Why do advanced segments get all the love in Google Analytics? What about report filters and profile (view) filters? Filters and segments work differently. Do you know when you need a filter instead of a segment?
You may think a segment is isolating the data you want and instead it returns too much data. Or you may think you’re getting all the data you want, and later find out a big chunk is missing.
To understand when you need a filter, it helps to know how filters and segments work. I highly recommend a read, or re-read, of Avinash Kaushik’s explanation of hits vs. sessions, because it all comes down to segments working on the session-level, and filters working on the hit-level.
But first let me show you how segments can go wrong. (more…)
The new goal setup in Google Analytics is great, unless you want to organize your goals. Here’s how to cope until (fingers crossed) Google fixes it.
Let me start by saying I love the new, clean look of the goal configuration screens. I love that GA is always adding new features and making adjustments. Every week there’s a surprise, often a pleasant one. One of the best things in the new goal setup is the next to last step: “Verify this Goal.” Why, yes! Yes, I would like to verify this goal before saving it and wondering next week where all my goal data is. Or without taking several extra steps to verify my goal first.
I’m perfectly happy about the clean, new direction of the administrative UI, except for one thing. The goals list is a jumbled mess!
Problem 1: Numbered Goals Now Alphabetized
Previously when I looked at the Goals UI, the goals were listed in the order they appeared in my reports. First I had Goal Set 1, and any goals I had configured in that set, then Goal Set 2, and so on.
Now the goals are alphabetized by the name of the goal, without regard to how they appear in my reports. All my macro conversions are in Set 1 (and they still are), but I can’t easily see that in this list. (more…)
One of the advantages of Google Analytics Premium is that you can get unsampled data, but it’s still processed data. Have you dreamed of getting access to your raw GA data?
Those dreams are about to come true. Announced today at Google I/O: later this year BigQuery will be available to users of Google Analytics Premium.
Query hit-level data at interactive speed
BigQuery is a web service that lets you query billions of rows, a.k.a. Big Data, with a response time in seconds. Without Google Analytics Premium, you upload some data first and then run your queries.
With Google Analytics Premium, your hit-level GA data will be available for the same type of interactive ad hoc queries. Pose a question, get an answer. Does that lead to another question? Rinse and repeat! You can batch queries, too.
Build complex queries and join data sets
Direct granular access to your GA data opens the door for all kinds of complex queries. You’ll also be able to combine data sets from other sources for powerful business insights.
Imagine having data at your fingertips to solve problems like these: (more…)
Your social media traffic data is split across several reports in Google Analytics. Are you taking steps to get it together?
Social media traffic sources appear in Social :: Network Referrals, as well as in Sources :: All Traffic and Sources :: Referrals. They also appear in Sources :: Campaigns if you use campaign-tagged links, not to mention the ones masquerading as direct traffic.
There’s little you can do about the direct traffic, but to get a handle on the rest of it, it’s helpful to understand where the reports overlap and where they don’t. Some of the sources for these visits are accounted for across reports. Others appear only in Sources reports and not in Social reports.
For example, the Sources :: All Traffic report shows visits from t.co and twitter.com and mobile.twitter.com (values of the Source dimension), while the Social :: Network Referrals report pulls them together as visits from Twitter (a single value of the Social Network dimension).
It’s not clear from these two reports whether the two campaign-tagged sources “twitter” and “Twitter” on the left are also pulled together into the social network “Twitter” on the right. Actually it’s not even clear that the other three sources are part of the social network, either, but we’d like to think that, wouldn’t we? (more…)
Keywords can be a rich source of visitor intent. I’m talking about search queries that lead to visits, as well as terms entered in site search after visitors arrive.
But looking at the top 100 or even top 1,000 keywords (ranked by your favorite metric: bounce rate, conversion rate, or whatever you like) won’t necessarily lead to the most accurate analysis because it neglects information in the long tail, which may be on the order of tens of thousands or more keywords.
If you’ve spent any time examining keyword data, you’ve observed similar terms dispersed throughout the long tail. I want to group those terms and analyze each group’s aggregated data to give a more complete picture. So what’s the best way to do that?
Where do visitors go after using your internal site search? How quickly does site search lead visitors to conversion pages? Do specific search terms lead to unexpected pages, for better or worse?
The answers are in your Google Analytics data, but not in the regular Site Search reports. Allow me to introduce Site Search Flow in Google Analytics: a way to use Visitors Flow for site search insight.
In the regular Site Search reports, you get useful data like which search terms resulted in immediate exits and which terms kept visitors engaged or even led to conversions. To see which pages visitors chose, go to the Visitors Flow report instead.
Turn Visitors Flow into Site Search Flow in 3 easy steps:
In part one I wrote that you should use both sides of your brain to do analysis. Use curiosity and intuition, and at the same time rely on structure and evidence. What exactly do I mean? Let’s take a look at a real-life example.
Suppose I work for a university and we’ve introduced a new section on our website. We hope the new section, a center for news and events, will boost views of content that’s previously been overlooked or underutilized. Three months after the section launch, I’ve been asked to find out how this new section is doing. What do the data show?
Last week my colleague Sayf Sharif posted another rant and playfully suggested it might be the second in a series named, “You’re Probably Doing It Wrong.” This week I’m starting a series of my own: “There Is No Right Way.”
There’s no perfect process, no one true path that guides you from reporting to analysis, from data to insights. Instead it takes both intuition and calculation, knowing when to let your curiosity follow tangents and when to let structured thinking rein it in. You have to use both your right and left brain in the art and science of analysis.
Ask Questions: Be Curious and Contrary
Create your own path to analysis by continually asking questions. Start with questions. End with questions. Be curious. What did your visitors do? How did the data change? Why did that happen? How can we make it better? What will you do next? (more…)
If you have Google Analytics page-level custom variables, you might be missing some data. Click and save our custom report to find out!
What’s this about missing data? It’s more accurate to say it’s hidden, and you might miss it. If you’ve implemented page-level custom variables, you might actually be getting a lot more hits on those pages than the standard reports show.
That’s because the metrics in the standard Custom Variable reports are visit-based, not page-based. But pages don’t get visits, they get pageviews. If you hear someone talking about GA visits for a page, remember: “That word doesn’t mean what you think it means.”
In fact, Google Analytics has tried to clarify this issue by renaming the metrics in the standard Custom Variable reports. Instead of “Visits” they now show “Visit Starts” and “Pages / Visit Start” and so forth.
Visit Starts: The number of visits starting with this value of the custom variable.
For session-level or visitor-level custom variables, the last value of the custom variable applies for the entire visit. Not so for page-level custom variables. Assuming you’ve implemented custom variables correctly, their values apply only to the page on which they are set.
If no visits started with the page where the custom variable was set to value X, then value X will not appear in the standard reports.
That’s right, it will look as if you never tracked any page with that value of the custom variable. It might make you wonder, “Did I get the code wrong?” Or, “Did no one see that page?” Or you may chalk it up to another unsolved Google Analytics mystery.
Well, wonder no more. Take a look at this example of our custom report in action. We’re setting a page-level custom variable with the authors of all our blog posts.