But that’s OK, I use data alerts to stay on top of my data even if I can’t check analytics every day. Are you using alerts?

If you’ve never used Google Analytics alerts they’re fairly simple to set up. You can find them in the Profile Settings.

There are five things that you need to do to set up an alert:

1. Choose a profile to apply the alert to.
2. Select how often you want to monitor the data (daily, weekly or monthly).
3. Identify the segment of traffic you want to monitor (i.e. which group of visitors, like visitors from a particular campaign or geographic region).
4. Choose what metric you want to monitor (Revenue, Visits, Time on Site, etc.).
5. Set a threshold for the alert meaning how much does the metric need to change to activate this alert (ie increase, decrease, etc.).

I divide my alerts into two groups: those that insure I have good data and those that measure the performance of the business. This post deals with the former: alerts that will keep your data in tip-top shape.

Alert #1: 10% Daily Traffic Drop

This first alert is simply there to make sure that I’m collecting data. I’ve noticed, for my site, that my traffic usually never drops more than 10% for a given day when measured week-over-week. So if I see a drop of more than 10% I know that something happened and I need to investigate more.

A 10% traffic drop alert.

Alert #2: Flatline! i.e. No Data

This is a generic alert that identifies when a metric goes to zero. I can’t tell you how many times a client tries to analyze data at the end of a month only to find a data issue. This commonly happens because not every metric is analyzed every day, some metrics are reported monthly.

But I don’t want to wait until the end of the month to find a problem.

So for almost every important metric I will create a daily alert to insure that I’m getting data. Here’s an example alert that monitors an event.

A Google Analytics Alert that monitors an Event

Alert #3: Daily Error Pages

Another metric I like to monitor is error pages, more specifically 404 errors. I want to know if any of my content is generating an error for my readers. So I created a little alert that will alert me when the number of 404 pages is greater than 0.

Monitor 404 pages, and other website errors, with an alert.

Obviously you can change this alert to monitor almost any type of website error, it depends on which errors you are tracking with Google Analytics. The key is that you need to be able to identify your error pages in a Google Analytics report.

As you can see above, my error page has a unique URL (404.html) and a query parameter that identifies the page that was missing. You may need to use a virtual pageview or an event to track your error pages. Once you do you can create an alert.

Alert #4: Non-Domain Traffic aka The Poser Alert

The hostnames report in Google Analytics is a handy little report. It shows the hostname that is in the location bar of the visitor’s browser. On numerous occasions people have ripped off borrowed my content without my permission. They just copied my source code and posted it on their own domain.

I know, classy.

This alert will automatically let me know if my account is getting data from any domain other than my own.

The "Poser" Alert will let you know if someone is poaching your content.

In some cases this alert actually detects something good! I regularly see the hostname translate.googleusercontent.com, which is used when popele use Google Translate to translate the content on my site.

A note from your data therapist

All of these alerts can help. But you’ll still have inaccuracies in your data. The idea is to keep them to a minimum. If you try to eliminate all errors 100% of the time you won’t do anything else. It’a almost impossible. Do the best you can.

If you do have some data issues, and you will, you’ll have to spend some time resolving them when you do your analysis.

Save Your Ass With Google Analytics Data Alerts is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Adding Business Data to Google Analytics Data
2. Google Analytics Real Time: Real Time Data for Real Time Decisions
3. An Analysis of My Data

The NEW Google Analytics Real Time Reports

Google Analytics now contains a series of real-time reports that measure what’s happening on your site in real time. These reports will roll out in the next 1 to 2 weeks.

I’ve long railed against real-time data. I’ve said that businesses don’t need real time data. It’s very hard to action on time data. I’ve said this on the record!

But here I am, with my tail between my legs, to admit that some businesses really do need real time data. I was wrong. I can absolutely see the need for real time data.

Chartbeat Real Time Report

And if you still need convincing look at the success of tools like Chartbeat. They provide some pretty awesome data that helps business make important decisions WHEN those decisions need to be made.

Real time data can be very helpfull in the following business scenarios:

• If you are a publisher, you often need real time data to understand what content is popular right now, so you can optimize the placement of that content, or publicize that content, to optimize ad revenue.
• If you are a retailer, you might want to understand, in real time, the launch of a new campaign. This is especially useful if you are using a channel, like social media, that has a very immediate impact.
• If you are holding a physical event, you might want to understand, in real time, how people in that location are using your website.

To fill this analysis hole Google Analytics now has a Real Time section of reports, creatively named Real Time. This series of repots offers a subset of data that is processed in real time, literally every second. You can see who’s on your site, how they got there and what they’re looking at in real time.

The data covers three dimensions:

• Geographic locations
• Traffic sources
• Content

I know, there’s no conversion data. Sucky. BUT let’s consider how much data is getting processed every second. Seriously, that’s a lot of data!

IMPORTANT: One thing to keep in mind is that the Real Time reports are NOT filtered. Even if you have a profile filter applied the data is not filtered. I guess that requires a bit too much processing power.

