Let’s walk through the measurement planning and implementation for a fictional non-profit called NPX (Non-Profit X).

To create a measurement plan I use a process of interviewing stakeholders and documenting data needs that I learned from Avinash Kaushik. You can read about his framework in his post on digital measurement. Thanks Avinash for all you contribute to our community.

Everything starts with the organizational mission, once we know that we can investigate the strategies and tactics used to execute that mission. All along the way we define pieces of data that helps us measure the performance.

Every non-profit needs to define a measurement plan to better understand their performance.

Let’s get started.

The mission or business objective for our fake non-profit, NPX, is:

“Improve the lives of those that are in need of stable, safe housing.”

The organization aims to provide affordable, stable housing to those that can not afford it. They believe that having a safe, stable living environment leads to success.

It’s important to know the mission because that’s what you want to measure: improved lives. The last time I checked that metrics does not exist in any tool :) But we will use that to define success for the strategies and other tactics.

But how does the organization go about accomplishing that objective? We identify the strategies by interviewing the people at the top. Board members, executive, etc. It can take more time to schedule a meeting with these people than actually meeting with them :)

They have three primary strategies:

1. Fund homebuilding projects by generating donations
2. Coordinate volunteers to build houses
3. Raise awareness of our cause

Identify the strategies that your non-profit uses to fulfill it’s objective or mission.

Anyways, from our strategies, we identify the main tactics to execute those strategies. To make things simple let’s make all of their methods web based:

1. Collect donations via a website
2. Recruit volunteers via a website
3. Educate public about the lack of housing via an informational website

A non-profit can have one, or more, tactics to achieve each strategy.

These all make sense right? I want to point out the strategies don’t really change over time. But the methods, or tactics, that NPX uses to implement those strategies change. For example, before the web NPX probably used direct mail to contact donors and generate donations. They might have used broadcast media, TV and radio, to raise awareness in the regions where they operated. The tactics were different, but the strategies the same.

Keep in mind you need to know today’s strategies & tactics and in the future. Technology changes – we in the measurement business know that! Knowing future strategies & tactics will help you plan your future measurement plan.

Now we come to the good part – creating metrics, also know as KPIs, to measure the tactics.

You don’t need a lot of KPIs for each tactic, maybe 3 to 5 for each. Here are the KPIs for the tactics we identified for NPX:

After documenting the tactics for each strategy define 3 to 5 KPIs to measure the performance.

To me, identifying KPIs for donations is easy – it’s all about money! In this regard its almost like an ecommerce site: revenue and avg. donation value.

Just a note here, sometimes people will use a segmentaiton of revenue as a KPI. For example, they might use recurring revenue, or donations. But I think that’s best left for the next section, segmentation.

Now we move on to KPIs for volunteers. Remember, the strategy is to get people to volunteer and the tactic is to help people find volunteering activities in their local area and sign up via a form. There’s also a volunteering newsletter that you can sign up for.

I want to track the number of people who are willing to volunteer today and contacting NPX to volunteer at a later date. I also have a micro conversion here: people that search for a volunteer opportunity.

1. Number of Volunteers
2. Number people willing to volunteer in the future
3. Number searches for volunteer activities

The last set of KPIs are for raising cause awareness via an informational website. This reminds me a little bit of measuring “brand engagement”.

NPX has a number of digital ways that they promote awareness of their cause. They have a series of videos that are important for people to use and they have a special tool on the site where users can submit letters to congress.

1. Submit congressional letter
2. Watch video
3. Frequency & recency to the blog

There are a lot of other KPIs here, like connect with NPX via social, newsletter, etc. and share content via social media. I just didn’t have room for all of those things in the diagram.

I’d also recommend that they use my technique for tracking how many people actually read their content.

Creating KPIs is a bit harder than it looks. Not only do you need to understand the “upper” part of the measurement strategy (objectives, etc.) but also the “data landscape” and capabilities of the tools.

Once we have our KPIs we need to identify segments. Segmentation, which is the foundation of all analysis, helps us understand why our KPIs move up, down or stay the same.

It’s critical to define the segments that a non-profit will use to analyze their KPIs.

