No matter what kind of redesign you might be doing (full scale or smaller one), clickstream web analytics can help both benchmark where you are (how do you know you’re successful if you don’t know where you are now?) as well as help in the process of coming up with potential tasks for usability testing.
This past July we redesigned the Penn State World Campus website. The redesign was driven by data – using clickstream analytics, onsite surveys, as well as extensive usability testing. Our old website was last redesigned in early 2005 so we thought it was time, but how did we really know we needed to redesign the site? What did we want to accomplish and how did we use web analytics and usability testing to keep internal opinions out of the process?
First thing was first – we documented the goals of the redesign as well as what can be measured to know whether the goals were met – the KPIs. In coming up with the KPIs we *didn’t* do two things: 1) we didn’t just think about metrics that we could get in Google Analytics and 2) we didn’t limit ourselves to what we could measure at the time. The second one is important so you can be sure to implement measurement for whatever that item is in the new website.
Here is a really quick example of what we learned from our web analytics and applied it to usability testing
We are obsessed with our internal site search keywords. People are telling us what they want in their own words. One of our goals was to decrease the usage of our internal site search and our knowledgebase for what we called “easy” topics. Granted, some people will always go right for site search instead of browsing via navigation, and that’s fine, but we wanted to see how many “easy” topics were searched for a lot within our internal search and our knowledgebase.
“Tuition” was the top keyword searched for the prospect audience (we filtered out current students). Although the popularity of the topic didn’t surprise us, the fact that people had to search for it to find it did. After all, it was listed clearly on the homepage.
Tuition is one of the most popular areas of our website. We assumed that it was easy to find as it is not only linked from our homepage, but also from all of our program pages. But, seeing it as the number one searched for item within our internal site search made us want to really test this out. Is this just because some of the population uses search and “tuition” is a popular topic? Could be. Is this because tuition is really hard to find (even though *we* think it’s easy). That could be, too. We decided to find out.
We added a task for tuition to the usability test. We wanted to know how easy it was to find tuition *and* how people searched for it. Seems kind of obvious now, but we were a bit surprised that even when the task specified a degree program (i.e., “You’re interested in the Bachelor of Science in Criminal Justice; find the tuition rate for this degree program”), most users tended to search generally instead of drilling down into the program first. They were looking for tuition, then the degree, not the other way around. Again, might seem kind of obvious now, but because a lot of important information “depends” (from tuition, to the admissions process, etc.), information on the old site was definitely program-specific without a lot of “general” information.
As a result of that test, we put tuition in both places (generally and within each program area). Getting around the “it depends” issue was tricky, but we decided to have that information live in one place, but be fed to both places – to the specific degree page as well as to a general tuition page.
“Tuition” is searched for much less now (down to 13th popular term for prospects). But now we are finding some unexpected other topics at the top of internal site search and you can bet we will be testing those out in near future.
So there is a simple example of how our clickstream analytics told us what was happening and then drove us to dig deeper with usability testing. We’ll be talking about this and much more during our session of the series including *how* to set up KPIs and segments for those KPIs in Google Analytics, how to report on your KPIs after the redesign, using onsite surveys to answer the *why*, specific examples of how usability testing helped us solve internal debates on layout and design, and much more.
It should be an awesome series, too. The first webinar in the series will be from Stewart Foss, founder of EduStyle talking about the top trends in higher ed redesigned websites. Next up is Mike Richwalsky, Director of Marketing Services at John Carroll University. He will talk about planning for and managing a successful redesign project. Dave Housley and I will round out the series talking about using data in the redesign process – before, during, and after.
Hope to see you at Web Redesign Boot Camp!]]>
By using a combination of Google Analytics events and custom reports, tracking the off-site conversion is a little easier. Does this give us a direct link from the visitor on the website to the actual conversion (if the conversion is off-site)? No. But it’s the next best thing – and it’s certainly better than guessing – or only paying attention to campaign click-throughs.
Events are not new to Google Analytics, but they are definitely underutilized. By using an onClick on the exit link (or download to a form, etc.) you can easily track those exit links as events and then tie them back to campaigns.
When you track an event, you specify the event category, action, label, and value. A more detailed description can be found in the GA event tracking guide. In post we’ll only be talking about using events for tracking outbound (exit) links or downloads.
