Practical Analytics by DigiKnow

Events - tracking in-page interactions

When a visitor comes to your site, data is collected in many ways: visitor data via the browser header and IP address, click data from pages loading and campaign data from URL parameters, but what about actions that happen within a page?  That is where “events” come in.  An event is an in-page action that normally would not be tracked by analytics, but through custom code, they can be captured, measured and analyzed.

Google Analytics events allow for in-page actions to be tracked and monitored without affecting the overall page view count of the website. Unlike page views, events can be organized by three groupings: category, action and label. For purposes here, the fourth variable, value, is not being used. Events called through JavaScript code and can be part of a link, or other page actions.

A full guide to Google Analytics Events implementation can be found at: http://code.google.com/apis/analytics/docs/tracking/eventTrackerGuide.html, but I wanted to run through some uses that I’ve found beneficial for our clients.

Content Rotators

More and more sites are including a large home page element that slides through images or news stories.  While traditional page view data can show you which image/link was the most popular, having this data nicely contained in an event is valuable, especially when you want to look at the data quickly.  Under traditional page view tracking, you would look at the traffic leaving the home page and see which pages were the most popular.  Generally you wouldn’t have any record of exactly which pages these were or which position they were in the rotator.  You could add in custom parameters like page.html?pos=1 for the first position in the rotator, but that makes your Content report messy with too many versions of a page.  This tracking could be done in Flash or in AJAX/HTML.

Video Playback

Another in-page interaction that is handy to track is video playback.  To keep from overwhelming the system with data, I’d recommend just tracking the video start and if the user reaches the end.  If you are using a standard player, you may or may not be able to add in that tracking, but some existing players (like Flowplayer) do allow for GA tracking.

AJAX content

Tracking usage of AJAX applications or interfaces fits ideally with events.  When interacting with an AJAX app, the page rarely refreshes, so traditional page view tracking doesn’t help.  Using events for adding/removing, filtering results, etc. is a great use of this tool.  In Google Analytics, there are some cases within AJAX where you want to “force” a page view to be logged, mostly for interactions that you want to be part of a Goal.

Filtered Search

A specific case of the AJAX-type tracking is a filtered search.  For search results that allow you to pick categories or ranges (say t-shirts under $20) and instantly update your keyword search results to those groups, using events to track the selection of “t-shirts” and “under $20” is very powerful.  This data then lets you see which criteria are the most popular and which drive sales or conversions.

Product Image Selection

For sites with a product catalog displaying multiple images per product, using event tracking can let you see which images are the most popular.  Knowing what is popular can inform your decisions on future photo selection or even content ordering.

 

Hopefully this has provided a nice overview of events.  In a future post, I’ll dive into the details of how to setup these events in Google Analytics.

Data Visualization Technologies

Last week I was part of the first annual Data Driven Conference put on by Community Research Partners.  The conference focused on helping non-profits and other groups use data more effectively in decision making and presentation.  I spoke on a panel with one of the CRP staff and a cartography professor from OSU and our presentation focused on the technology of data visualization.

The full day conference was attended by over 200 local and regional data “geeks” and panelists presented on data collection, data visualization and using data effectively.  The full presentations are available on the CRP website.

Below is a run down of the technologies I covered as well as the full presentation.

 

Stream Graph

 

Microsoft Pivot

Network Graphs

Google Fusion Tables

Google Analytics

Data Visualization Technology

View more presentations from Scott Chapin.

Google Analytics: Accounts, Domains, and Profiles (oh my)

Over the past week, I’ve had several clients ask me about the best way to configure their overall Google Analytics setup to cover multiple sites and domains.  I wanted to take a few minutes today to outline the options, what factors determine their use and how to setup the right solution.

Within Google Analytics there are three tiers for grouping your data: Accounts, Domains and Profiles.

Accounts – analytics accounts are the top-level container for all other Google Analytics data.  For the most part, unless you are an agency, like DigiKnow, you will only have access to one account.  Account = Company.

