Event Tracking Pt. 3: Reporting & Analysis

October 16, 2007 by Justin Cutroni

This is part three of a three part series about event tracking in Google Analytics. You can find more information in these other posts:

Event Tracking Pt. 1: Overview & Data Model
Event Tracking Pt. 2: Implementation

I wrote this post before having access to the new events reports. As a result, I don’t have any screen shots for you guys! Sorry I don’t have anything to show you guys, but I will try to explain as much as possible.

The new event reports section will have specific reports for objects, actions and labels. If you have not read my post on the event data model then I strongly suggest that you do so. The data model is the driving force behind the structure of the reports.

Event reports will let you see summary information at an Object level, action level and value level. Here’s an example using Google Maps. If we create a ‘Map’ object then we will get a unique count of how many times the map object has been created. We can then drill into that event and see the actions and values of those actions.


Map
|_ Click Map View
|_ Satellite
|_ Hybrid
|_ Map
|_ Traffic
|_ Street view

This drill-down functionality is very similar to the drill down that you can do with campaign tracking.

The interface will also show you the values of each label. Remember, Satellite, Hybrid, Map, Traffic and Street view, are all labels and they have an associated value. GA will total up the value and show a unique count of how many times each action happened, the total value and the average value.

This is fantastic information because we can now measure what people are doing on our media rich pages. We no longer need to rely on Average Time on Page to gauge the success of our content.

I know this is really high level, and I’m sorry I don’t have any screen shots, but I’ll try to get some soon. Hopefully you got a taste of what is possible with the event reporting.

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GA On Site Search Pt. 2: Reporting & Usage

October 16, 2007 by Justin Cutroni

Onsite Search Reports MenuThis is part two in a two part series about the new GA on site search reports. In part one I discussed the setup.

There is a tremendous amount of information in the new Site Serch reports about ow your visitors interact with your search engine. But it goes way beyond measuring how many searches occurred for a keyword. The new reports actually tie outcomes to searches providing insight into what product sand content your site visitors are looking for. But enough babble, on to the screen shots.

What Reports are Available

First and foremost there is the Usage report. This reports helps you understand if visitors are using site search. If you’re trying to push site search as a navigational tool for your visitors, and only 2% of site traffic is using site search, then your navigation may not be working. You may want to change the visibility of the search box.

Google Analytics On Site Search Usage Report

The Usage report is pretty good, but everyone wants to know what people are searching for. The Search Terms report contains all of the search terms that visitors entered into your on site search. One thing you should know is that GA is reporting UNIQUE searches. This means that duplicate searches, made in the same visit, are excluded. So it is not a count of how many times a term was search for, but rather how many VISITS contained a search for a specific term.

Google Analytics Search Terms Report

Take a look at the narrative at the top of the report. It contains a number of new metrics to evaluate on site search. Total Unique Searches indicates what search terms people are entering. % Search Exits indicates what percentage of visitors are leaving immediately from the search results page. This could indicate that visitors are unhappy with the search results. Time after Search and Search Depth indicate the engagement of the visitor after using search.

Google Analytics does not normalize the search terms. This means that misspellings and similar searches are not grouped together. So the search terms ‘red sox’, ‘red socks’ and ‘Red Sox’ would appear as individual line items. You’ll need to review your data and manually normailze it using filters.

Another really cool report is the Site Search Start page. This report shows where your site searches originated. It identifies the page that the visitor was on when the seach occured. This can help identify issues with navigation or the data architecture.

GA Search Start Pages

Now we know where people started their searches, but what about where they end up? The Search Destination Report shows which pages people navigate to directly from the search results page.

GA Site Search result pages

What’s really cool is if we click on a destination page that is listed in the above report we can see all the search terms that drove people to the page. Here’s what happens if I click on one of the results in the previous report:

GA search reports page terms
There are other reports that do a good job of illustrating how visitors use site search. If you select a search term in any reports you can do a really deep analysis using the analyze drop down. One option is the Search Navigation report. This report shows where someone started their search and where they went after the search.