For the donation strategy we want to split revenue by the marketing activity that generated the revenue. This helps us calculate an ROI. We also want to segment donation based on the donation type (amount of the donation). I also want to identify repeat donors as their behavior is _very_ different than first-time donors.

So my list of segments looks like this:

1. Marketing Activity
2. Donation Type
3. Repeat Donors

Segmenting the volunteer KPIs is similar. I want to see how different marketing campaigns perform. I also want to segment repeat volunteers from fist-time volunteers. NPX volunteer opportunities are geographically based – so let’s segment based on geographic location.

1. Marketing Activity
2. Volunteer type
3. Geographic location

Next is the cause awareness. I’m going to start by segmenting by traffic source – big surprise! I spend time and money marketing for my cause so I need to measure that. Visitor type. This is a combination of the above. I want to know if people that are converting at other goals are engaging with my content. Next I’m going to look at the content type that people are engaging with. This might be video, text, etc.

1. Marketing activity
2. Visitor type (donator, volunteer)
3. Content type

Now that we have segments it’s time to add some context to the data in the form of targets.

Targets are my favorite form of context. It’s how all almost every business evaluates their performance. Every organization, at the beginning of the year, lays out where they want to be every month or quarter (depending on how they run).

Defining targets for each of your KPIs and segments provides context to the performance of your business.

Then as the year proceeds you can compare where you’re at with where you need to be. You can also account for upcoming activities when determining if you will, or will not, make your targets.

That’s it, the plan is done.

You probably notice something – once you have a measurement plan reporting and analysis becomes almost easy. The metrics and segments you need to analyze are listed right in the plan. Sure, you might need to design some pretty reports and dome dashboards, but most everything you need is identified in the plan.

Given this fact, let’s ask the question – what is analysis?

If you meet or exceed your targets you should ask yourself “Why did it work?”

If you miss your targets you should ask yourself “Why didn’t it work?”

Many times looking at the performance of your segments, and applying additional segmentation, will lead to the answer.

Translating this plan into an analytics implementation, or a Google Analytics implementation, can be some work.

Implementation Planning

Most of the data you need for the above plan will come a fairly basic implementation of Google Analytics. Of course, the devil is always in the details. But just by tagging your pages you get lots of information.

To make reporting easy, you can track the KPIs with the Goals feature. Remember, you get 20 goals, so there are more than enough for all KPIs across the various business strategies.

Segments are a bit harder. Some segments, like Geography, are automatically generated by Google Analytics.

Other segments, like marketing channels need to be created using Campaign Tracking.

Am important segment in our plan is donation type. I recommend that non-profits use the ecommerce tracking feature to track donations. Not only will you get revenue, but you can track each donation type as a different product.

Note: If you’re using Universal Analytics the ecommerce implementation is a bit different.

Another feature that’s really important is custom dimensions (formally custom variables). This feature lets you segment users based on data from other systems. For example, when someone submits a donation you can set a custom dimension that indicates the user is a donor. When someone volunteers via the website you can identify them as a donor. Both are important segments for our measurement plan.

It takes a little extra work, but it’s well worth connecting data from your system back into analytics. It’s provides powerful segmentation.

Other Technical Considerations

Many non-profits, especially small ones, use third party systems to collect donations. These systems require cross-domain tracking to correctly attribute donations to their marketing source.

I’m not sure if they support it today, but hopefully, someday, all of these platforms will natively support cross-domain tracking.

While they’re at it, it would also be cool if these platforms automatically implemented custom dimensions (or variables) for the sites they manage.

Can you tell I want all platforms to make implementation easier :)

I’d also encourage all non-profits to look at the Remarketing feature in Google Analytics. This innovative tool let’s you segment visitors to your site and engage with them over the Google Display network, this can be a very valuable way to keep them moving towards conversion.

Read more about getting started with Google Analytics remarketing.

Reporting & Analysis

We’ve covered the features to get the measurement plan implemented. Here are a few other non-technical things you can do to make your life easier:

Use a custom dashboard to monitor KPIs day to day. If you’re looking for a template, here’s a Google Analytics Dashboard for non-profits that I created for the plan above. Remember, you need to have ecommerce implemented to track donations and goals configured to track KPIs. Plus I’m using Custom Variables to segment the user types.