The real time reports are NOT real time data for all metrics, only a subset of metrics. But they are pretty good. You have access to real time stats for:

• Visitors
• % New visitors & returning visitors
• Pageviews
• Traffic sources (referrals, direct, campaigns, etc.)
• Active pages

Let’s look at the reports and how they solve the above business cases.

Real Time Overview Reporting

Everything starts with the Overview report. This is mission control for Real Time data and gives you a general overview of what’s happeneing.

The Google Analytics Real Time Overview Report

At the top of the report Google shows a second by second view of pageviews. It then aggregates this data and shows a minute-by-minute view. I know, this view is mesmerizing. Try not to wast too much time watching this.

The top of the report also shows the total number of unique visitors on the site (it counts the cookies). Google Analytics then segments this number to show you the amount of new visitors and returning visitors.

Moving deeper into this report we get information about what’s the most popular content right now, where visitors came from (both traffic source and keywords) and a geographic breakdown of where the visitors are located.

With this one report we’re able to answer the business questions we posed above.

Real Time Locations Report

The location report has basically the same geo data as the Overview report. It still show second-by-second data and a breakdown by Country.

The Google Analytics Real Time Locations Report

One thing that is different is the ability to view geography using Google Earth. Does this offer any additional insights? HELL NO! But it looks sexy. The Google Earth plugin will zoom you from from city to city.

Oooooooo! Zoom from city to city with the Earth View

Real Time Traffic Sources Report

The real time traffic sources is literally a dump of your top traffic sources. Again, not too different than the data on the Overview, the tabular data shows the Medium and Source dimensions.

The Google Analytics Real Time Traffic Sources Report

This is especially handy when you launch a new campaign and want to see how it’s working. Think about when that big tweet goes out and you want to see what’s happening on the site.

Or consider the case where you might have an unexpected spike in traffic. This report will help you understand if there is some referring site that is driving a lot of traffic. We still can’t see if there is a lot of conversions (grrrrrr) but at least we can see traffic.

Real Time Content Report

And finally we have a dedicated real time content report. Again, not much new here. You can view how many active users are viewing a specific page or get an agregate view of pageviews over the last 30 minutes.

The Google Analytics Real Time Content Report

Notice how some of the data rows are shaded green and red? Green indicates a row of data that has increased while a row of data that is red indicates a row of data that has decreased. This is a nice touch to make it easier to see things “move.”

So there you have it. Google’s new Real Time feature for Google Analytics. How do you plan to use this to optimize your business?

Google Analytics Real Time: Real Time Data for Real Time Decisions is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Adding Business Data to Google Analytics Data
2. An Analysis of My Data
3. Event Tracking Pt. 1: Overview & Data Model

Data integration is nothing new, companies have been doing it for a long time. I’ve written in the past about integrating Google Analytics with CRM data.

Other analytics vendors have been supporting this functionality for a while. But I think as more organizations move to Google Analytics they’re looking to get the same functionality as their other tools. Hence this uptick in data integration.

So I figured this would be a good time for a high-level overview of the most common way to integrate Google Analytics with a data warehouse.

What is a Data Warehouse?

In reality a data warehouse is just a big database that pulls from lots of different sources. A businesses would build a data warehouse to do more complex types of analysis. Other types of data you might find in a data warehouse is customer data, purchase history information or demographic information.

When all of this data is put together you can do some interesting types of analysis. For example, you can do better analysis about online behavior and offline conversion. Or tie long-term purchase history to online behavior.

Another benefit of building a data warehouse is the ability to create very custom marketing segments. Adding information about conversion behavior and marketing activities can help augment historical sales data and create new marketing programs.

Gary Angel wrote a great post on some of the reason why you would want to build a data warehouse. Avinash Kaushik also wrote an interesting counter-point, why you might not want to build a data warehouse.

My opinion, this is very useful, but do it in an agile, flexible way that does not take years. This stuff changes too fast to rebuild.

Almost all data integration projects revolve around a primary key. A primary key is a unique number that exists in different data sets. Because this number exists in multiple data sets you can connect the data sets using the key.

To add Google Analytics data to a data warehouse you need to add some type of primary key to Google Analytics. In most of the work that I’ve done this key is a visitor ID. This anonymous identifier usually comes from some other system like a CRM.

When we add the customer ID to Google Analytics we store in a visitor scoped custom variable. This means that the ID is stored in a cookie on the visitor’s machine. We normally set the cookie when the visitor makes a purchase or logs into the site. Basically any time we can positively identify the visitor.

For the sake of this example I’m going to use a little piece of JavaScript to create a unique ID for visitors. I’m actually going to extract a unique ID from the Google Analytics __utma cookie. Then I’m going to set the unique ID in a custom variable, specifically in Custom Variable slot 5.

NOTE: The function _ugc() is a generic function that get’s a cookie value. In this case it’s extracting part of the __utma cookie.