GA Search Nav report

What about peple who search multiple times? Another analysis option is to use the Search refinement report. This report shows how people refine their search terms during their visit. So, in the image below, visitors began by searching for dog. Then they refined their search using one of the terms in the report.

GA Search refinement Report

Ok, one more thing about the Search Reports. Notice that the standard Goal Conversion and Ecommerce tabs exist on most reports. These tabs provide information about which searches lead to conversions and, if you’re an e-commerce site, the revenue that each generated. Pretty darn cool is you ask me.

GA Search Reports Conversion

What’s Missing

The one report that is missing from Google Analytics is the ‘0 Result’ searches. It’s really important to know what on site searches are producing 0 results. This is an indicator of missing site content. You can artificially create this data by creating an event or a pageview (I suggest event) in Google Analytics. I’ll write more about how to do this in another post. But be aware that the new reports to not contain this data.

In Summary

There is a ton that you can do with the new on site search reports. Not only can you analyze what people are looking for and optimize your content, but you can also identify how visitors integrate search into their visit.

Have fun with these reports!

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New GA Feature: Segmented Widgets

June 3, 2007 by Justin Cutroni

I’m on a roll with GA Desktop Widgets :) Another really cool feature of the new GA dashboard is Segmented Widgets. This is very similar to the Sticky Filters feature that I wrote about last week.

With segmented Widgets you can add a segmented report to your dashboard. Here’s an example. I can choose a keyword from the keyword report, segment it by City, and then have the resultant data placed on my dashboard.

First, choose a keyword from the keyword report:

Keyword Report

Then, segment the data for that keyword:

Segment the keyword

Finally, after GA has segmented the data, add the report to your dashboard:

Segmeted Widget

This feature will work for any report that you segment. I really like this feature, especially for AdWords analysis. I can segment the AdWords Analysis report by geographic area and place the result on the dashboard. Then I have instant data on how my geo-targeted AdWords campaign is performing.

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Changed GA Feature: Report Organization

May 8, 2007 by Justin Cutroni

20050507_dashboard_nav.pngThe GA team has completely reformatted and re-organized the reports. A couple of reports have changed dramatically and, I’m sorry to say, one report did not make the cut and has been removed from the product. A moment of silence please…

Quoting the GA help center…


Changed & Removed Reports

All Navigation Report

To find what pages visitors came from and left to for each individual page for your website, you no longer need to hunt for the page you want in the All Navigation Report. Instead, you can go to the Navigation Summary in the Content Overview report. From here you can either click the link for the page you want in any of the tables, or select the page you want from the drop-down menu under the report summary.

All CPC Report

Information provided by the All CPC Report in the previous interface can now be found in the Keywords and AdWords reports under the Traffic Sources group. You’ll be able to intuitively drill in to those reports to find the information you need.

Keywords Considerations

Though the Keyword Considerations report was potentially very useful, it didn’t meet our demands for reliability and also was not one of the widely used reports. This may change.

If you’re having trouble finding a report in the new GA try using the Report Finder tool.

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New GA Feature: PDF Export and Vector Graphics

May 8, 2007 by Justin Cutroni

20070507_export_options.pngYou can now export Google Analytics reports in PDF format. The export button has been moved from the top right of each report to directly under the report name. When you click on the export button you get four choices, PDF, XML, CSV (comma separated value) or TSV (tab separated file).

Something I’m very excited about is the way that the new PDF creates images. The graphs and charts in the PDF export are Vector Graphics. Not many people are going to write about this change, but I think it’s pretty cool… This means that you can scale the graphics in the PDF incredibly large and they will not become distorted. This will make it easier to use them in Keynote presentations.

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New GA Feature: Custom Dashboards

May 8, 2007 by Justin Cutroni

Add To Dashboard At the top of each report you’ll see an ‘Add to Dashboard’ button. Simply click the button and GA will add the report to your dashboard. I’m not sure if there is a limit to the number of reports that can be added to the dashboard, but I would not add more than 6 or 7. Remember, the primary purpose of the dashboard is to provide a high level view of your website’s health. If you see something unusual in your dashboard then you should investigate further.

