Mind the Gap: Improving Referral Information with Universal Analytics

The following is a guest post contributed by Dan Wilkerson, marketing manager at LunaMetrics, a Google Analytics Certified Partner & Digital Marketing Consultancy.

A core issue with measuring social media is that due to the way that traffic migrates around the web, there are lots of situations where we lose referrer information and those visits end up being labeled as 'Direct' inside of our analytics.

This can happen for a variety of reasons, but the most common situations where this kind of erroneous attribution occurs are:
  • When a user clicks an untagged link inside an email
  • When a user visits from a mobile application
  • When a user clicks a link shared to them via an instant message
If a visitor has come to your site previously, Google Analytics will simply apply the same referral information it had for their previous visit, which it retrieves from the UTMZ cookie it previously saved on the visitor's browser. But, if there are no cookies, Analytics has no information, and buckets the visitor into Direct.

Obviously, this is problematic; 'Direct' is supposed to represent visitors who bookmark or directly type in our URL. These users are accessing our site through a shared link, and should be counted as referrals. Thankfully, we have some tools at our disposal to combat some of these scenarios, most notably campaign parameters. But campaign parameters only help with links that you share; what about when a visitor comes to your site and shares the link themselves?

These visits can cause serious problems when it comes time to analyze your data. For example, we offer Google Analytics & AdWords training. Most of our attendees are sponsored by their employers. This means they visit our site, scope out our training, and then email a link to a procurement officer, who clicks through and makes the purchase. Since the procurement officer comes through on the emailed link and has never visited our site, the conversion gets bucketed into 'Direct / None' and we lose all of the visit data for the employee who was interested in the first place. This can compound into a sort of feedback loop - the only data we see would be for individuals who buy their own tickets, meaning we might optimize our marketing for smaller businesses that send us less attendees. In other words, we'd be interpreting data from the wrong customers. Imagine how this kind of feedback loop might impact a B2B trying to generate enterprise-level leads - since they'd only see information on the small fry, they could wind up driving more of the wrong kind of lead to their sales team, and less of the right kind.



For a long time, this has been sort of the status quo. Now, with new features available in Universal Analytics, we have some tools we can employ to combat this problem. In this post, I want to share with you a solution that I've developed to reduce the amount of Direct traffic. We're calling it DirectMonster, and we're really excited to make it open source and available to the Google Analytics community.

What is DirectMonster?
DirectMonster is a JavaScript plug-in for Google Analytics that appends a visitor's referral information as ciphered campaign parameters as an anchor of the current URL. The result looks something like this:


When the visitor copies and shares the URL from the toolbar, they copy that stored referral information along with it. When someone without referral information lands on the site through a link with those encoded parameters, the script decodes that information as campaign parameters to pass along to Google Analytics, waits until Analytics writes a fresh UTMZ cookie, and then ciphers, encodes, and re-appends the visitors current referral information. It also appends '-slb' to the utm_content parameter. That way, those visits can be segmented from 'canonical' referrals for later analysis, if necessary. The visitor who would have had no referral information now is credited as being referred from the same source as the visitor who shared the link with them. This means that visits that normally would have been erroneously segmented as 'Direct / None' will now more accurately reflect the channel that deserves credit for the visit. 

At first, this might seem wrong - shouldn't we just let Analytics do its job and not interfere? But, the fact is that those visits aren't really Direct, at least not in its truest interpretation, and having 'assisted referrer' channel information gives you actionable insight. Plus, by weeding out those non-Direct scenarios, your Direct / None numbers will start to more accurately represent visitors who come to your site directly, which can be very important for other measurement and attribution. It's actually better all the way around. After all, if a Facebook share is what ultimately drove that visitor to your site, isn't having that information more valuable than having nothing at all? This way, you'll have last-click attribution for conversions that otherwise would have simply been bucketed as Direct. Of course, you won't have the visit history of the assisting referrer, but... well, more on that soon.

We've been fine-tuning this on our site for the past few months, and we've been able to greatly enhance our conversion attribution accuracy. In our video case study, I mentioned that we enhanced attribution by 47.5%; since that time, we've seen the accuracy of our data continue to climb; whereas before, we were seeing 'Direct / None' account for 45.5% of our conversions, it now accounts for just 20.6% - a decrease of 54.7%. Better yet, look at what it's done to all of our traffic:


We've gone from having about 20-25% of our traffic come in 'Direct / None' to just under 15%, and I anticipate that number will continue to fall.

DirectMonster and Universal Analytics
One of the coolest features that Universal Analytics has given us is Custom Dimensions. If you're not familiar with them, take a minute and read the Google Developer Resources page about what they are and how they work. Although initially designed for the asynchronous code, Universal Analytics has allowed us to put DirectMonster on steriods. 

In our Universal implementation, we store the visitors CID as a visit-level custom dimension, and we add their CID to the hashed parameters we're already storing in the anchor of their URL. 