Once the ID is in the custom variable we should start to see data in Google Analytics, specifically the Custom Variables report. Here’s the custom var:

And here are all the visitor IDs:

I know what you’re thinking, “You can’t store personally identifiable information in Google Analytics!” But this isn’t personally identifiable information. And besides, this is the same as the transaction ID stored in the ecommerce data.

Now that we’re done debating PII, we need to merge this data with other data. This is where things get complicated. The rest of the process is all programming. And all the programming is custom. The language you use, how you store the data, it all depends on your IT department and their platform of choice. The rest of this post is meant to be a high-level overview.

First, let’s extract the data from GA via the API.

This query will generate a list of all the unique IDs stored in the custom variable. The key dimension in this query is ga:customVarValue5, that’s the value of the Custom Variable in slot 5 which holds the IDs. I’m using the Data Feed Query Explorer to test these queries.

The basic query is something like this:

https://www.google.com/analytics/feeds/data?
ids=XXXXXXXXX&
dimensions=ga:customVarValue5&
metrics=ga:visits&
start-date=2011-04-20&
end-date=2011-05-04&
max-results=50

And the data coming back would look something like this:

So where do you put this query? You need to create some type of code that runs automatically. You might pull data from Google Analytics daily or weekly. It all depends on the volume of your data and your specific needs.

Once I have a list of IDs I can create some looping logic, again part of an automated program, that iterates over the list of IDs and makes an API request for each ID number found above. This second query will get detailed information about each individual ID.

The secret to the second query is adding a custom segment that includes the ID number. Look for 1041533899 in the query below. That’s the ID number I pulled from the first API call above.

https://www.google.com/analytics/feeds/data?
ids=XXXXXXXXXXX&
dimensions=ga:customVarValue5,ga:date,ga:source,ga:medium,ga:keyword,ga:pagePath&metrics=ga:visits&
segment=dynamic::ga:customVarValue5==1041533899&
start-date=2011-04-20&
end-date=2011-05-04&
max-results=50

With this query I’m getting the data, the traffic source, keyword and all pages that the visitor viewed. I could also pull things like revenue, conversions… But I’m looking for data that I don’t already have via some other system. So I would stick to things that are happening on the site, like where the visitor came from and what they did on the site (watch a video, use a calculator, etc.)

Once this query comes back it’s time to do the data integration. You might want to manipulate the data before you store it in a database or you might choose to insert the raw data. Again, a lot of this depends on your specific environment.

The important thing is that you now have the data. Integrate.

Gotchas To Watch For

There are some definite gotchas when implementing this type of soltion. Most notably, you need to deal with data sampling applied via the API. From the Google Analytics API docs:

Google Analytics calculates certain combinations of dimensions and metrics on the fly. To return the data in a reasonable time, Google Analytics only processes a sample of the data. In these situations, the metric data returned in each entry includes a confidence interval.

So massively huge data sets might need more frequent queries. Or you might need to break the data down into multiple profiles and then query it via the API. Again, this is what makes this type of solution so custom.

Another limitation is that each query is limited to a maximum of 7 dimensions and 10 metrics. But you can get around that with some creative queries.

Thanks for sticking through this rather vague post. I know it lacks some detail, but this is a very specific technique that can differ from one client to another.

Merging Google Analytics with your Data Warehouse is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Adding Business Data to Google Analytics Data
2. Make GA Data Quality Suck Less!
3. An Analysis of My Data

When I started my job at WebShare 12 months ago I knew it was going to be a big challenge. I was joining a very talented team that was looking to expand rapidly. In fact when I talked to Corey and Dave, the owners of WebShare, about this challenge I wanted a commitment that they would do what was necessary to expand our ability to do more analytics work.

However, as all of you in the analytics industry know, growing a web analytics company is not an easy task. To scale you need more people. And finding smart analytics people is really, really hard.

But the great thing about working with Corey and Dave is that they find a way to solve problems. And boy, did then come up with a solution to help us scale.

Today we’re announcing the merger of VKI Studios, PublicInsite and WebShare to create a new analytics firm named Cardinal Path!

You can read the official release here.

Not only does Cardinal Path have more bandwidth we also have a deep skillset. From a tool perspective we work with Google Analytics, Omniture, WebTrends, Yahoo! Web Analytics, Google Website Optimize and a host of other tools that are popular today.

Our services span installation and configuration, maintenance, long-term analysis, CPC management, and various usability activities. And our new client list crosses almost every vertical from financial services to automotive to consumer products.

I can’t tell you how incredibly excited I am to be working with so many smart people. I’ve know various people at VKI and PublicInsight for a number of years and they’re all intelligent and down to earth.

To my new co-workers, I’m excited to see how we can continue to advance analytics and help our clients be more successful.

Now back to work!