Start thinking about multi-channel analysis and multi-channel funnels. Learn how to use channel groupings to get a deeper understanding of how your marketing activities work together to generate conversions.

So that’s about it. I hope this helps some non-profits plan and implement a measurement strategy that’s consistent with their mission.

And while I’m at it, thanks to all those that tireless work to help others. It’s a noble calling.

Measuring the Non-Profit: From Planning to Implementation is a post from: Analytics Talk by Justin Cutroni

The post Measuring the Non-Profit: From Planning to Implementation appeared first on Analytics Talk.

How Ecommerce Tracking Works

In general, Universal Analytics ecommerce tracking functions the same way as the standard Google Analytics ecommerce tracking. Google Analaytics collects the transaction data using a special JavaScript tag that you need to place on your thank you page.

The hard part of tracking transactions is you need to insert the transaction details into a special ecommerce tag. That means you need to add some server side code to properly format the data.

The big difference between the current Google Analytics ecommerce tracking code and the Universal Analytics ecommerce tracking code is the format of the transaction data on your thank you page.

Step by Step Instructions

First, unlike the original emcommerce tracking code, you need to add an extra line of JavaScript to your thank you page. This code will load a special ecommerce tracking library to your thankyou page. This library contains all the code necessary to track a transaction.

ga('require', 'ecommerce', 'ecommerce.js');

For all you nerds, the reason that the ecommerce code is in a separate file is to reduce the overall size of the analytics.js file. This makes your website load faster and improves the overall experience for your visitors.

The above code is in ADDITION to the standard page tag that you need to add to every page on your site.

Next, you need to add the transaction details to your thankyou page. Remember you need to create server side code that formats the transaction in a special way:


ga('ecommerce:addTransaction', {
id: '98765', // Transaction ID - this is normally generated by your system.
affiliation: 'Sugarbush Mt.', // Affiliation or store name
revenue: '89.00', // Grand Total
shipping: '0' , // Shipping cost
tax: '5.99' }); // Tax.

ga('ecommerce:addItem', {
id: '98765', // Transaction ID.
sku: 'LTAFD', // SKU/code.
name: 'One Day Adult Lift Ticket', // Product name.
category: 'Lift Tickets', // Category or variation.
price: '89.00', // Unit price.
quantity: '1'}); // Quantity.

ga('ecommerce:send');


There are three parts to the ecommerce tracking code.

The first section in the transaction data. This is summary information about the total transaction.

The second section contains information about each item in the transaction. In the code above there is only one item, it has SKU LTAFD. If the customer purchased multiple items then there would be multiple item sections in the code. There needs to be one item section for each specific item purchased.

The final part of the code is the send line. This single line of code actually sends the data off to Google Analytics.

For all the nerds, the data is sent by an image request back to the Google Analytics servers.

Ok, let’s clearly define what each piece of data actually means.Here’s a short description of each part of the transaction data.

• ID: This is the transaction ID. It’s normally generated by your system. It should be unique for each transaction.
• Affiliation: In reality this is an open field that you can use to store any piece of data. This field is supposed to store any affiliate information, or store name. But you can basically use it for anything. I’ve seen people use it to store the name of a coupon.
• Revenue: An important metric for any ecommerce business. This specifies he total revenue associated with the transaction. This value should include any shipping and tax costs.
• Shipping: Any shipping costs associated with the transaction.
• Tax: Any tax associated with the transaction.

Now let’s look at the data associated with each individual item in the transaction.

• ID: This is the same id used in the transaction section. Google Analytics uses it to link the items in the transaction to the actual transaction.
• sku: Stock-keeping-unit. From the all-knowing Wikepedia: “SKU can also refer to a unique identifier or code that refers to the items or products they have available for sale. Each SKU is attached to an item, variant, product line, bundle, service, fee, or attachment. SKUs are often used to refer to different versions of the same product.”
• Name: A human-readable name for this product. This will be visible in many of the product reports.
• Category: The category that this product belongs to. Google Analytics can only assoicate one category to each product.
• Price: The price of an individual unit of this particular item.
• Quantity: The quantity that the customer purchased in this transaction.