Domains – a domain is generally an individual website and more specifically is a unique Google Analytics tracking code.  For the most part every domain or sub-domain your company has, will be its own domain in Google Analytics. Domain = Website.

Profiles – a profile is an individual view on a domain’s data.  This one is a little more tricky.  For the most part, if you don’t have someone on staff or your agency that is intimately knowledgeable about Google Analytics, you will just have one profile that has all your data.  There are nearly infinite ways to create views on your data and I’ll cover a few of them in a minute.

One of the big challenges when you have multiple websites is whether it is better to setup multiple domains or multiple profiles within a single domain.  Generally, I recommend creating a unique GA domain for each website you have.  The one exception (which I ran into this week) was where it was technically difficult to use a different tracking code on each sub-domain.  My client didn’t have an easy way to put a unique code in each footer, so having a single domain split into profiles for each sub-domain was the better solution.

So how do you use profiles effectively?  There are some best practices, but ultimately it comes down to business needs.  For everyone, I recommend two profiles be created right from the start.  The first is an unfiltered profile that has all the collected data and the second is a profile that has your own traffic removed via IP filtering.  This lets you separate out what you are doing from what “real” customers are doing, but still gives you access to everything should you need it.  From there, the possibilities are nearly endless, but let me give some examples of what we’ve done.

• For a local public institution, we setup profiles with and without visitors on the local wifi and publicly available computers.  This let us see how visitors used the website differently in the building, the workplace or at home.
• For a national CPG company, we setup a profile to treat a section of the site as it’s own website.  This let us see the interactions with the site in a narrow view. Data like page views, bounce rate and referring sites give a greater accuracy than with an advanced segment.
• For a state university, profiles were setup for each of the individual colleges to allow access only to data for their area of the site. 

Profiles and filters can create a near infinite views on your site data.  Hopefully this serves as a good primer for the setup of a set of domains and profiles within Google Analytics.  I’ll continue to expand on this in the future.

Analytics for iPhone Apps

Usually when I talk about analytics, we are focused on websites or at least apps that live within a browser.  Today, I want to look at analytics within mobile apps, specifically for iPhone apps.  Although we’ve build iPhone apps before, we didn’t care much about analyzing usage previously, but today we launched a new app, the Latin Recipe Shaker, and because type of app it is and the scope of the advertising around it, we wanted to make sure everything we did was measurable.

The first challenge we faced was the decision on which analytics package to use.  We ended up focusing on two main solutions: Flurry and Google Analytics.  In looking at both tools from an analysts perspective, I didn’t see much difference in the reporting provided.  The way the user’s location is tracked seems to be a bit more robust in Flurry, but since the app isn’t mobile aware, that was not a factor for us.

The second question was about implementation.  After having my developers look at both systems, they said the implementation was nearly identical, so there wasn’t any preference on their side.  Both systems use an asynchronous processing of data so that every click and swipe isn’t pushed up in real time.  Flurry uploads the data from the previous visit when the app starts.  Google Analytics lets you set a delay, which defaults to 10 seconds, and then it uploads the data on that interval.  Although it uses more pushes, I prefer the Google Analytics route.

Since all things were equal, and since DigiKnow is a Google Analytics Authorized Consultant, we decided to go with Goggle Analytics.

The implementation basically allows for two types of tracking: page views and events.  From what I know, you can’t do custom variables yet.  We setup page views for each of the main screens and did events for the settings changes, adding/removing on the favorites page and the keyword recipe search. 

The only major hiccup that we’ve seen so far is that the Visits data doesn’t seem to be quite right.  Somehow a single real-life visit shows up as multiple visits in Google Analytics.  This may be related to the asynchronous processing.  The other data point that isn’t quite right is geography.  Since we didn’t want to use the iPhone GPS, our app just uses IP for location, and every iPhone that is on 3G or Edge looks like it is in Dallas (the home of AT&T). 