When a visitor comes through on a link with a CID that differs from their own, we capture the stored CID as the Assisted Referrer. Then, we can open up our Custom Reports later on and view what visitors were referred to our site by whom, and what they did when they got there.

What does this mean? If a celebrity tweets a link to your product, you can discover exactly how many visitors they referred, and how much revenue those visitors generated. 

By cross-referencing the Assisted CID for single-visit 'Direct / None' purchases, you can discover the true visit history of a conversion.

Since it takes advantage of advanced Universal Analytics functionality, DirectMonster 2.0 requires some advanced implementation as well. Unlike its cousin, you'll need to adjust your Analytics tracking code to include a few functions, and you'll need to configure the Custom Dimensions you'll be storing a visitors CID and assisted referrers CID inside of. For a full reference on how to get either version of DirectMonster and configure it for your site, check out our blog post covering the topic in detail here or visit our GitHub page and get DirectMonster for yourself. 

I hope that you're as excited as I am about this development and all of the things Universal Analytics is enabling us to do. Think of a use case I didn't mention? Share it with me in the comments!

Posted by Dan Wilkerson, marketing manager at LunaMetrics

Analyze Organic Search Engine Marketing with Google Analytics & Webmaster Tools Data

There are many ways to measure the effectiveness of organic search engine marketing. We’d like to explore various techniques in a series of posts here on the Analytics blog. Today we’ll talk about understanding organic using landing pages and Webmaster Tools data. 

Today, almost all marketers are investing heavily in creating high-quality content as a way to reach users with information about their products and services. The content can take many forms - from product specific content to brand specific content. The intent is to generate traffic and conversions from a variety of sources, one of the largest of which is often search.

One way to measure the effectiveness of content is to analyze its performance as a landing page. A landing page is the first page a user sees when they land on your site. If it’s great content, and if it’s ranked highly by search engines like Google, then you should see a lot of websites ‘entrances’ via that page. Looking at landing page performance, and the traffic that flows through specific landing pages, is a great way to analyze your search engine optimization efforts.

Begin by downloading this custom report (this link will take you to your Analytics account). This report shows the landing pages that receive traffic from Google organic search and how well the traffic performs. 

Let’s start at the top. The over-time graph shows the trend of Google organic traffic for your active date range. If you are creating great content that is linked to and shared then you should see the trend increasing over time.

When you look at this data ask yourself the question: how well does the trend align with my time investment? Looking at the data below we see that the organic traffic is increasing, so this organization must be working hard to create and share good content.

Organic traffic is steadily increasing for this site. An important question to ask is, “how does this align with my search optimization efforts?”
The table, under the trend data, contains detailed data about the acquisition of users, their behavior on the site and ultimately the conversions that they generate. This includes data like Visits, % New Visits, Bounce Rate, Average Time on Site, Goal Conversion Rate, Revenue and Per Visit Value. 

Using the tabular data I can learn how search engine traffic, entering through a specific page is performing. 

Each metric provides insight about users coming from organic search and entering through certain pages. For example, % New Visits can help you understand if you’re attracting a new audience or a lot of repeat users. Bounce rate can help you understand if your content is ‘sticky’ and interesting to users. And conversion rate helps you understand if organic traffic, flowing through these landing pages, is actually converting and driving value to your business.

Again, we’re using the landing page to understand the performance of our content in search engine results.

Remember, make sure that you customize the report to include goals that are specific to your account. You can learn more about goals and conversions in our help center.  

Another very useful organic analysis technique is to group your content together by ‘theme’ and analyze the performance. For example, if you are an ecommerce company you may want to group all of your pages for a certain product category together - like cameras, laptop computers or mobile phones.

You can use the Unified Segmentation tool to bundle content together. For example, here’s a simple segment that includes two branded pages (I’m categorizing the homepage and the blog page homepage as ‘brand’ pages).


You can create other segments that include other types of pages, like specific category pages (and then view both segments together). Here is the Acquisition > Keywords > Organic report with both segments applied. This helps me get a bit more insight into the types of pages people land on when visiting from Google organic search results.

Plotting two segments, one for branded content landing pages and one for non-branded landing pages, can help you understand your specific tactics.
Regardless of the tool you use, the analysis technique is the same: look at the performance of each landing page to identify if they are generating value for your business. And don’t forget, the best context for this data is your search engine marketing plan. 

Here’s one final tip when analyzing organic traffic. Whenever you create a customization in Google Analytics, like a segment or custom report, don’t use the keyword dimension. Instead use the Source and Medium dimensions. Set the Source to ‘Google’ and Medium of ‘Organic’. It provides the most consistent data over long time periods. 

In addition to using Google Analytics, you can also use the data from Webmaster Tools to gain an understanding of your search marketing tactics. You can link your Google Analytics account and your Webmaster Tools account to access some of this data directly in Google Analytics. If you’re not familiar with Webmaster Tools, check out their help center for an overview or this awesome video.