If you want to track an outbound link as an event, here’s how to do it:
Once that is in place, go into Google Analytics reports. To view events go to content >> event tracking.
Here is how it looks in the reports. “Outbound” is the category. “Click” is the action. In the below image, if you click on “click,” you then go to the event label screen shown in the second image below.
“Application Link” is the event label in this example.
This report then shows you the number of times the outbound link was clicked. Be sure to use the same naming convention with your category, action, and labels. Otherwise you’ll end up with outbound, Outbound, and exit (when they all mean the same thing). Think campaign URL parameter names (email, e-mail, and E-mail) – keep it consistent. By the way, I chose “outbound” for the category and “click” for the action above because that is what is used by default in the gaAddons script (introduced below).
So there you go. Simple, right? Now you’re tracking your outbound links as events. Well, hold on to your hats, here’s where it gets good.
Stéphane Hamel, web analytics consultant, wrote a great script called gaAddons. This script automatically tracks outbound, download, and mailto links as events (and more). It’s so important for higher education, in my opinion, for a few reasons:
This script overcomes all 3 challenges.
If you’re still using the old version of the Google Analytics tracking code, there is an older version of the gaAddons script that can be used with the old version of the GA tracking code. Hopefully everyone has upgraded to the newer version of Google Analytics tracking code, however. There are so many advantages of upgrading to async. Also, in the gaAddons version 2.0 (to use with the new version of Google Analytics tracking code) there are so many more options available. Of course you can track outbound, download, and mailto links as events, but you can do so much more with it.
So, now that your outbound or download links are being tracked as events, you need to be able to easily see how your campaigns are doing driving people to those “events.”
This is where we’ll take advantage of custom reporting.
The way events are currently reported in Google Analytics is clunky. It’s great that they are there, but beyond just seeing those events, it’s hard to determine if campaigns are driving people to those events.
So we create a custom report to more easily show this data. To set up a custom report, in the left nav in Google Analytics, click on custom reporting >> manage custom reports. Then click on “create new custom report.”
Here’s how to set up this specific report. I call it “events by campaign.” You can call it anything you’d like.
You can also use the shared custom report by clicking here.
This custom report allows you to see all at once if campaigns led to any events. Here is the first page of the custom report:
So all your campaigns that led to an event are listed. (not set) means no campaigns led to those events.
Then, if you click on “campaign #1,” you drill down to see which specific events were credited to campaign #1.
Now you can both see your important outbound (exit) links as “events” and then easily tie those events to campaigns.
So there you have it. Not perfect (we’d all love to see how many off-site conversions our campaigns drove), but it’s better than guessing – or just looking at click-throughs.
What do you think? I’d love to get your feedback about tracking important outbound and download links.]]>
Now let’s talk about visitor recency.
Visitor recency is simply taking your returning visitors and it measures how long it’s been since they’ve come back within a certain date range. In other words – how loyal are your returning visitors within the specified timeframe?
So who cares about visitor recency? How can it help?
Let’s take a couple of scenarios.
Scenario #1: you run a blog (maybe a student, admissions, or alumni blog). What is the goal of the blog? Whatever the goal – engagement, conversion, whatever – you need people to come back, right? We went over visitor loyalty last time. But, you don’t just need people to come back, don’t you want them to check back often? If you post fresh content frequently, you want people to come back frequently to check out your content. For a blog, we want “high” visitor recency. In other words, we want people to come back often within a day or 2 or 3.
High visitor recency looks like the image below – visitors usually come back within a week. Depending on how frequently you update your posts, *high* visitor recency for your blog might be different. For instance, if your blog was updated multiple times a day, you’d want people to come back more frequently than a week. High recency for you might be within 1 or 2 days.
Scenario #2: You run the admissions website. Although you want your visitors to come back, how many visitors are going to research the school, start the application, and submit it all in one visit? Not many.
Unlike the blog, although we want these visitors to come back, they probably won’t come back as soon as the blog visitors. The buying cycle is too long for this crew.
The example below isn’t a perfect low recency example because there is a distribution at the high-end, but pay attention to the distribution at the bottom. Notice how there is a much higher distribution at the bottom than that of the blog (above). The below website has a much lower recency.