Overall, the process of implementing analytics was fairly simple and the data coming in is very valuable.

Analytics @ SXSW

I was fortunate to be able to spend four days last weekend down in Austin, TX at South By Southwest’s (SXSW) Interactive Conference.  In addition to meeting a ton of great people from around the world, there were amazing sessions on every topic under the sun, from user experience to mobile, from copyright to managing Gen Y.  One thing that was obviously missing from the conference though was a discussion on analytics.  Of the several hundred panels within the interactive schedule, there was just one panel on analytics.  Today, now that I’m recovered and back in the swing of things, I checked the panel picker, where interactive fans and attendees can vote on which topics will be presented at the conference, and of the 2400 submitted ideas, only 20 had anything to do with analytics (some very loosely).  Analytics is clearly underrepresented.  Next year, I think I’ll need to do my part to try and fix that!

Just One Panel?

The one panel that  made it onto the conference was “What Are Analytics? A Guide To Practical Data”, which had a slightly misleading title, since it was 100% about social media analytics. Margaret Francis and Blake Robinson lead a packed room of over 200 attendees and did a nice overview of the top things to be measuring within social media campaigns.  Some of their key points included a focus on social media sentiment, comparing your brand/company to the competition and considering the tangential relationships that can be found by going down the rabbit hole of tag clouds and keyword usage.  The panelists admitted that we are very much in the infancy of social media measurement.  While tools like PostRank.com, TwitterCounter.com and Nielsen's BlogPulse are useful, they are not at all equivalent to the tools that we have to measure website interactions today.  Geographic and demographic profiles on social media are barely directional at this point, but everything will continue to evolve.  Their key takeaways were: make data visible, set goals and metrics, track performance over time.

Mobile Tracking

As part of the conference, DigiKnow had the opportunity to work on a mobile project with one of the event sponsors (who I can’t mention by name).  I wanted to share a little of this experience, since it was our most focused effort in measuring mobile.  For this site, we initially setup Google Analytics with image based mobile tracking, figuring that would cover both smart phones that could run the JavaScript tracking and feature phones that might not be able to.  On Sunday (half way through the conference), our data seemed low and we wondered if there was a problem, so we added in a second profile running the JavaScript tracking.  As it turned out, the image tracking was only getting about half of the data that the JavaScript tracking revealed.  Today, well after the fact, I also ran the site’s logs through a log-based tool and it came back with data that was very similar to the JavaScript tracking.  Given the fact that the typical SXSW crowd are smartphone users, this wasn’t too surprising.

Key takeaways:

• For mobile, if you can use multiple collection methods (image, JavaScript, logs) you will get the most complete picture, but if you can only do one, I recommend sticking with standard JavaScript tracking

• Any time you are using a new tool or data collection method, test it thoroughly before launching it.  We needed more test data to reveal the deficiencies in the image tracking for mobile in our project.

Questions from the field

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 analytics@digiknow.com 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.

DigiKnow is now a Google Analytics Authorized Consultant

 

The main purpose of this blog is to provide training, advice, tips and examples to help you, our readers, with the every day analytics challenges.  Today, I must take a little diversion from that to announce that DigiKnow has been added to Google’s Authorized Consultant list. 

We are very happy to be added to this group of top-tier consultants and are excited to see how we can use this new status to further help our customers and provide input on the Google Analytics system. 

As you’ve probably gathered from examples in our blog posts, we are strong advocates of Google Analytics, both from it’s cost perspective (it’s free!) and what it can do.

DigiKnow’s analytics team guides clients through the code implementation, configuration, reporting and analysis within Google Analytics.  With our new status, we will continue to offer all of these services, and with access to a new community of experts, our own knowledge will continue to grow.

If you have any analytics needs, please contact us at analytics@digiknow.com or call us at 216-325-1900.