In general the Webmaster Tools data will help you understand how well your content is crawled, indexed and ranked by Google. This is extremely tactical data that can inform many search marketing decisions, like which content to create, how to structure your content and how to design your pages. The reports are in the Acquisition > Search Engine Optimization section. 

Let’s start by viewing some data using the Acquisition > Search Engine Optimization > Landing Pages report.

Webmaster Tools data is available directly in Google Analytics. You can view the data based on landing page or search query.
Let’s review a couple of metrics that are unique to Webmaster tools: Impressions, Average Position and Click Through Rate. Impressions is the number of times pages from your site appeared in search results. If you’re continuously optimizing the content on your site you should see your content move up in the search results and thus get more impressions.

Average position is the average top position for a given page. To calculate average position, Webmaster Tools take into account the top ranking URL from your site for a particular query. For example, if Alden’s query returns your site as the #1 and #2 result, and Gary’s query returns your site in positions #2 and #7, your average top position would be 1.5 [ (1 + 2) / 2 ].

Click Through Rate (CTR) is the percentage of impressions that resulted in a click and visit to your site. Again, you can see both the impressions and the CTR for every landing page on your site. 

If we’re optimizing content then hopefully we should see our average position increase, the impressions increase and ultimately an increase in click-throughs. A very easy way to observe this behavior is by applying a date comparison to the Acquisition > Search Engine Optimization > Landing Pages report.

Use the Search Engine Optimization > Landing Pages report to understand if your content is getting ranked higher and generating clicks.
What happens if impressions and average position are increasing but you’re not getting clicks? You’re getting ranked better, but what is listed in the results may not get a response from the user. 

There are lots of ways to optimize your content and change what is listed in the search results. You could adjust your page title or meta description to improve the data that is shown to the user and thus increase the relevancy of the result and your Click Through Rate. 

We’ll be back soon with another article on measuring and optimizing organic search traffic with Analytics.

Posted by Justin Cutroni, on behalf of the Google Analytics Education team

Monitoring & Analyzing Error Pages (404s) Using Analytics

I recently wrote a post on the Google Analytics + page about monitoring error pages on websites. The post was well received and generated a healthy discussion on Google+, so I decided to write a more detailed article on the subject here.

First of all, what exactly is an error or 404 page? According to WikipediaThe 404 or Not Found error message is a HTTP standard response code indicating that the client was able to communicate with the server, but the server could not find what was requested.” Or, in more general terms, the 404 is the error you get when the page you are looking for does not exist, usually because the link you clicked was broken.
Another important question is: why should I care? Often times the 404 is forgotten and no one cares to prioritize its optimization. I believe the answer to prioritization lies on section 2 of this post: by monitoring the percentage of users that arrive at this page you will be in a better position to know if (and how quickly) you should optimize your 404 page. In any case, even if the number of people viewing this page is low, you should at least have a page in the lines of your brand and try to add the elements described in section 1 below; after all, you don’t want to disappoint your customers!
In this post I propose a few techniques to help improve error pages, engage visitors and improve the website experience. The questions I will try to answer are the following:
  1. How to build your 404 page?
  2. How to monitor your 404 page traffic efficiently?
  3. How to analyze & optimize 404 page success?

1. Error Pages Best Practices

Before we dive into Google Analytics, let’s take a look into some ways to create a great 404 page from the beginning. Following are some good usability insights proposed in a book called Defensive Design for the Web. The authors advise us to offer customized "Page Not Found" error pages; and they provide an interesting insight into how to create error pages:
Instead of merely saying a page is not found, your site needs to explain why a page can't be located and offer suggestions for getting to the right screen. Your site should lend a hand, not kick people when they are down. Smart things to include on your 404 page:
  1. Your company's name and logo
  2. An explanation of why the visitor is seeing this page
  3. A list of common mistakes that may explain the problem
  4. Links back to the homepage and/or other pages that might be relevant
  5. A search engine that customers can use to find the right information
  6. An email link so that visitors can report problems, missing pages, and so on

2. Monitoring Error Page Traffic

Suppose a prominent blog links to your site and the link is broken, this will cause a negative experience to users (which will not find what they expected) and to search engines (which will not crawl the right content). How long will it take until you notice it? How often do you check the traffic to your 404 page? Chances are you don’t do it every day, but you should! Or at least you should have someone look at it: why not let Google Analytics do it for you? 
Create an Alert on Google Analytics
In the screenshot below you will see how to set an alert on Google Analytics that will let you know each time your 404 pageviews increases above a certain threshold. This will enable you to do the work once and be alerted every time there is a problem. 
The alert below is based on the increase in error pageviews, but if you decide to create a goal (as suggested below) you could create the alert based on the goal too. Note that you can opt in to receive an email or a text message when the condition is met (404 pageviews increases more than 15% compare to previous day). Also note that I decided to define the 404 page based on the title of the page, very often there is no indication of an 404 page on the URL (read more about this below). 
To learn how to set a Custom Alert check this help center article.