Customizing Dashboard Layout

The dashboard is not static, meaning you can re-arrange the layout of the reports. Simply drag and drop the reports on your dashboard as you would a widget on your Google Homepage (aka iGoogle). Just place the mouse pointer over the report header and it will turn into a hand. Then click and drag!

Dashboards: A Recommendation

When using your dashboard I recommend comparing two date ranges. The reason is that looking at the data, without any historical context, is useless. For example, if your dashboard reports that your conversion rate was 5% for the last week what would you think? You wouldn’t know what to think because you don’t know if 5% is good or bad. Adding a date comparison shows how the data has changed over time. The only downside of date comparison and dashboards is that Google Analytics will not remember if you applied a date comparison to the dashboard This means that every time you log in you’ll need to reapply the dates you want to compare.

Automated Dashboard Delivery

Another cool feature of the new Custom Dashboards is the ability to have your dashboard EMAILED to you at a regularly scheduled interval (you can read more about the emailed reports, and how to set them up, here). You no longer need to log into GA to check your stats. Just have your dashboard emailed to you every morning. And, when you schedule your emailed dashboard, you can specify that Google Analytics includes a date comparison. So, if you receive your dashboard weekly Google Analytics will compare the last two weeks of data.

Creating Your Own Dashboard: Where to Start

Start by identifying the KPIs vital to your business. Then, find the reports that contain those KPIs and add them to the dashboard. If you’re still having trouble go out and get a copy of Eric Peterson’s book, The Big Book of Key Performance Indicators. Chapter four lists the best KPIs for the four main business models:

If people still need some help I’ll post some dashboard suggestions later. Just leave a comment if you’re interested.

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New GA Feature: Date Range Selector

May 8, 2007 by Justin Cutroni

20070504_date_range_selector.pngAs soon as you log into Google Analytics you’ll notice that the old date range selector is gone. The new date range selector is located at the top right corner of every page. If you click on the dates a large box will appear with lots of options.

20070507_date_range.png

There are two ways to specify a date range in the new GA. You can use the ‘traditional’ calendar method and choose days, weeks or months. Or you can use a new timeline selection tool.

The timeline selection tool is accessed via the timeline tab in the date range selector. The timeline selection method displays a graph of your visitation data over time. This small graph makes it easy to identify spikes in your traffic. If you see something that piques your interest you can drag the date range slider to cover the spike in traffic.

20070507_timeline.png

You can use the ‘handles’ on each side of the timeline slider to expand or contract the time segment.

The date range selector still let’s you compare two date ranges. Just click the compare to past checkbox and you can enter two date ranges using the timeline view or the calendar view.

20070507_compare_to_past.png

You’re probably wondering why the timeline selector was added to Google Analytics. It’s all part of the new paradigm. The new GA interface is meant to facilitate analysis. The timeline selection tool makes it easier to identify time segments that may need more analysis. No more specifying a date range in a calendar and then checking the reports to see what happened. You can now identify major traffic changes right from the date selection tool and drill down on that time segment.

The timeline selection tool looks like the date range selection tool in Google Finance, huh? ;)

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Google Analytics Cross Segmentation: Something You Should Know

March 6, 2007 by Justin Cutroni

Google Analytics has a great feature called Cross Segmentation. Using this feature you can ‘drill down’ into your data to gain more insight. However, there is one thing that all GA users should know. Sometimes cross segmenting data does not produce the desired result.

Example

I’m a big fan of bounce rate. I think it’s a vital metric that explains a lot about the online sales channel. One things I like to do is measure the bounce rate for marketing campaigns. Using bounce rate I can tell if the marketing message that drove a visitor to the site matches the message shown to the visitor when they land on the site. Nothing revolutionary here…

I start with the Content Optimization > Navigational Analysis > Entrance Bounce Rate report. Here it is in all it’s glory:

20070307-entrance-bounce-rate.jpg

To get the data I want, the bounce rate for a specific page coming from a specific source, I need to cross segment the above report. Usually there is a special landing page (or multiple landing pages) for the campaign, but in this case we’ll look at /blog/index.php. Cross segmenting row 1 in the above report yields:

Boune Rate: Cross Segmented

See how the report columns have changed? We can no longer see the bounce rate. We only see the visits, pageviews, conversion rate and revenue per visit. Honestly, I don’t need that data, I really need to know the bounce rate for each source. Unfortunately I can’t get that data using the cross segmentation feature.