Notice also that I filtered out new visitors. If you don’t filter out new visitors, Google Analytics will show new visitors in the first row. The distribution will be different, however. This is because the percentages will be based on all visitors. When you filter out new visitors, the percentages will be based on all returning visitors, not all visitors. This way you’ll get a better idea of the distribution for your returning visitors which is what you’re looking for with Recency.
When should I use Recency as a key performance indicator?
Although I probably wouldn’t use Recency as a KPI for a website whose target audience has a longer buying cycle, I would definitely use it for a blog or other website where the goal is frequent engagement – you not only want people to engage – you want them to engage frequently and often.]]>
Do our visitors want to come back for more?
Let’s take an admissions website. The conversion usually doesn’t happen on the first visit. The visitor might research the admissions process and programs offered. They might take the virtual tour. Maybe they read some student stories. The point is, this is rarely linear – come to site, take virtual tour, read student stories, apply – all in one visit.
Ultimately, they usually need to come back.
So what does loyalty look like?
Notice that almost 80% of visitors only visited once. This website might be great at acquiring new visitors, it needs to work on visitor loyalty.
This looks a bit better:
There is still a large percentage that only came to the website once, but there is more of a distribution at the bottom.
Take a look at this segment of visitors – obviously a more engaged bunch. A good 48ish% is very loyal.
So, what kind of distribution is good? That’s going to be different for different websites. The more loyal the visitors, the bigger the bottom of the distribution will be. Depending on the website, though, a bottom-heavy distribution might not be realistic or necessary.
To come up with a goal for your website you can take a look at a few things. First, what is your current distribution? Where are the majority of your visitors? Use that as a benchmark. You can also take a look at how many visits it usually takes for someone to convert. Take a look at how many visits it takes for people to do other important things on your website (maybe your micro-conversions). Taking all these into account, come up with a number for your website. Set that goal, then see how the website improves over time.
The type of website is going to matter as well. If the website is for prospects, the distribution will probably be a bit more top-heavy. If the website is, let’s say an intranet or a blog, the distribution might be top *and* bottom heavy – meaning you get a good number of *new* visitors, but you also have a good number of very loyal visitors.
Warning: If your website caters to more than one audience – for instance, if your website is for prospects *and* current students, when looking at the loyalty report, be sure to filter out the audience that you’re not measuring at the moment.
Quick example – if you’re using loyalty as a KPI for your prospects – let’s say your website goal is to increase applications and one of your KPIs for that goal is visitor loyalty (because you know that people don’t usually apply on their first visit), filter out your current students. If you don’t, the numbers will be skewed and misleading.
Using visitor loyalty with campaigns. Another great way to use Loyalty is with campaigns. Hopefully campaigns are tagged correctly. If they are, you can easily build an advanced segment for visitors coming in from specific campaigns (or a group of like campaigns – let’s say brand campaigns).
Then go to the visitor loyalty report showing data from the advanced segment that you just created. Traffic from campaigns is obviously nice, but how many times does the traffic come back? Are they one-hit wonders?
Cookie deletion and visitor loyalty. Does cookie deletion affect visitor loyalty? Yes. That’s why it’s important to set a goal and see your trend over time. No matter what the cookie deletion rate is, if you look at your trend over time, the deletion rate will remain basically the same, so it shouldn’t matter too much.
Next up, visitor recency. Loyalty is great when used with visitor recency (when visitors do come back, how much time is there between visits – a day, a week, a month?). If content is updated frequently, let’s say a blog or an intranet or an IT alerts website or any other website that’s updated frequently, visitor recency is really important.
In part 2, we’ll talk about visitor recency. Stay tuned.
The executive summary is out and available at the HigherEdAnalytics website. Taking a look at it, a few initial thoughts came to mind. I’m just going to run through them here in no particular order.
First, I was ecstatic that 95% of respondents track website traffic (I know it’s not 100% but in the words of Bill Murray – baby steps). What struck me, though, was that a full 35% did not track any conversions and of the 65% that do, a minority track clickstream and conversion from marketing campaigns (email, online advertising, print, etc.). Now, this may just mean that they aren’t in the marketing department. I’d love to see that data segmented by department. I’m hoping that the majority of folks in the marketing department do indeed track those stats. What’s more – even those folks *outside* the marketing department should be tracking if they do any kind of external communication via emails, social media, etc.