Introducing Cardinal Path: An Analytics Consultancy is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Introducing Google Analytics v4
2. Introducing SiteScan
3. Introducing Urchin 6

What? You’ve never thought about analytics relationships? Well, they abound everywhere. The life of a web analyst is very much the the same as the life of a relationship counselor. We’re constantly managing the relationship between tools, data and people. Let’s take a look at some of the most common relationships.

The Relationship between Data and Business

Probably the most critical relationship in analytics is between the data we collect and the metrics we create from the data. Our metrics must be highly related to the business objectives. We’re trying to understand what’s working and what’s not working. We want to promote the things that are working and kill or fix the things that are not working.

As we all know, we call these metrics key performance indicators and almost every analytics tool has a way to track them. In Google Analytics we use the Goals feature.

We don’t want to focus on metrics that don’t help us meet our business objectives. All data should relate to:

* Increasing revenue
* Decreasing costs
* Improving customer satisfaction

We must know what metrics are important to the business so we can convert all of our data to business metrics and create a strong relationship between business objectives and the data coming from analytics.

The Relationship Between Multiple Data Sources

While we’re on the subject of data, let’s take a moment to consider the relationship between multiple data sources! We’ve got ad-serving platforms that provide pre-click data, web analytics tools provide post click data, and customer databases/CRM systems provide purchase history and off-line data.

All of this information is super helpful when doing analysis, but sometimes creating a relationship between these data sources is very hard.

Our ability to tie the data together is dependent on creating keys between the data sources. If you use Google Analytics then you’re familiar with the process of Campaign Tracking and link tagging. This is really just adding keys to the data so we can tie the pre-click campaign metrics to the post click activity on your website.

We often tie customer information to web activity using some unique ID number. The most common integration right now is linking web analytics data with CRM data. While we dream of a single key to tie offline data to online data it’s very hard to do.

The Relationship between IT and Business

Here’s a relationship between people!

If you work in a large, corporate environment you know that analytics sits between business people and IT people. Sometimes this can be… challenging, to put it kindly.

Business people want to track all sorts of things (hopefully things that actually make a difference). But getting IT resources to configure the analytics tool to track these things can hard.

Many IT teams are stretched thin and don’t want to be bothered with adding more JavaScript to some AJAX widget. I’ve literally sat in meetings where a digital marketing team would occupy one end of a table, and the IT team would sit at the opposite end of the table. Not a good relationship. Where’s the LOVE!

But the relationship between business/marketing people and IT people is a critical relationship for analytics to succeed in any organization. We depend on it to help implement and configure our tools.

Let’s take IT & marketing out to dinner and build a strong relationship so the data can keep flowing.

The Relationships in Our Community

Last but certainly not least, there is the relationship between all of us. I know, kind of sappy. But we are a pretty tight-knit group.

You’re only a tweet away from industry leaders and you can reach almost everyone via a blog. What I really appreciate is the guys that came before us have helped pave the way for all of us to succeed.

So, on this day that we celebrate love and relationship take a minute to think about all the analytics relationships in your life.

Celebrating Our Analytics Relationships is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Tracking Google Audio Ads with Analytics
2. Twitter and Google Analytics: What to Track
3. Google Tackles Campaign Attribution with AdWords Search Funnels

Throughout the discussion it has become clear that the classic first click and last click attribution models that many web analytics tools use are flawed. The problem is no one has come forward with a better solution to the attribution issue… until now.

Google has taken a very low-risk move by tackling campaign attribution for AdWords only. The new AdWords Search Funnel reports help marketers understand which cpc ads people see and click on prior to converting.

If you’re looking for details about the reports and how to use them check out the video below from Google. The new Search Funnel reports have not been rolled out yet so no one has had a chance to play with them. Hence no real description here :)

We’ve long known that people see a lot of different cpc ads during a sales cycle. Avinash Kaushik calls these keywords “upper funnel” keywords. They are used by people that are early in the buying cycle. While many of these keywords don’t always lead to a conversion they help educate a potential customer and move then closer to purchasing a product or service.

Even though they do not directly generate revenue there is some value in bidding on upper funnel keywords.

Up until now we haven’t had many ways to help us understand the true value of upper funnel keywords. Sure, we can use time on site or pageviews per visit to measure “engagement”, but that was a bit of a hack. We can also create all sorts of custom JavaScript to store the first click and last click in a Custom Variable. But again, these are just hacks.

The Search Funnel reports are a well thought out way to understand how people interact with AdWords ads prior to conversion and thus help us understand the ROI of our AdWords spend. The reorts provide insight into which keywords

I think this is a good first step by Google. They took reliable set of data that was just sitting around a data center and created some reports that will help marketers understand the real value of different types of keywords. This is all very low risk for Google with very high potential (read: more AdWords revenue).

But these new reports are also a good test of how users, and the overall analytics market, will respond to Google’s version campaign attribution reporting. Real attribution models are very complicated to create. They involve a lot of data about different types of campaigns (banners, cpc, email, etc.).