Another Example

Here’s the Marketing Optimization > Visitor Segment Performance > Referring Source report.

20070307-ref-source-cross-segmented.jpg
** Please Note: I initially posed the wrong image above. The image should contain referrals from Web Analytics Demystified. If it shows data from StumbleUpon then you may be viewing a cached image. Sorry. Now, back to our story. **
Thanks Eric for all the traffic :) Let’s cross segment row #1 by ‘Content’ and see what happens:

Referring Source: Cross Segmented

You may think that we’re segmenting by the content on my site, but we’re not. This isn’t the same content from the ‘Top Content’ report. What we see here are the pages on Eric’s site where people clicked on links to my site. How can I be sure? All the pages on my site start with ‘/blog/’.

So why is this happening? It’s just the way that GA is storing data. It’s not a bug, it’s just the way that GA works. Don’t worry, there is a work around :)

The Solution

The solution comes down to two things: planning and knowledge. Know the exact metrics you need for your analysis and make sure Google Analytics can deliver them. If you can not cross segment a report to produce the desired data, then try creating an additional profile (using filters).

Here’s how I get around the bounce rate issue above. I use a filtered profile to generate the bounce rate. I create a new profile and apply an include filter based on the campaign, medium or source, that I want to analyze. When the filter is applied to the profile then all the reports in that profile will be specific to the campaign, medium or source, specified in the filter. Obviously this is practically impossible if you are doing an analysis on the fly, or if you need to filter on a piece of data that is unknown when you set up GA.

As a rule, I always create specific profiles for major marketing campaigns. Here’s an example of the filter I might use:

Campaign Name Filter

The above filter only includes data coming from a single campaign named ‘Important-Campaign’. That means that the the data in the Entrance Bounce Rates report is only for the ‘Important-Campaign’. I’m essentially cross segmenting when Google Analytics processes the profile data.

Conclusion

I truly believe that GA can provide most of the metrics you will need for a thorough analysis. However, you must plan ahead. As the above example shows there are some anomolies, but they can be mitigated with a logical plan for analysis.

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An Analysis of My Data

March 5, 2007 by Justin Cutroni

I’ll be honest, it has been a long time since I looked at the analytics for this blog, I just haven’t had any time. Work has been flat-out busy since January 1. That’s also the reason that my posting has been very light.

Anyway, I logged into GA today to check some stats and was surprised by some of the data. I thought you all might be interested in the basic process I used to figure out what happened.

The Big Picture: What’s happened Since January 1

Since I haven’t reviewed the traffic data for a couple of months I started by adjusting the date filter to cover January 1 to March 4. Here’s the Executive Overview report:

Analytics Talk Traffic

Notice anything? What the heck happened around February 1st? Obviously some website drove a ton of traffic to the blog. Was it Digg?

Drilling Down: When did it Happen?

Before I get ahead of myself and try to figure out where the traffic came from, let’s determine exactly when it happened. I’m going to use the date filter to drill down and isolate when the date happened. If I hover my pointer over the data above GA shows that the spike occurred on February 1.

If I adjust the date filter one more time to cover February 1 we can see there was a big jump in traffic at 8 AM:

picture-3.jpg

Now we’re getting somewhere. Now that I know when the spike happened I can start to figure out where the traffic came form?

Getting Closer: Where did they come from?

I’m trying to figure out where the traffic came from, so I’m going to look at referral information using the Marketing Optimization > Visitor Segment Performance > Referring Source report. This report segments the site traffic based on where it originated. When we look at this report we should immediately know where the traffic came from:

picture-4.jpg

A ha! I was Stumble-Uponed! For those of you unfamiliar with StumbleUpon, here’s a description from their website:

People-Driven Technology:
Using a combination of human opinions and machine learning to immediately deliver relevant content, StumbleUpon presents only web sites which have been suggested by other like-minded Stumblers. Each time the ‘Stumble’ button is clicked, the user is presented with a high quality web site based on the collective opinions of other like-minded web surfers.