Measure your 404 Page as a Goal
Setting the 404 page as a goal on Google Analytics will surface important information that can be achieved only through goals, e.g. the last three steps before getting to this page. Below is a screenshot on how to do it, but note that you would need to have an identifier on your URL (or trigger an event) in order to set your 404 as a Goal.
Add Your 404 Content Report to Your Dashboard
Every report on Google Analytics can be added to the dashboard. By adding the 404 page to your dashboard you will be able to constantly monitor the trend of visits to your 404 page. Learn more about customizing dashboards.

3. Analyzing & Optimizing Error Pages

Monitoring your 404 pages is important, but useless if you don't take action using this information. Taking action means doing all you can to decrease the number of people getting missing pages. Below I provide a few tips on how to find and fix both internal and external broken links.
Check Your Navigation Summary Report
This will help you understanding where did visitors come from from inside your site, i.e. it will tell you which pages contain internal broken links. You will also be able to understand what is the percentage of visitors that arrive to the 404 page from internal and external sources; the internal sources will be listed on this report. See navigation summary screenshot below:

Check 404 Page URLs
Learning which URLs are producing the errors is a great way to get rid of them. If you learn, for example, that 100 visitors a day get an error when they visit the page “/aboutS” you can infer that there is a broken link leading to it; sometimes it might not be possible to find the source of the error to fix the link, but you can add a redirect from that page to “/about”, which looks to be the right page. 
In order to do that you will need to find the report below, but please keep in mind that some assumptions were made to arrive at it. Most CMS (Wordpress, Drupal, and others) will return an error for non-existing pages on the actual content section, but they will keep the original URL; however, they will have a page title with the word 404 in it. So check your site to know if that is the case before you try the report below.
Once you find this report, click on the first entry and you will get a list of all the URLs that triggered an error page. Good luck with the redirects!
Measure Internal Searches From this Page
If you do not have a search box on your 404 page, you should seriously consider adding one. Through searches performed in this page you will be able to understand what people were expecting to find there and you will get insights on which links you should add to the page. If you don’t have Internal Site Search enabled on Google Analytics check this help center article.
Below are the metrics you will be able to analyze if you use this feature:
  • Total Unique Searches: the number of times people started a search from the 404 page. Duplicate searches within a single visit are excluded.
  • Results Pageviews/Search: the average number of times visitors viewed a search results page after performing a search.
  • % Search Exits: the percentage of searches that resulted in an immediate exit from your site.
  • % Search Refinements: the percentage of searches that resulted in another search (i.e. a new search using a different term).
  • Time after Search: The average amount of time visitors spend on your site after performing a search.
  • Search Depth: The average number of pages visitors viewed after performing a search.
Closing Thoughts
As we mentioned above, errors happen, and we must be prepared for them. We must give a hand to our visitors when they are most frustrated and help them feel comfortable again. The level of online patience and understanding is decreasing and users have a world of choices just one click away, so website owners cannot let one small error get on their way.
Posted by Daniel Waisberg, Analytics Advocate

Webinar Video & Recap: Measuring Success in a Multi-Device World

Last Thursday, we held a webinar discussing how to effectively measure the customer’s journey in a multi-device world. We focused on high-level best practices and strategies, as well as how Google Analytics and other Google tools can help you measure and respond to the evolving customer journey.

Watch the webinar video here to learn more about:
  • Holistic, full-credit, and active measurement
  • Everyday strategies to improve your measurement and marketing performance
  • Basic techniques for marketing attribution
  • Google Analytics features and tools for measuring the full customer journey

During the webinar, we received dozens of great questions from viewers. Read on below for responses to some of the most common questions we received.

Questions and Answers

What other blogs would you recommend for advice on measurement best practices?
Avinash Kaushik is the author of Web Analytics 2.0 and Web Analytics: An hour a day. On his blog, he discusses how to use digital marketing and measurement to focus on the customer while maintaining your ROI.

Justin Cutroni is the author of Google Analytics, Performancing Remarketing with Google Analytics, and Google Analytics Shortcut. He uses his experience as a consultant to guide his blog topics. His blog provides readers with techniques for using Google Analytics to maximize their marketing strategies.

Where can I find the “Think Insights” website referenced during the webinar?
Visit www.google.com/think for access to all sorts of statistics and articles about the latest trends in customer behavior. To learn more about the customer journey to online purchase, view the interactive benchmarking tool here.

How does marketing attribution help with intra-channel optimization?
Marketing attribution can help you to optimize intra-channel campaigns by allowing you to see value for each of the specific moments in the customer journey that you may be addressing within that single channel. For example, if you are running a search campaign, you may think about the role that different types of keywords play at different moments to help generate awareness for your brand, move the customer to consider your product, or to help close the deal. Using tools such as AdWords Search Funnels, you can determine where in the customer path those keywords had an impact, and this can help you optimize your keyword mix.