[Side note: Why is it that we haven't seen any DoubleClick data in Google Analytics yet? Pulling that data into GA will be critical for real attribution measurement.]

In addition to the data complexities, every business will have their own way to weight certain marketing activities in an attribution equation. For example, some companies may value email more than paid search. This business logic will be difficult to implement. Not impossible, but difficult.

At the end of the day the new AdWords Funnel reports are exciting. But I’m excited to see how Google takes information about how these reports are used and tackles the bigger challenge of true campaign attribution!

Google Tackles Campaign Attribution with AdWords Search Funnels is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Google Analytics Campaign Tracking Pt. 3: Reports and Analysis
2. GA On Site Search Pt. 2: Reporting & Usage
3. Google Analytics Campaign Tracking Pt. 1: Link Tagging

Google Analytics offers many different ways to segment data. Each has pros and cons but there is always a way to get the data you need…. well, almost always.

There are 6 different ways to segment data in Google Analytics:

1. Using certain reports
2. Dimension drop down
3. Report filters
4. Advanced segmentation
5. Custom reports
6. Profile filters

Bet you didn’t think there were SIX ways to segment data. :)

Using Certain Reports

Ok, you may think this form of segmentation is lame, but it’s not!

Many reports in Google Analytics are segmented by some default piece of information. There’s nothing for you to do.

For example, the Browsers report segments your data based on the different web browsers that visitors use to access your site. Google Analytics automatically identifies this information when collecting visitor data.

Other segments that are automatically include in Google Analytics include:

* Visitor type (new and returning)
* Geographic information
* Operating system (and many other “nerd” segments)

Marketing segments are not AUTOMATICALLY segmented. You need to configure Google Analytics to track campaigns (i.e. link tagging) in order to get correct traffic source segmentation.

Pros:
* Easy

Cons:
* You better make sure you’ve got your campaigns tagged correctly ;)

Dimension Drop Down

Many reports have a dimension drop down that allows for segmentation right in the report. This is a handy way to quickly drill down into a piece of data.

For example, let’s say I want to see the most popular landing pages in a particular state. I can navigate to the state in the Visitors > Map Overlay > Regions report, click on the state I’m interested in, and then choose Landing Page from the Dimension drop down.

You can see in the image above that you can segment based on campaign information, some technical information and some visitor information (visitor type, language).

Overall, this is a good way to go when you’re drilling down and want to segment a single data point by some dimension.

Pros:
* Quick for one-off segmentation

Cons:
* Can trigger sampling
* Limited number of dimensions and no metrics
* A pain if you need to segment a lot of things, like top landing pages for every US state

Report Filters

Bet you don’t think of filtering as segmenting, but it is!

Any report displaying tabular data has a filter tool at the bottom of the data. This let’s you quickly view data that matches, or does not match, some condition. The condition is the pattern, or regular expression, that you enter into the filter. Using a regular expression you can add lists to the filter.

Here’s an example. Suppose I want to quickly view traffic coming from the Pacific sales region. I can apply the following filter to the Visitors > Map Overlay > Regions report:

California|Oregon|Washington

[The above is a regular expression matching California OR Oregon OR Washington]

Notice that the Scorecard (the top row of data in the table) indicates how our segment, i.e. the data that matches our filter, compares to the overall site? We can now compare the Pacific sales region to the entire site.

And here’s a neat trick, if you add the filtered report to your dashboard the filter will persist in your dashboard widget. I call it a sticky filter.

Pros:
* Quick and relatively easy
* Can be applied to historical data
* Will not trigger sampling

Cons:
* Restricted to one report and the data in that report
* You should know some basic regular expressions
* Can not be shared easily

Advanced Segments

There has been a lot of conversation over the last few months about Advanced segments and rightly so. This analysis tool is really powerful and let’s you slice the data many different ways using different dimensions and metrics. Want to see all visits that generated more than $100, coming from paid search and occurring after 8 AM? No problem with an advanced segment.

But there are some downsides. First, sampling. Because Advanced segments re-process data in real time there is a sampling algorithm applied to minimize the load on Google’s servers.

You can’t segment more than 200k visits. If sampling is applied you’ll see a confidence interval next to your data.

Again, the problem is that small segments of data will be really inaccurate when the sampling algorithm is applied. There is no way to disable sampling.

The most common ways to get around sampling are segmenting using profile filters or potentially a report filter. It really depends on the exact situation.

The second issue is that not all reports can be segmented. Due to the segmentation technology certain reports can not be segmented, like the Absolute Unique Visitors report and the Funnel visualization report. Those reports can only be segmented with profile filter (see below).

Pros:
* Can be applied to historical data
* LOTS of flexibility, can segment based on a huge number of dimensions and metrics using different combination of both

Cons:
* Sampling will be applied if trying to segment more than 200,000 visits
* Not all reports can be segmented
* Specific to your username, can not be shared with other users

Custom Reports

Another beta feature that can be used for segmentation is the Custom Reporting tool. This tool is more than just pretty reports. It allows you to create 5 levels of segmentation in a report.