The report also states that 15% of respondents said they do nothing with the data. That makes me sad. : (
The most interesting part of the report for me was around tracking conversions. The “would like to track” column being the most intriguing. To me, this shows that we *want* to measure conversions, we just can’t for one reason or another. In other words, we need help. The more I wondered about it the more I wondered about the reasons why we don’t (or can’t) track conversions … maybe:
Whatever the case, it seems as though we really want to. Now we just need help to be able to do it.
Another area of the report that was interesting was the question, “who spends at least 20% of his/her time working on analytics?” 35% responded either 1, 2, or 3 people. This astounds me – in a good way. I was shocked to find that number so large as I realize that so many people in the higher ed web world are jacks of all trades. Obviously it would be awesome if at least some reported that, “it’s my entire job,” but … baby steps. As an industry we’re certainly nowhere near that yet. It’s definitely a good start, though. I know there were 50% that responded nobody : ( but this surprised me much less. I actually thought that number would have been higher.
Although the majority of people said they were tracking the basics – visits, page views, etc, when we get past the basics, the percentage really drops off. I wonder why. Is this because of a lack of resources? Is it because of the lack of *insights* we’re getting? If it is the lack of insights, the catch-22 here is that you’ll almost never get insights from the very basic metrics, especially if there is no segmentation (unless your site is down and your visits just flat-lined). That was something I also wondered about.
Anyway, I think the report shows both that we’re doing great stuff and there is also a long way to go. But we’re headed in the right direction. : )
So let’s get this party started! Starting August 12, on the 2nd Thursday of each month, we will be collecting data to start the analytics revolution in higher education. Karine’s group will then release the benchmarking data from the previous month at the end of month. For example, July’s benchmarking data will be released at the end of August and so forth.
To get a benchmarking report, all you need to do is participate in the benchmarking. Go to the HigherEdAnalytics website to join the revolution.
So, I’ve blabbed long enough. Go read the executive summary. I’d love to know your thoughts.]]>
Last week Karine Joly launched the “State of Higher Ed Online Analytics” survey to get a better idea of where we are as an industry with our use of web analytics.
Complete the survey today!
Enter your email address at the end of the survey to receive an executive summary in July highlighting the survey results.
Thanks to Karine for putting together the survey and starting the revolution!
Survey closes on May 24th.]]>
Obviously this won’t be a super popular game on TV – mostly very loyal fans and alumni, but that’s ok. It will still have some impact.
Here is what I plan on monitoring over the weekend to see how much of an impact the commercial (and even the game) had on traffic:
When looking at these reports, I have to remember that “like” date ranges matter. For instance, I won’t want to measure the difference in those metrics from yesterday to today because I know that our traffic goes down naturally on weekends (Friday and Saturday aren’t like days). This weekend should be compared to last weekend. Further, I need to keep in mind that the game itself (regardless of the commercial) will likely have an impact on traffic. Unfortunately we don’t have a “like” weekend to run it against (a weekend where our blue/white game aired on ESPN2 without the commercial). So, we’ll have to make due with just realizing that the commercial itself may not have caused the traffic.
I do think the metrics that have to do with branded keyword referrals and direct traffic can show impact from the commercial itself. Why? Because they have to do specifically with users seeking us out by our unit name (not just happening on our site or coming to our site from our main university site).
What do you think? What other metrics should I be looking at?]]>
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.
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:
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.
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*.
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.]]>
Take this example – let’s say you open up your analytics tool and see that, on average, users view 5 pages per visit. Ok, pretty good. You take note and move on. But if you used segmentation, you might see that the pages per visit is completely different depending on the type of user. Let’ say on average:
Of course these are made up numbers, but you get my point. Doesn’t this tell you much more? My site doesn’t seem to render well on mobile devices and campaign A needs a good look.
The fact that my site averages 5 pages per visit actually tells me absolutely nothing.
This is why segmentation is essential. Averages are misleading.
In Google Analytics you can segment users in different ways – the 2 main ways are using filters and advanced segments. Which one should you use?
When choosing between a filter and a segment, I usually go with this rule of thumb – if it’s a permanent segment, let’s say you *always* want to filter out internal traffic, then I’d create a filter. This is because filters segment out before the data gets into the reports. This also means that if, down the road, you no longer want to segment out internal traffic, although you can delete the filter, you can never get the data back.
For this reason, always keep one profile that has no filters.