 

 

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Custom Variables – Tracking more than just visits

Analytics has come a long way over the past ten years.  We started with simple hits and visits from daily log analysis, then moved on to real-time tagging and more “modern” tracking methods.  Along the way we’ve gained the ability to segment, test various ideas, and filter out the noise from our website data.  Today, we have the ability to add data to website visitor activities to gain another level of insight.

When web analysis started, the only data we had access to was that coming from the web browser stored in server log files.  This included the pages/files requested, computer information and visitor IP address.  Even if the visitor had logged in and we had a complete profile on them, we couldn’t do anything with that data.  Today, thankfully, we can insert these additional data points, such as demographics (age, gender, etc.), preferences (heavy user, light user) or even memberships levels (non-member, basic, premium), into the data stream and use this data in our analysis.

Each analytics package has it’s own way of handling custom variables, both in the number allowed and the code-level configuration.  Below is a video from Google about how to handle this in Google Analytics (fast forward to 11:21 in the video – audio quality is not great).

One custom variable implementation that DigiKnow did that I think is a little outside the box, is using the page-level variable to track 404 (page not found) errors on a website.  This may be a little more technical than I usually get here, but to explain what we did, the line of code in red was added to the 404.html page for the website.  The parameters on the “setCustomVar” call are indicating: slot/key #1, Category of variable, Value for category and that the variable is page-level (3).

<script type="text/javascript">
var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www.");
document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E"));
</script>
<script type="text/javascript">
try {
var pageTracker = _gat._getTracker("UA-XXXXXXXX-1");
pageTracker._setCustomVar(1, "Error", "404", 3);
pageTracker._trackPageview();
} catch(err) {}</script>

What results from this is new content in the Custom Variables section (under Visitors)

More importantly, now I can setup custom report “Pages with 404 Errors” and a segment “Visits with 404 Errors” and see how and why visitors are getting to pages that don’t exist. 

If the technical details here are daunting, don’t worry, there are experts to help with that.  Just knowing that you can bring customer profiles or other custom variables (like the 404 example) into your analytics data should get your mind going about what else you might want to track.

Back to Basics: Segmentation

Although I've made several posts about segmentation, I thought it would be good to take a step back and talk about what segmentation is and why it is one of the most powerful tools in the analytics tool kit.  Segmentation is the ability to look at just a portion of your traffic, based on some criterion such as geography, site action, time of day, or some other custom variable you set.  The power from segmentation (also known as filtering) is by comparing the patterns of one segment of your users versus the activities of another.  Separating out these groups helps you see how each is performing and try to isolate opportunities.

Let me provide a concrete example.  For a client, we are running a banner and paid search campaign to drive traffic to the site.  The campaign traffic currently accounts for less than 5% of the total site traffic, so there aren’t any dramatic changes in the overall site traffic usage patterns.  Knowing that, I wanted to see if despite being low volume, whether the campaign driving high value.  By creating two segments, one for banners and one for paid search, I could see not only the basics of page views, conversion, etc. (available elsewhere in Google Analytics, in this case) but I could also see what specific pages received the most traffic, the city/state where visitors were coming from, and the browser/OS used to access the site.  With this information, I got an idea of the type of visitors coming in from each of the campaigns and can put more money in the right place next month. 

Segments are powerful.  Segments let you slice your site data down to a single user and see what he/she did.  Scary powerful.  But how often do you really need or want to do that.  If you use Google Analytics, within Advanced Segmentation (their name for this), there are several predefined segments: New/Returning Visitors, Paid/Non-Paid Search, Search/Referral/Direct Traffic, Visits with Conversions, Visits from iPhones, Non-Bounce Visits.  These are all great and if you do nothing beyond using these, you are already ahead of many people. 