In a previous post I talked about segmenting campaigns by time of day to better understand day parting.

We could take that example one step further by adding geographic region to the report. The result would be a report that has Campaigns data that could be segmented by time of day and then by geographic location.

The problem is that not all dimensions can be used together. The reason is that only certain metrics are related in the Google Analytics data architecture. You can find a complete list of combinations in the GA support docs.

Pros:
* 5 levels of segmentation
* Advanced segments can be applied to a custom report
* Can be shared using automated email feature

Cons:
* Can only segment using dimensions, not metrics
* Limited number of dimension combinations
* Can only drill into one data point at a time

Filtered Profiles

Filtered profiles are the nuclear bomb of segmentation. They are permanent, segment every report in a profile, and can easily be shared.

In case you’re not familiar with filtered profiles, you can include and exclude data from a profile using a filter. Google applies the filter during data processing, thus segmenting the data.

Once the data has been processed it can never be changed. This means that you can filter historical data AND if you mess up the configuration of a filter you could have really crappy data.

Another issue with filtered profiles is not all data can be filtered. For example, transactional data is different than pageview data. This can cause some funky information in campaign reports and commerce reports.

If you need to filer pageview data then you’ll need to filter your commerce data with different include or exclude filters. Also note that Event data can not be filtered.

But, on the up side, you can use filters to segment things like Absolute Unique Visitors and the Funnel Visualization report. Neither can be done with an Advanced Segment.

Pros:
* Segmentation of every report in Google Analytics
* Can control access by assigning users to filtered profiles

Cons:
* Only effective from date of implementation forward
* Limited number of dimensions
* Issues with filtering other types of data, like events and transactions

I hope this inspired you to come implement different segments using different techniques. As I said in the beginning, there are a lot of ways to slice data in Google Analytics. Find the technique to suit your needs and start segmenting!

Segmentation Options in Google Analytics is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Google Analytics Advanced Segmentation
2. Google Analytics Cross Segmentation: Something You Should Know
3. Custom Segmentation with Google Analytics

In the past few weeks I’ve gotten a lot of questions about how to track email with Google Analytics. While I did cover the broad topic of online ad tracking in a previous series of posts, email tracking has certain nuances that I think should be addressed.

The Concept

Tracking email campaigns in Google Analytics is done using a process called link tagging. This process is the manipulation of the links in your emails. Here’s a sample link that might appear in an email:

http://www.mysite.com/page.php

To track it with Google Analytics it would be modified like this:

http://www.mysite.com/page.php?utm_campaign=fall-sale&utm_medium=email&utm_source=female-list

And another email link that looks like this:

http://www.mysite.com/page.php?prodid=100

Should be modified like this:

http://www.mysite.com/page.php?prodid=100&utm_campaign=fall-sale&utm_medium=email&utm_source=female-list

When someone lands on your site after clicking on a tagged link, GA removes the information from the URL and stores it in a cookie. Because the info now resides on your machine (in the cookie) GA can associate all visitor actions (like conversions and transactions) with the email. Pretty slick, huh?

How Link Tagging Works

What is all that info I added to the URL? They’re called link tagging parameters. The name of the parameter is on the left side of the equal sign and the value of the parameter is on the right side.

Each parameter represents a different attribute of your email. Looking at the example above we can identifiy the following parameters and their values:

utm_campaign=fall-sale
utm_medium=email
utm_source=female-list

Each one is identified by the Google Analytics tracking code and helps GA understand that the visitor arrived on your site via an email.

You must use the parameters that Google provides. However, you can specify any value for each parameter. This is where the real power lies. By using your own values for each parameter you can add markting information, that is specific to your business, to GA. We’ll get to where that information appears in a second.

[ NOTE: All you advanced user may be calling my bluff here. You can rename the link tagging parameters that GA uses, but it is an advanced technique that requires a change to the GA tracking code. I'm not going to cover it in this post but you can learn more in the GA help section. ]

Let’s look at each link tagging parameters and some of the logical values for each.

utm_campaign

This parameter identifies the marketing campaign that the email belongs to. It may be that this email is just one part of a bigger online marketing strategy. For example, you may be using paid search, some display advertising and this email to reach new prospects. You can group this email with other marketing activities by using a common value of utm_campaign.

As for suggested values, use something that represents the campaign that your running.

utm_medium

The medium parameter describes how the message got the to visitor. In the case of email I recommend that you always use the same value. I like to use ‘email’. It’s short and pretty darn descriptive.

Using a single value consolidates all email generated traffic into a single line item in the reports.

utm_source

This is where things get interesting. Traditionally, in link tagging, the source is the ‘who’ attribute. It describes who you’re working with to push a message out. But how does the concept of ‘who’ map to an email?

