 or $1 or some other arbitrary value.  From there you can work toward some relative goal values (e.g. contact us submission = $100, newsletter subscription = $10) and then hopefully as you gather more data, real goal values for each outcome.

Finally, there is one exception to this rule.  Goals for Ecommerce transactions are desirable for many reasons, but if you are using GA Ecommerce tracking, then your corresponding goal should never have a value as this will distort the calculated values we mentioned previously.

Google Analytics Premium customers smiled a little brighter yesterday after one specific announcement was made at the Google I/O. An additional feature, Google BigQuery, is planned to become available to Google Analytics Premium users later this year.

Talk about a game changer. This goes far beyond just Web Analytics and opens the doors to a tremendous scope of opportunities. But, the basics are just amazing enough to make me anxiously call out a couple of them.

Access and query your Google Analytics data alongside and in combination with any of the other data sets your company derives for its business intelligence needs; all in a matter of seconds.  I’m talking about trillions of rows of data right at your fingertips.  Really, you can export the data, print it out and touch it if you’d like.

As if Web Analytics data isn’t multifaceted enough, there is an abundance of other data sets companies collect that are just as intricate and essential to running their businesses. With more and more organizations aware of the “big data” phenomenon, key stakeholders are starting to ask more complex questions and are leaning on their analysts for the insights to make better-informed business decisions.  And they should.

This exercise can be daunting; exhausting; frustrating and sometimes near impossible. And, chances are most time is spent trying to query, join, and report on the correct data which means less time for true analysis to provide actionable recommendations to decision-makers.

The Google Analytics Premium and BigQuery integration is planned to ease these pains. It will provide granular data access for you to:

• Join and cross-tabulate multiple data sources

• Understand complex queries

• Create detailed custom analyses

• Identify what’s really going to drive and improve the bottom line

With so many possibilities, you can get lost in exploring them all.  But, before you meander off on your own, make sure you get back to the boss on that request from…

As an analyst, one of my favorite mantras is:  “Keep Digging”.  Actually it’s probably “Always be Segmenting” or “If you are not Segmenting, you are wrong!” but for the purpose of this post we’ll stick with “Keep Digging”.  Whatever it is that drives you to keep drilling through reports and looking at every facet of your data is unimportant.  What’s important is that you do it.

So how do Shortcuts help you as a user of Google Analytics (Ga)?  Let’s walk through a scenario to illustrate when you may want to use GA Shortcuts.  Let’s imagine that you are using GA to optimize the performance of a new campaign.  Inevitably you’ll find yourself three levels deep in a report with an alternate view (something other than what GA presents for you), and a filter and segment applied — and there it is – the data nugget you’ve been looking for, the key to optimizing said campaigns!  You share the data with the rest of your team and your boss is amazed by your ability to squeeze so much from Google Analytics.  Now they ask you to show them how you got the data.  You pour back into the reports and … well … you get lost and can’t recreate what you’ve reported.  Don’t be embarrassed, it happens to everyone — until now.

Enter Google Analytics Shortcuts.   Now as you drill deep into reports you can simply save a shortcut and once applied, it will take you right back to the view, segment and report as if you’d never left!  It’s like Hansel and Gretel’s breadcrumbs, but faster.

So let’s use one of my favorite reports – Landing Pages – as an example.

This is how the landing page report is presented in GA (with page names redacted):

As you can see by the red box above, we are in the view that GA presented to us, which is known as Data (Table) view.  By itself, a nice report with some useful data; however, with a little customization, this report is more powerful.

Let’s change views, add a filter and a segment for paid traffic:

As you can see in the red boxes above, we’ve filtered for pages containing “index”, changed our view to comparison and selected bounce rate as the metric we’d like to compare.  Now we can see the index pages’ bounce rate as they compare to the site average for paid traffic (in this view you are not able to see that the paid traffic segment is applied).

Let’s just say that this is reporting nirvana for you and its how you always want to use the Landing Pages report.   In the past, to recreate this you’d have to make a note of what you did and then follow these steps:

1. Go to Landing Pages Report
2. Change Views to comparison
3. Select Bounce Rate for your comparison metric
4. Add a filter for “index”
5. Add an advanced segment for Paid Traffic

Wow that’s a lot of steps!  Google Analytics Shortcuts makes this so much easier.  To save your Shortcut:

1. Click on Shortcuts

2. Name your Shortcut (something relevant)

And that’s it!  Now when you want to apply those same settings, go to Shortcuts in the left-hand report navigation:

Click on the name of the Shortcut that you want to apply and GA will revert to the report, view, filter column selection and segment that you had applied when you created the Shortcut!  (Date ranges do not change.)