What are first-click and last-click attribution models?
The first and last clicks are important parts of two  commonly used attribution models, the “first interaction” attribution model and the “last interaction” attribution model. Depending on which model you use, all credit for the sale (or conversion) is attributed to either the first or last click. In the “first interaction” model, the first touch point would receive 100% of the credit for the sale. In the “last interaction” model, the last touch point receives 100% of the credit. Historically, many businesses have relied on the last-click model alone, but since this model (like the first-click model) only addresses a single touch-point along the customer journey, it may miss other important marketing interactions.

There is no one specific model that will work for every business or every program within your business. Rather, you should explore different models and experiment to see which model or combination of models best fits your needs. Check out Google Analytics Multi-Channel Funnels and Attribution Modeling to get started.

What are some tips for measuring the customer journey with Universal Analytics?
Consider integrating Universal Analytics with all of your digital touchpoints (see some examples in this post). Here are a few use cases that our Certified Partners are already implementing to measure the customer journey beyond web:

  • Integrated measurement and analysis of in-store POS systems along with desktop and mobile e-commerce platforms.
  • Measuring offline macro and micro conversions through physical buttons or integration with CRMs.
  • Measuring physical interactions -- for example at display booths at conventions or artworks at major exhibitions -- through to online engagement with associated websites.
Posted by Sara Jablon Moked & Adam Singer, Google Analytics Team

Getting Started with Analytics Measurement for Marketing Campaigns: A Brief Guide

As an analytics practitioner, one of the most important things I try to teach marketers is how to properly tag their campaigns so we can report on the success of their efforts. To do this, I've created a guide for them to follow to make it easy to choose the proper UTM codes to have consistent campaign tagging across the business. This allows us to begin to assign source and medium values to finance channels and usage metrics to really understand how each campaign performs in terms of our bottom line business metrics. 

OVERVIEW
Setting up tracking and reporting on your marketing campaigns is simple and fun. This guide will walk you through the process and demonstrate with a real-life example.

Part A: Set up UTM tracking code

1. Below are all the elements you’ll need. If you set these up correctly, you’ll be able to report on multiple elements of your campaign:
  • Campaign -name of your overarching campaign - e.g. spring-2013-collection or summer-2013-announcements. Be sure to follow a consistent campaign naming structure.
  • Medium - the medium used to send your campaign. Include “email” for an email campaign, “cpc” for ads, “social” for a social network or “landing-page” if you’re tracking button clicks from a landing page. 
  • Source - used to differentiate the type of medium. If medium = cpc, then source may be google, bing, or yahoo. If utm medium = email, source can be used to call out the action (try, buy, coupon, awareness, etc).
  • Content - this is essentially a bonus field - it can be used to track many differentiating factors for your campaign. For example, you can use this field to track different versions of your email or landing page - e.g. “60-dollars-off” or “15-percent-off”.
2. Make a copy of this template and update it with your campaign’s values.  You’ll likely end up with several tracking links for your campaign.

3. Tag each version of your campaign creative with the matching link. After updating the values, your tracking link should something like this:

https://www.googlestore.com/?utm_source=coupon&utm_medium=email&utm_campaign=summer-sale&utm_content=15-percent-off

Part B: Testing reporting

1. Before launching your campaign, verify that your tags are working correctly. Open an incognito window and click on one of the links you set up to track your campaign. If your campaign’s objective is trial signup, try completing the trial sign up form. If your objective is redeeming a coupon, try redeeming the coupon. Try this with each tag created for your campaign (best practice is to clear your cookies in the incognito window before clicking each tag). For landing pages - make sure to go all the way through to your main site or objective.
  • Recommended best practice is to try each link multiple times, dropping off at various points to ensure you can track a funnel flow. 
2. Wait 24 hours (in a crunch data should populate in analytics within 4-6 hours but depends on volume).

3.  Go to google.com/analytics and click Sign in.

4. After signing into analytics you will be on the “Audience Overview” page. Click on “Traffic sources” - > “sources” - > “campaigns”.

5. Type the name of your campaign into the search box in the middle of the page and click on the search icon.


You should now see an overview of all clicks on your campaign. However, since you are in a non-standard report in GA sampling will likely occur (you may not see all - or even any of the test clicks on your campaign). Given sampling,  you may need to export an unsampled report after all filters/segments are applied to your test - see step 9.

Click into this overview.



7. A screen similar to the below should appear, breaking out your campaign performance into different source/medium.



8. To drill down into the different elements of your campaign, click on the “secondary dimension” tab and type in the element - this could be “content” (shown below) or “medium” or “source”.



You will then see your Source/Medium broken down by content. In this example utm_content was used for ad creative, so the Ad Content secondary dimension breaks each Source/Medium down by which creative was clicked.