But of course, I would love everyone to take this further. Here are some other general segments that I would suggest (with links to Google Analytics examples):

• Completed a specific site goal – if you have goals setup in GA (I hope you do!) then you can use the goal as a segmentation point.  If you aren’t using goals, visitors that reach a specific page can be considered a goal.
  • Example: Complete Goal #1 segment
• Mobile visits – For the purposes of segmentation, I consider anything smaller than 640x480 to be mobile.
  • Example: Mobile segment
• Campaign visits (general or specific campaign) – this can be setup using a campaign name, campaign medium or many other dimensions.
  • Example: All campaign traffic segment; Banner campaign traffic segment
• Visits from or excluding a specific geography – this could be visitors specifically in the US or only international visits; maybe visitors from Ohio vs. visitors from Pennsylvania
  • Example: US visits segment; non-US visits segment
• Visits within a time period – knowing what visitors are doing within or outside of business hours can be powerful, especially if your site has a clear pattern of more weekday traffic than weekend.
  • Example: Visits outside of business hours (all days)
• Internal website visits – it is sometimes helpful to know what your own company is doing on your website.  The easiest way to track this is by Service Provider in Google Analytics if your company shows as its own service provider.  If not, this is tricky, but can be done through custom code.
  • Example: DigiKnow internal traffic segment (this is only useful as a model/pattern)

The easiest way to learn how to use segments it to dive right in.  If you use Google Analytics, the examples above will get you started.  Any time you dig into analytics, the best approach is to have a goal in mind.  If you don’t know what you are trying to find out, you can spend a lot of time exploring, but not learn much and won’t find anything actionable.  Rather than try to explain any more here, I wanted to link you to a great video that explains Google Analytics Advanced Segments.

Related Posts About Segmentation 

• Profile-specific Segmentation

• Managing Google Analytics Segments

• Measuring Mobile

Book Review: Web Analytics 2.0

Although it is only January 6th, I can say with a pretty high degree of confidence, that the analytics book of the year is Avinash Kaushik’s new book Web Analytics 2.0.  As a follow up to his first book, Web Analytics: An Hour A Day, I think Avinash out did himself with this book.  If you don’t know of Avinash Kaushik, among other things, he’s Google’s Analytics Evangelist and his blog, Occam’s Razor, is a great resource for anyone interested in analytics.

Web Analytics 2.0 is a nice balance of theory and practical tips, but more importantly, provides guidance for readers with a wide range of skill and experience.  Over the 14 chapters of the book, Kaushik covers almost every aspect of web analytics, from competitive analytics to optimization, even guides for picking a solution vendor and starting a career in the industry. 

By far my favorite part of the book was Chapter 3 (“The Awesome World of Clickstream Analysis: Metrics”).  This one chapter should used as a primer for anyone thinking about buying, selling, or using web analytics.  In the chapter Avinash covers all the basics including his eight standard metrics (visits, visitors, time on page, time on site, bounce rate, exit rate, conversion rate, and engagement) and also delves into how to define a great metric.  If every website owner, marketer and developer read this, the world might just be a better place. [Note to Avinash/Wiley Publishing: release Chapter 3 as a inexpensive white paper/eBook]

Overall the book is an easy read for anyone interested.  Avinash’s causal writing style and frequent examples makes the text engaging and entertaining.  For the most part, you can dive into any area of the book without missing too much context from the rest of the book.  Of course I recommend reading the whole thing, but even if you just read one chapter, say on social measurement, you’ll still get a lot out of it.

Included with the book is a CD-ROM containing presentations, audio/video, white papers and what maybe the best Easter-egg on the disk, an Excel spreadsheet with the 70 URLs reference in the book.  Although the book has handy short URLs for every site referenced (like http://sn.im/gifors) having them all in one place is extremely useful.  I’m tempted to publish them here, but that might violate copyright and I don’t want the folks at Wiley hunting me down.

If you are reading this blog, I 100% recommend buying this book.  On top of being a well-written resource, all of the profits from this book (and his previous one) go to two charities: The Smile Train and The Ekal Vidyalaya Foundation.  Get the book and start the new year off right with a quick education on the in’s and out’s of web analytics.