When it comes to email I like to think of the ‘who’ as the list of recipients that you’re sending the message to. This may be a segment of your email list (like a specific gender segment, age segment of purchase history segment) or your entire email list. For example, some potential utm_source values might be:

utm_source=gender:female
utm_source=gender:all
utm_source=purchase:last-30-days
utm_source=purchase:last-60-days
utm_source=purchase:free-shipping-offer

The key here is that by identifying the segment in the utm_source parameter you’ll be able to measure the performance of that segment in GA. You are segmenting your email list, right?

utm_content

The final parameter is named utm_content and helps us test emails. The content parameter identifies the actual content of the email. So if you’re producing different versions of the email for an A/B test you can mesaure the performance of each by varying the value of utm_content. For example:

utm_content=free-shipping-offer
utm_content=20-off-offer
utm_content=product-creative
utm_content=value-creative

Some folks like to use utm_content to describe not only the version of the email that the recipient received, but also the actual location of the link in the email.

utm_content=top-nav
utm_content=call-to-action
utm_content=image-link

Sometimes this can be overkill as it leads to a lot of very granular data. Normally we just use this to measure which email variation performed better.

Think about how powerful this can be. Using utm_content and utm_source you can measure the performance of a specific message to a specific segment of your customer base (i.e. email list). This is a great way to measure if you’re sending the right message to the right person!

How to Tag Your Links

So now that we know what paramters we can use to track our email, how do we actually tag the links? It starts by assigning a value to each parameters. You could use the Google Analytics URL builder: a free tool in the GA help center. Just enter a value for each parameter, along with the URL from your email, and the tool will automatically generate a tagged URL that you can place in your email.

But I find the URL builder can be cumbersome when tagging a large number of links. Just think of all the links that you might have in a single email!

Instead I use a small Google Spreadsheet that has a built in formula. Just enter your campaign values in the columns, along with the URLs from your email, and drag a pre-programmed formula to automatically created your tagged URLs. Then place the URLs in your email.

You may have noticed that a tagged URL is pretty ugly. If you’re sending an HTML email to you can hide the long URL using an anchor tag, but if you’re using a text based email the recipient will see the entire crappy URL. Try using a service like Tiny URL to hide the query string parameters.

I should note that some email platforms (the cool ones!) have begun to integrate GA link tagging into their tools. Check with your email provider to see if they offer this service.

The Reporting

As I mentioned before, the values used in your link tags get pulled directly into Google Analytics. Each parameter becomes the foundation for a report. Let’s start with the Traffic Sources > Campaigns report:

This report lists all the values of your utm_campaign parameters. You can measure the performance of your email campaigns by finding the values you use for utm_campaign. But be aware, this report will also contain the titles of your AdWords ad campaigns. They’re automatically imported from AdWords. Also remember that you might use the same value of utm_campaign in activities other than email.

Remember utm_source and utm_medium? We can drill into a campaign to determine how the email medium, for a specific source, performed in the campaign. Select a campaign by clicking on the name. Then use the dimension drop down to view all the sources within the campaign.

The above report shows just one source within this campaign, but that’s all that was used. The important thing to understand is how you can see certain sources, specifically email segments, contributed to the success of a campaign.

But what about evaluating a source across multiple campaigns? Try using the Traffic Sources > All Traffic Sources report:

The first column shows all sources and mediums, so in our case we can see how a segment of the email list performed cross all campaigns. We can quickly filter this report by ‘email’, the medium, to identify how well a segment performed. Remember how

What about the utm_content parameter? Where can we find that data? It’s in the Traffic Sources > Ad Versions report.

Here’s where we can evaluate the performance of our different email variations. The Ad Versions report not only contains the values from utm_content, but also the titles from your AdWords campaigns. This is another piece of data that GA automatically pulls in.

And let’s not forget that all of these reports have three tabs full of metrics: site usage, goal conversions and ecommerce (if you choose to use ecommerce tracking). All of these metrics provide insight into the sales or conversion process.

Bounce rate provides insight into the begining of the process. A high bounce rate probably indicates a disconnect between the message in the email and the content on the landing page.

You can quickly switch to the goal conversions tab to measure the other end of the process by looking at the conversion rate for your email. And if you’re using the ecommerce tab you can look at a metric like revenue to qualify the conversion rate.

Don’t Forget the Pre Click Data

While all this data is great, don’t forget that your email provider has a number of metrics that give insight into what happened before the visitor arrived on your site. Such metrics include # emails sent, # emails received, # bounces, # emails opened and click throughs.

I know that metrics like open rate are inherently flawed due to the tracking technology, but you can’t evaluate things like subject line effectiveness using the data in GA. Don’t be afraid to look at metrics like # of bounces when evaluating the performance of email.

Create your Advanced Segment

With GA’s new Advanced Segments we can really drill into the email traffic segment. At the very least, you should create one advanced segment to evaluate email traffic.

To create the advanced segment use the ‘medium’ dimension and enter a value of ‘email’. Remember, ‘email’ is the value we used for utm_medium in the link tagging. Talk about coming full circle!