9. If all of your test campaign metrics are coming up in GA reporting you are ready to launch (be sure to keep track of # of links clicked/steps completed for each test link to match back data). Good luck! And come back to GA to see reports on your live campaign.

Part C: Advanced Reporting

A few more notes on nifty things you can do with GA reporting.
1. Advanced segments enable you to view all data in GA for a target segment in your campaign. 
  • Click on “Advanced Segments” at the top of your GA window.  
  • Click the button “+new custom segment”
  • Using “and” or “or” statements, define the segment of your campaign you want to see GA data for:
  • Save the segment. You can now browse through your Analytics reports, viewing data only for this segment 
2. Set up a dashboard. Under “MY STUFF” on the left-hand navigation. Click “Dashboards”. Here you can customize a dashboard for external stakeholders looking to monitor the performance of your campaign.

Happy tagging and analyzing!

Posted by Krista Seiden, Product Marketing Manager, Google Enterprise

The Periodic Table of Google Analytics

The following is a guest post from Jeff Sauer, Vice President at Three Deep Marketing, a Google Analytics Certified Partner. Jeff writes often about Digital Marketing at his Jeffalytics Blog and he writes about Travel on his Free World Traveler blog. 

I have long compared Google Analytics to the slogan for board game Othello: "A Minute to Learn, a Lifetime to Master" because it is so easy to get started, yet there are so many customization options and continuous product improvements that it takes years to master all aspects contained within GA.

The Google Analytics team has done such an amazing job at making the product easy to use that a significant portion of the web measures their website performance with Google Analytics. With that said, I've found that because it is so easy to use, not all marketers are aware of the advanced features contained within GA.

While conducting in-person Google Analytics training the past several years, I have been looking for the best way to show my students just how much they can do with Google Analytics. While a comprehensive classroom session goes a long way, a more elegant way of simplifying the complex is also valuable in the long run.

After much brainstorming with colleagues, we came up with the idea of creating the Periodic Table of Google Analytics, inspired by similar periodic table pieces that have ran in the past by well known marketing focused websites. Since the concept works so well for displaying many elements in one place, it worked perfectly for defining the 60+ elements that were included in the Periodic Table of Google Analytics. We hope you enjoy the results!


We created the table to be consumed in many different ways. On Jeffalytics.com, you will find an interactive version of the table which gives an explanation of each element as you hover over. You can also find a printable PDF version of the document for printing at home. Last, we have created an embeddable graphic that you can share on your site.

Posted by Jeff Sauer, Google Analytics Certified Partner

3 Key Google Analytics Features In-House Practitioners Should Be Using

Working as a practitioner in house at a technology company, one of my jobs is to teach my team members how to fish with Google Analytics. What should they be looking for in GA? Where do they start? What is meaningful? Are the campaigns being measured? Are the microsites tagged? These are the types of questions I get everyday, and very likely, you do too. 

I've narrowed down my tips to 3 key things I try to get people comfortable with first (bite sized bits to get them hooked). 

1. Event Tracking
Most of the things that people are interested in are actions on a page. Did a visitor click on button X? Did they complete form Y? Watch video Z? These are all questions we can answer with event tracking. 

Because event tracking in Google Analytics is a blank slate in terms of setup and use, there is no one right answer for how to set it up and use. Given that most of my account was setup before I arrived in this position, I too have had to get used to a new architecture. The way I do this, and the way I explain it to my colleagues, is by investigating the event hierarchy. What are the categories, actions, and labels? How is data organized into these three tiers? 

While there is no one 'best way' of organizing an event tagging hierarchy, and while it will vary site to site, I like to set mine up like this:
  • Category: location of event (Homepage, About Us page, Resources page, etc)
  • Action: action the user took (Video, Whitepaper download, Start Trial, etc)
  • Label: specifics about action (Video name, Whitepaper name, detail of linked clicked if there are multiple with same action (ex. Learn more - product A, learn more - product B, etc) 
2. Advanced Segments
Advanced segments are a great way to filter data to be more specific to the question you are trying to ask. For example, you can create a segment for a region (North America = US + Canada), or you can create a segment for a set of pages (meaning visit applies to homepage and/or about us page). To evangelize and teach this, I've created a Google doc that I've shared with my team with step by step instructions and links to some pre-built regional segments. 

As an account admin, it's great to share out globally the segments you make that may apply to multiple consumers. And you can easily share links to segments for users to apply to their own account.

Regional Segment example:

3. Shortcuts
Normally when an internal user asks for GA training and/or help pulling a report, it's for something they plan to look at on more than one occasion. Depending on how complex the report is, it may be useful to create a shortcut.

Ex. Your account has 5000 uniques pages tracked in the pages report. You are interested in 4 pages that all share the same sub-domain (they may be steps in flow - example: www.myshoppingsite.com/women, www.myshoppingsite.com/accessories, www.myshoppingsite.com/handbags, www.myshoppingsite.com/gucci). 