I’m sure you can think of many more uses for Shortcuts.  For now Shortcuts can not be shared with other users, but they are still one of my favorite new features in Google Analytics.

In January Google updated its image search algorithm to improve the user experience. The “new” Google Images now shows the selected picture in a black display pane.

By comparison the older version pulled up the website in the background. This seems like a straightforward enough change, certainly in line with Google’s many adjustments geared to a cleaner visual experience.

However, for websites that have many images in the Google index, there is a less than exciting result to this new and improved user experience. The newest update to the image search decreases the referral traffic from Google.com.  Because the new display feature does not pull the website up behind the selected image, the Google Analytics code does not have a chance to fire, removing the page view credit and decreasing referral traffic.

Websites with substantial image libraries will see the decrease in Google Images traffic starting at the end of January when the switch was made. Traffic from Google Images can be found under Traffic Sources > Sources > Referrals> Google.com. To identify the Google Images traffic specifically, click Google.com from the referral sources listed and look for /imgres.

In order for the Google Analytics code to fire using the new Images features, the searcher must select to visit the page by clicking on the image a second time, or selecting one of the two buttons to the right of the image. Although the impact of the change may appear extreme for some sites initially, the new functionality will result in a truer site visit, rather than one indirectly generated by Google’s Image search.

As I mentioned in my last posts, AdWords Bots in Google Analytics (GA), and Bot Traffic in Google Analytics we’ve seen more and more bot traffic registering as visitors in GA.  (Bots are automated programs that normally don’t fire JavaScript or images, so this traffic shows up in web server logs, but not in Google Analytics.)

In this post we’re going to discuss how to block these bots from your GA reports so that you can have clean data from which to make smart business decisions and make more money!

First let’s understand that “well behaved” bots usually have one of two fingerprints:

1. Technology profile – this includes browser user agent, version, java profiles, etc.
2. ISP – Yes that’s right, Microsoft, Yahoo! and Inktomi bots come from Microsoft, Yahoo! and Inktomi ISP’s.

What kind of tracks does your bot traffic leave?  Well it’s best to start with an Advanced Segment that begins to whittle away this bot traffic from the real traffic.  Apply this segment to your Google Analytics data and you’ll be viewing only direct traffic that bounces.

https://www.google.com/analytics/web/permalink?uid=BH3Nk-LCSpmv7UQyPXgGDA

Do you see any patterns in the browser version reports?  (Audience>>Technology>>Browser & OS)

In the screen shot above we’ve selected “Mozilla Compatible Agent” and version 5.0 with no Java Support looks like a likely bot suspect.

What about identifying bots by ISP? (Audience>>Technology>>Network)

So from the data above we can see some interesting sources of bouncing traffic.  Next I created another segment to view each of these ISP’s (1. microsoft corp , 6. yahoo! inc. and 16. Inktomi Corporation).

https://www.google.com/analytics/web/permalink?uid=nt7BieC5RR24dRcai6Kb_g

As you can see from the data above, all of the traffic from these ISP’s bounced.

Your next action is to decide if you want to go a step further and actually filter out this traffic from your analysis profiles.

If you decide to block traffic based on browser profile, then you’ll need to construct a series of filters to do this.

The first two combine browser data together with Java Support (yes or no) to allow you to then filter only the offending browser profile (Third filter listed in the image above)  that is utilized by the bots.

If you decide that the ISP route is the way to go, then you’ll have a much easier path:

Where the pattern is equal to   yahoo|microsoft corp$|inktomi

Either way, you should remember to:

• Always have an unfiltered profile that collects all data

• When possible, test your new profile filter as a segment

• Always apply a filter first to a test profile and when happy with the resulting data, to your analysis profile(s).

• Consider creating a profile to capture the traffic you’re excluding so that you can monitor it more easily and ensure that you’re not excluding any “real” traffic.

In summary, it really does not matter why the bot is on your site, what’s important is that they are triggering nuisance pageviews that can skew your numbers and conversion rates.  Are they affecting your GA data?  Apply the segments above and find out for yourself!