Using an advanced segment helps you easily identify what content the email segment found interesting, if they converted, how well the progressed through various processes, etc.

Common Problems

The most common problem we see with link tagging is that people forget to tag their links. Link tagging is usually a process related issue, not a tech related issue. Before your organization sends any email communication make sure the links are tagged.

A simple way to test your links is to send the email to a few coworkers and ask them to click on some links. In a few hours you should see the data in your GA reports.

The second most common problem has to do with redirects. Many times a site may have a redirect that strips off the campaign tracking parameters. The simple test mentioned above should tell you if you have a redirect issue. Remember, when you click on a tagged link you should see your link tagging parameters in the URL of your site.

A Note on Privacy

A few people have mentioned that it is possible to add a visitor’s email address to your GA data using link tagging. While this is possible, keep in mind the GA terms of service specifically forbids the collection of personally identifiable information with Google Analytics.

If you’re still reading, and you’re trying to understand how to track other types of online ads, then you may be interested in these posts:

• Google Analytics Campaign Tracking Pt. 1: Link Tagging
• Part 2: EpikOne Link Tagging Tool
• Part 3: Reports and Analysis

Tracking Email with Google Analytics is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Google Analytics Campaign Tracking Pt. 1: Link Tagging
2. Give Me What I Want and I’ll Do What You Ask
3. Google Analytics Campaign Tracking Pt. 3: Reports and Analysis

Below is a list of all standards defined in the WAA metrics definitions document and GA compliance with each definition. GA is compliant with 19 of the 26 metrics. Most of the non-compliance is due to the fact that GA does not offer all the metrics that the WAA defined.

Google Analytics Compliance with WAA Standard Metrics is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. How Does Google Analytics Track Conversion Referrals?
2. GA On Site Search Pt. 2: Reporting & Usage
3. Tracking Google Audio Ads with Analytics

But I believe that many of us, myself included, are really missing the point when it comes to Google Analytics. I don’t think that we fully embrace the paradigm shift that Google is pushing with GA. Let me explain…

By releasing Google Analytics for free, Google has thrust web analytics into the mainstream. This is great for our industry, but it places a heavy burden on Google. They must provide a product that makes analytics easy.

But is web analytics easy? That’s debatable, but the simple truth is that there are millions of people out there getting into web analytics via GA and Google needs to help them understand their data.

Paradigm Shift #1: use GA to do more analysis.

Google Analytics is designed to facilitate the understanding of click stream data. How? By providing an interface that helps people explore their data by providing lots of context. All of the time spent redesigning GA was for one reason: to create a product that makes analysis easy for everyone. That’s a rather lofty goal but it is analysis most critical part of being a web analyst. If GA can help us understand what’s happening with our website then we can take action.

Have people embraced this idea? To some extent, yes. Many users I talk to like the features in GA and feel that it is an effective tool for analysis. But there is a huge number of users that still do not understand or know about many of the features in GA. Believe it or not, people still don’t know that you can track different types of online advertising, not just AdWords. A large segment of people use GA to track “hits” to their site.

Let me caveat this by saying that there are some analysis tasks that are just not possible in GA. However, these tasks are usually more advanced analysis techniques, like applying a control limit or doing a long tail analysis. Perhaps the GA team will ad advanced features as the product matures with the market.

Paradigm shift #2: less formal reporting, more pestering!

Just as important as understanding the data is the distribution and access to the data. Google has tried to facilitate this need in two ways.

First, access to Google Analytics is pretty easy. Anyone with a Google Account can get access to a GA account. Three fields and two clicks and you can add someone to your analytics account. It’s that easy. And, to the best of my knowledge, there is no limit to the number of users. Now Joe in IT can access the data along with Kathy in marketing.

Second, the new GA redesign included email reports. While this does not seem like a revolutionary feature, the ability to distribute actionable data, to anyone, at any time, helps drive change. Why export data, create a report and then email it when you can email it right from GA? You can pester your co-workers and contractors (who are the ones that usually need to take action on the data) with the information they need to make change.

I don’t think that many people have really embraced GA’s email reports feature. Multiple people a week ask me how to get data out of GA so they can create elaborate dashboards or other reports. They try to force GA into their current reporting framework rather than trying to change. I’m not saying this is wrong, but I think we need to give GA’s features a chance.

Parting Thoughts

While Google Analytics is not the end-all be-all web analytics application, it is a solid foundation that will continue to evolve and meet the needs of a growing and maturing user base. The big question is, will we embrace the change that many of GA’s features support, or will we continue with analytics as usual?

Think I’m off the mark? Have I consumed too much of the Google Analytics Kool-Aid? Leave a comment below!

Do we really “get” Google Analytics? is a post from: Analytics Talk by Justin Cutroni

Related posts:

1. Breaking Up Is Hard To Do: GA Account Setup Tip
2. Track your Google Analytics Changes
3. Welcome to the New Google Analytics