You can filter the pages report (using advanced filters) to show only these 4 pages. Then you want to know how many visits to those pages had a checkout, so you apply a checkout segment onto the report. Then you also want to define that group one more step by only looking at North America traffic, so you apply a second advanced segment for North America. Then, just for kicks (or analysis) you want to know what the landing page was for this subset of purchasers, so you apply a secondary dimension for landing page. 

Now that's a fairly complicated report that took several steps to build. Your may not want to go through all those steps the next time you need this report (nor as an admin/power user do you want to have to show them again), so you can create a shortcut for this report. The shortcut link is a new beta feature located on the top nav bar that allows you to save a report as is and provides a shortcut link on the left hand nav to get back to it quickly. Pretty handy.

As an admin or power user: Once your users have these three functions handled they will a) be able to pull a lot of their own data, freeing up your time, and b) feel more confident and excited about using Google Analytics to make data driven decisions. Win-win.

Posted by Krista Seiden, Product Marketing Manager, Google Enterprise

5 Things You Should Be Doing With Google Mobile App Analytics Crash & Exception Measurement

When an app crashes, it disrupts the user experience, may cause data loss, and worst of all, might even cause users to uninstall the app altogether. As developers, we do our best to minimize crashes, but no app is ever perfectly stable.

A crash can actually represent a great opportunity to improve an app and one of the best things we can do as developers is to measure our crashes and exceptions.

The crashes and exceptions report in Google Mobile App Analytics.
Measuring crashes in your app can help you make better a product, make more money (if that’s your thing), and use your development resources more efficiently (especially if you are the only developer).

Google Mobile App Analytics offers easy-to-implement automated crash and exception measurement for Android and iOS as part of the V2 SDKs, as well as a host of reporting options to slice the data in context with all of the user engagement, goal completion, and in-app payments data you already know and love.

To help new developers get started, and to give existing developers some pointers, here are four things app developers should be doing today with Google Analytics crash and exception measurement:

1. Automate your crash measurement.
Want to measure app crashes but don’t want to deal with a complicated implementation? Fully automated crash measurement with Google Mobile App Analytics takes just one line of code to implement for Android or iOS:

<!-- Enable automatic crash measurement (Android) -->
<bool name=”ga_reportUncaughtExceptions”>true</bool>

// Enable automatic crash measurement (iOS).
[GAI sharedInstance].trackUncaughtExceptions = YES;

Implement automated crash measurement with just one line of code on Android or iOS.

Now each time your app crashes, the crash will be measured and sent to Google Analytics automatically. Try automated crash measurement now for Android or iOS.

2. Find out how stability is trending.
Are new releases increasing or reducing app crashes? Monitor the stability of your app from version to version by looking at crashes and exceptions by app version in the Crashes & Exceptions report.

If you are measuring the same app on two different platforms, like Android or iOS, you can break this view down further by selecting Platform as the secondary dimension.
View crashes and exceptions by app version number in the Crashes & Exceptions report. In this example, version 1.1.7 has crashed 7,285 times, while the latest version 2.0.0 has only crashed 91 times in the same period. Nice work dev team!
To graph crashes for two or more versions over time, you can create advanced segments for each version number, and apply them both to the Crashes and Exceptions report.

See crashes by app version over time using advanced segments and the crash and exception report  In this example, a bug fix pushed around January 24 caused significant reduction in crashes across both versions, but crashes persist for v1.1.7 that might warrant some additional investigation.
3.  Find out what crashes are costing you.
Do you know what app crashes are costing you? Find out what crashes cost in terms of both user engagement and dollars by using a custom segment.

By using a particular crash or exception as a custom segment, you can see how user engagement and in-app revenue may be impacted by a particular issue or set of issues.
Use custom segments to segment user experience and outcome data by crashes. This gives you some idea of what they might be costing you in users and in dollars.
To set this up, you’ll want to create two custom segments: one that contains all the sessions in which the exception(s) occurred, and another baseline segment that contains all other sessions unaffected by the exception(s).


Once created, try applying both segments to your Goals or Ecommerce Overview reports to get a sense of how the exception(s) might affect user outcomes. Or, apply the segments to your Engagement overview report to see how the exception(s) might impact user engagement metrics.

4.  Gain visibility into crashes at the device model level.
Do you know which device models are the most and least stable for your app? Developers can’t always test their app on all devices before launch. However, by using Custom Reports in Google Mobile App Analytics, you can monitor crashes and exception per device to find out where additional testing and bug fixes may be needed.

To see crashes and exceptions by device, create a custom report and use a dimension like Mobile Device Marketing Name, with Crashes and Exceptions as the metric.


See crashes by device by using a custom report. To get even more detail, add the Exception Description dimension as a secondary dimension. In this example, the high level view shows the Galaxy Note and Desire HD as device that might need additional testing before the next launch.
5.  (Advanced) What about caught exceptions? You should measure those too.
While caught exceptions won’t crash your app, they still may be valuable events to measure, especially when they might have an impact on user experience and outcomes.