Based on this description I’m going to say that StumbleUpon drives qualified traffic to my site. We like qualified traffic, it usually converts better :)

Is there anything else to know?

What else can I learn about these people? First, they didn’t convert on any of my GA goals! I can see this in the above report. There is a 0% conversion rate for Goal 1 and Goal 2. This means that they didn’t do what I wanted them to do. (The goals for my website are to sign up for the RSS feed and to use the contact form). What’s disturbing is that this was qualified traffic! Is this indicative of a problem with my website? Why didn’t these people convert?

I’m going to dig deeper by cross segmenting the data in the Referring Source report. I’m trying to find out more about these people that came to my site from StumbleUpon. Did they come to the site before (i.e. how many return visits?):

picture-5.jpg

Not too many return visits, almost all new visits. So I now know that StumbleUpon drove 136 new visits to my blog in about one hour and none of them converted. Wow, that’s a complete bummer.

Summing Up

I could dig a bit deeper, and look at how the visitors from StumbleUpon navigated the site. But, with an average number of pageviews below 2 I’m not going to discover too much. There are some lessons to be learned here:

First, I should have been paying closer attention to my reports. I probably don’t need to review them daily, but I should review them once a week. Also, I’m going to dig a bit deeper into my new visitor segment. I really think I may have a problem converting new visitors. But that’s another post for a later time :)

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Google Analytics Campaign Tracking Pt. 3: Reports and Analysis

March 4, 2007 by Justin Cutroni

In Part 1 of this series I explained link tagging, the technology that Google Analytics uses to track on-line marketing campaigns. In Part 2 I discussed how to tag your links and posted a tool that I use to quickly tag large numbers of marketing URLs. Today, in Part 3, I’ll start to pull this whole thing together by walking through a very basic analysis.

How I Start

I like to start my marketing analysis using the Marketing Campaign Results reports. Using these reports I can immediately identify any campaigns that are under or over performing. They’re a great launching pad for further analysis. You can find them in the Marketing Optimization > Marketing Campaign Results section.

The reports segment the data based on where the visitor came from using the values from the campaign tracking variables. So, for each of the major campaign variables we discussed in Part 1 (utm_campaign, utm_source and utm_medium) we have a corresponding Google Analytics report. Here’s the mapping of campaign variable to GA report:

Campaign Variable Report Name
utm_campaign Campaign Conversion
utm_source Source Conversion
utm_medium Medium Conversion

This means that the values you used in the campaign variables will be pulled directly into the reports. Exciting stuff, huh? :)

Campaign Conversion Report

Let’s start with the Campaign Conversion report.

Campaign Conversion Report
This report segments the traffic based on campaign name. It contains information from tagged URLs (using the utm_campaign variable) and un-tagged URLs. How does it get data for the un-tagged URLs? If you’re using the auto-tagging feature in AdWords then Google Analytics will automatically pull in the Campaign names you create in AdWords. All other un-tagged URLs get put into the following buckets:

  1. (direct): visitors that entered your website address directly into the browser
  2. (organic): visitors from an un-paid search engine listing
  3. (referral) : visitors that clicked on an un-tagged link
  4. (not set) visitors from links that were tagged but were missing some information. For example, if you are looking at the Campaign Conversion report, and see that there were 10 visits from ‘(not set)’ this means that the utm_campaign variable was missing from the tagged link.

Ok, so what does this report tell us? It helps us quickly understand how well our campaign is performing using some basic metrics:

  • Visits: How much interest did the campaign generate?
  • Goal Conversion Rate (G1/Visits): Did the visitors from this campaign do what we wanted them to do?
  • Transaction Average (T/Visits): How many transactions were generated by this campaign?
  • Revenue per Visit ($/Visits): How much money did we make from each visit in the campaign?

It’s important to realize that each metric gives you a bit more insight into what is going on. For example, let’s say a campaign has a very low conversion rate. Why? Look at the number of visits. Is the campaign generating a lot of traffic? If there are a high number of visits but a low conversion rate there may be a disconnect between the marketing message you’re sending and the content the visitor sees when they land on the site. Dig deeper, Try checking the bounce rate for the landing page.