If, on the other hand, you need more flexible segments – segments you want to turn off and on, then use the advanced segments feature.
For this post, I’m going to talk about using advanced segments, not filters.
The segments you use will depend on your goals. In the example above, if mobile visitors aren’t a priority right now you may not even look at that segment.
Almost all analytics tools offer segmentation now. Google Analytics released advanced segmentation about a year and a half ago. It’s amazing to me, though, how many Google Analytics users still don’t use this essential piece of functionality.
There are built-in advanced segments and also the ability to create custom segments. Google has both instructions and a video on creating your own (custom) advanced segments.
One of the most fundamental segments you will probably use is new and returning visitors. See how these 2 segments use your site differently. What’s the different in bounce rate, top landing page, referring keywords? For referring keywords, you might find that new visitors use more general keywords and returning visitors use more branded keywords.
Creating a segment for social media sites will help you answer the question on everyone’s mind nowadays – what is our social media ROI? Of course there is a lot more that goes into that, but this segment will help. Don’t forget to include all social media sites that your audience uses. This means going beyond Twitter and Facebook. Segment your social media traffic and see how they are behaving on our site. Are they converting? Are they doing something else? Of course this goes back to your social media goals. Is this segment doing what you want them to according to your social media goals?
This is another one of my favorite segments. First, how does your campaign traffic act differently than your organic traffic? What about pages per visit, internal site search keywords, time on site?
Next, to answer questions about how a specific campaign is performing, create a segment for traffic coming in only from that specific campaign. This will give you such great insights into campaigns – beyond just conversions and bounce rates. If you have a poorly performing campaign, use this method to dig deeper and find out why. High bounce rate or low conversion rate? How about looking at the internal site search keywords this segment uses to find out if it’s good or poor quality traffic?
Another use for looking at this segment is find out what else visitors from a specific campaign are doing on your site. It might surprise you to find out that a campaign for one area or program is actually bringing in traffic that is interested in another area or program. You also might find that traffic from this specific campaign are doing things other than the end-conversion. Maybe they are watching a specific video or visiting specific pages.
Depending on your site and its goal and intended audience, you may even want to filter out your internal traffic. If you don’t use a filter, though, consider at least building segments for both. This way you can easily see how these 2 segments act differently. Chances are these segments act *very* differently.
Segmenting on organic search traffic can bring valuable insights as well. Then you can drill into each search engine, see how much each is bringing. Further, drill into important keywords (within each search engine) and see the trend of traffic from those. Is it going up or down? Were you ranking for an important keyword, but now you’re not getting a lot of traffic from it?
Obviously there are many other segments you can use to help. These are just 5 that I’ve found very helpful. The important thing is to go back to your website goals and to use segments relevant to those. If your goals focus on geography (let’s say your continuing education or a community college) you may want to segment by state or region or city. Likewise if you have a goal of being more mobile-friendly next fiscal year, you can create the relevant segments to help you see how your mobile traffic is performing.
What other segments do you find helpful?]]>
A little background … Penn State is very large. We have about 43,000 students at main campus and about 78,000 students throughout all of our campuses. Needless to say we have a lot of websites – I can’t even guess how many we have – 200? 400? 500? Not sure. It’s a lot, though. That much I know.
With so many websites owned by so many different units, community is so important. We have a great web community with a fantastic annual web conference and yesterday we started a different kind of web community – one devoted to web analytics.
Since most units at Penn State use Google Analytics, it was called the Google Analytics User Group kickoff event. Going forward we’re probably going to call it something less tool-specific, but we’ll see.
Some great folks at the Penn State Libraries – including doteduguru Nikki Massaro Kauffman – put the event together. It was an awesome event and the attendee list was completely full only a few days after invitations went out.
We gathered in the morning at the libraries to kick off the event with an open panel – discussing how Google Analytics was being used at the university within different departments. It was so great to hear the different ways web analytics is having an impact at the university and how website owners are using it. Experience with the tool (and analytics in general) ran the gamut from just getting started to years of experience.
There were many sessions throughout the day including methodologies (which led into a discussion about privacy issues), Google Analytics implementation, new users, and reporting.
At the wrap up discussion we talked about how we will continue with the community. I’m very excited to continue the conversation within Penn State and we already have ideas of specific projects going forward. What a great way to end the week! I’m excited to continue the conversation.]]>