For instance, if your app normally catches a server timeout exception when requesting user data, it might be useful to measure that caught exception to know how often a user’s request is not being fulfilled.

A caught exception is measured in Google Analytics using a custom description. In this example, a number of failed connections may indicate a backend problem and could be causing a poor user experience. Reducing the number of these caught exceptions could be a goal for the dev team in the next release.

As always, please keep in mind that you should never send personally identifiable data (PII) to Google Analytics. Raw exception descriptions may contain PII and we don’t recommend sending them to Google Analytics for that reason. 

Also note that there’s a 100 character limit on exception descriptions, so if you send your own descriptions, be sure to keep them concise.

Lastly, here are some links to resources you might find helpful when implementing crash and exception measurement in your app:


And for brand new users:

Posted by Andrew Wales, Google Analytics Developer Relations

Are you a Datavore? Insights on the use of online customer data in decision-making

The following is a guest post contributed by Hasan Bakhshi and Juan Mateos-Garcia who work at Nesta, an independent charity based in London. Nesta’s mission is to help people and organizations bring great ideas to life by providing investments and grants, and mobilizing research, networks and skills.

We surveyed 500 of the UK's companies that were actively online and promoting their products. We asked about the collection, analysis and use of online customer data in their decision-making, and the impact this has on their bottom line. Our research suggests that a startlingly high number of businesses in the UK's Internet Economy would benefit from reading Michael Loban's post on data resolutions for 2013. Here, we revisit some of his insights backed up by our data to illustrate how big the online data gap is for many UK companies, and what they must do to bridge it.

Insight 1: 'Address your data phobia'. 
We identified a cohort of companies in our sample with apparently no fear of data. We call them 'datavores'. When making decisions about how to grow their sales, they rely on data and analysis over experience and intuition. They collect data comprehensively, analyze it thoroughly and use it to make decisions. But they are in the minority: only 18% of the companies we surveyed compared with 43% who make decisions on the basis of experience and intuition. These ‘gut-driven’ companies would stand to reap significant commercial benefits from their online data if they could get over their data phobia. We find that datavores are four times more likely to report substantial benefits from their online customer data.

Insight 2: Get on with social network marketing. 
Only 40% of businesses in our sample report that online data is important for designing and evaluating their social media strategy. Lacking the right data to make decisions, perhaps we shouldn’t therefore be surprised to learn that more than half of the businesses we surveyed were hesitant to dip their toes in social network marketing, despite the fact that nearly 1/2 of the UK population* uses social media actively. (* cited from UK Office for Communications)

Insight 3: Tools are great, but great analysts are awesome. 
Our survey suggests that fully harnessing the potential of online data requires up-skilling the workforce. Over three-quarters of businesses who have trained their staff to improve their data capabilities in the past two years report significant benefits from online data (compared with only 20% of those who haven’t provided training). Another of our findings leads us to add a coda to Michael’s resolution, however - while it is true that great analysts are awesome, it appears that great analysts who are empowered to act on the basis of their insights are even better. Datavores are much more likely to delegate decision-making than other firms. The implication is that making most of your data is not always painless. It may require re-organizing the business, changing its culture and rethinking the role of managers. 'No pain, no gain', as they say about most New Year's Resolutions.

Insight 4: ACTION!
This brings us neatly to perhaps our most important finding: in order to benefit from online data UK businesses need to put their data to work. They need to use it to improve their website to be sure, but they also need to feed it into decision-making process in other parts of their business – such as in product development and business strategy. In fact, the ‘use of data to make decisions’ turns out to be the main factor discriminating between the datavores and the other companies we surveyed. And that, controlling for other relevant business characteristics, ‘using data’ is what really makes a difference on the impact of data on performance. To put it in stark terms, if you don’t use your data, you may as well not have collected and analyzed it.

We present the findings above (and many more) in greater detail in our Rise of the Datavores report. This is the first milestone in our program of research and action looking at the potential of online data for innovation and growth.

In the coming months, we will be matching our survey responses to financial data to measure in quantitative terms the connection between data-driven decision-making and sales growth, profitability and productivity. To get a really robust handle on the direction of causality in this relationship we are looking to run a controlled experiment to measure the impact of an Online Analytics intervention on a randomly selected group of UK businesses. In related research we plan to quantify the extent of business demand for data-savvy talent and assess the adequacy of the UK's education system in supplying it.

Last, but not least, we will be looking through practical work to identify datavores in the public and third sectors, and work to encourage the transfer of good data practices across different parts of the UK’s economy and society.

In all these areas we will be looking for data experts to work with, to probe whether we are asking the right questions, to refine and help implement our research plans. Drop us a line if you want to hear more!

hasan.bakhshi@nesta.org.uk
juan.mateos-garcia@nesta.org.uk

Posted by Hasan Bakhshi and Juan Mateos-Garcia at Nesta