Again, this is a good starting point for a deeper analysis. And analysis means segmenting the data to gain more insight.

Segmenting Campaign Data

Notice that the first column in the report is named ‘Campaign/Source’ and not just ‘Campaign’? The reason is that this report let’s us drill down into our campaign and view the sources associated with the campaign. If we click on the plus sign for the ‘Ongoing’ campaign we can drill into the data and see the associated sources.

Campaign Source Report

This tells us is there were three sources of traffic in the ‘Ongoing’ campaign: Squidoo, Wikipedia and MySpace. This is real data from a company using social networking and viral sites to drive traffic. The value in the brackets (Social_Networking and viral) is the medium (which we’ll get to later).

Remember from Part 1 of this series, the source is the ‘who’ part of the campaign. ‘Who’ did we partner with to distribute our message? By drilling down into the data we can find out. Drilling from the campaign level to the source level revealed a lot about our campaign. It looks like this business should dump MySpace and focus more on Squidoo! Not only do they get more traffic, they get far better conversion.

But let’s take a different look. It could be that these sources are used in multiple campaigns. Maybe MySpace did really well in a different campaign. Let’s use the Source Conversion Report to get a different view.

Source Conversion Report

Source Conversion
The source report shows us how all of our various sources are doing. It does not matter which campaign the source belongs to, they are all listed in this report. This report is very helpful because it shows, historically, how well a source performs. It may be that a source just under performed for a specific campaign. We can see above that Squidoo, which performed very well in the Ongoing campaign, does not crack the top 10 sources. MySpace is no where to be found. This probably means that MySpace performs poorly across the board, not just in the Ongoing campaign.

Next to each source we can see the medium associated with that source. Again, I like to think of the medium as the mechanism that we use to push our marketing message out. Was it email, CPC, banner, print, etc. Google Analytics has pulled the medium value from the utm_medium variable and placed it in the report.

Medium Conversion Report

Looking at the medium we can evaluate how well the mechanism is working for us. Let’s see how well the Social Networking ‘mechanism’ is working.

Medium Conversion Report
Interesting. We can see that the ‘Social Networking’ medium doesn’t get a lot of traffic, but it gets attentive traffic (high number of pageviews per visit) and what appears to be an average conversion rate (for this site) for Goal 1.

The medium report is good at identifying dependencies. Are you too dependent on a particular way of getting traffic? If all your conversions come from organic, and the search engines drastically change their ranking algorithms, then you could loose a lot of traffic and a lot of money!

Other Reports

In addition to the Conversion reports, there are also three ROI reports. These reports are very similar to the Conversion reports. They segment the data in the same way (based on campaign, medium and source). The difference is the metrics reported. Rather than conversion rates, these reports show cost, revenue and ROI. If you have an e-commerce site and are collecting revenue, or have monetized the values of your goals, then the revenue generated by each campaign will be displayed.

Campaign ROI Report

A warning about this report. GA will only pull in cost data from your AdWords campaigns. Do not be alarmed if you see no other cost data in this report. GA is a closed system, you can not import cost data from other sources. This means that the ROI calculations will be incottrct for non-AdWords campaigns.

The referral conversion report is another fantastic report :) This report lists all of the un-tagged, non-organic and non-direct links that drove traffic to the site.
Referral Conversion Report

Drilling down into this report will show you where on each referral domain, the visitor originated. I find the referral report enlightening. The web is a wacky place. And people reference content in so many different ways. This report will help you hunt down all the sources of your traffic.

referral-drilldown.jpg

Some Final Thoughts

You may notice that some of the reports above have multiple lines for the same items. For example, the Medium Conversion report has two line items for social networking:

  • Social_Networking
  • social_networking

The reason the item is listed twice is that the person tagging the links specified two different values for the utm_medium variable. That’s why it’s important to use a standard naming convention when tagging your links.

Well, that wraps up this Part 3 of our Campaign Tracking series. What did you think? My ultimate goal is to make you all marketing measurement wizards. Am I doing a good job?

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