Ecommerce Tracking Example

Ok, that’s a lot of techno-talk. Let’s look at a real-life example. Suppose I own a a bag company and someone completed the following transaction:

Transaction Details:
Transaction ID: a23nm45ys23
Total: $175.00
Tax: $0.00
Shipping: $25.00

Items Purchased in the transaction:
12345: one, Black 17″ Rollerbag, $100.00, SKU = RB17, Category = Luggage
45678: one, Black 15″ Rollerbag, $75.00, SKU = RB15, Category = Luggage

Here’s how I, as the business, would need to format the data in the thank-you page:

Here’s how the Universal Analytics ecommerce tracking code might look for a purchase.

Notice how there are two sections for addItem? That’s because there were two items in the transaction. Remember, you need an addItem for each item that the customer purchased.

Don’t Forget Tag Management

If you want to future-proff your implementation, you might want consider adding your ecommerce data to a Data Layer. A data layer is data about your visitor and visit stored in a standardized way. By placing your data in a data layer you can switch between tracking technologies easily.

To track transactions using Google Tag Manager make sure the transaction data is added to the GTM Data Layer.

Creating an Ecommerce Tracking Plan

Implementing ecommerce tracking code is a critical measurement step for any ecommerce business. But it’s only one part of the tracking story. You need to consider collecting other information, like the number of customers vs. number of browsers using your site. You need to have a bigger measurement plan.

Check out some ideas for customizing Google Analytics to better measure your ecommerce business.

Tracking Ecommerce Transactions with Universal Analytics is a post from: Analytics Talk by Justin Cutroni

The post Tracking Ecommerce Transactions with Universal Analytics appeared first on Analytics Talk.

Note: if you’re a beginner, and you just want to get started, all you need to know is this: get the Universal Analytics tracking code from the Tracking Info portion of the Admin Section.

Place the Universal Analytics tracking code on every page of your site.

After you copy the code, past it into the HEAD section of your website. If you use some type of template just add it to the template.

Put the Universal Analytics tracking code in the HEAD section on all the pages of your site.

Once you’ve added the tag to your site you should start to see data in the Real Time reports. Google Analytics processes data about every 4-ish hours. So the standard reports will be populated about 4 hours after the code is install. But it can sometimes take up to 24 hours for data to appear.

Using the basic tag you’ll get a lot of data including:

• Visitor count
• Number of visits
• Pageviews
• Geographic location of your visitors
• Referral information
• Device information

For those that want a little more insight into how Universal Analytics works for a website, keep reading.

The Universal Analytics Page Tag

Like the previous version of Google Anlaytics, Universal analytics uses a JavaScript page tag to track a website. Here’s what the tag looks like:

< script >
(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,'script','//www.google-analytics.com/analytics.js','ga');

ga('create', 'UA-YYYYY-XX', 'cutroni.com');
ga('send', 'pageview')
< /script >;

NOTE: The tag for your site will look different. Specifically the account number, which is UA-YYYYY-XX will be different, and the domain name appearing after the account number will also be different. More on this below.

The page tag consists of two parts.

1. First, a request to the Google Analytics servers for a code library. It’s named analytics.js.

(function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
(i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
})(window,document,'script','//www.google-analytics.com/analytics.js','ga');


For you coding nerds, you’ll notice that the library has changed names, from ga.js to analytics.js.

One difference that you can not see is that analytics.js does NOT contain any ecommerce code. That code is stored in a separate file that you use when you track a transaction. By splitting out the ecommerce code it helps the tracking code load faster on your site and speeds up your site (a little bit).

Also notice that new tracking code is asynchronous, just like the previous tracking code. This means that your website page will load while the Google Anlaytics tracking code is loading. You don’t need to worry about the code slowing down your page and causing a bad user experience.

2. The second part of the code is a list of actions.

ga('create', 'UA-YYYYY-XX', 'cutroni.com');
ga('send', 'pageview')


There are two actions.

First Google Analytics connects this tag to your account. That’s done via the create command. It creates something called a tracking object to connect the data that is sent to your account on the server.

Once the tracking object is create the code sends a data hit, in this case a pageview, back to Google Analytics.

You’ll also notice that the syntax has changed. There are no longer functions like _trackPageview. Now you ‘send’ or ‘create’ hits. Each type of hit has a different commend.

To track a website add the page tag, which is ALL of the code above, to all of your pages. To get the most accurate data, you should add this code to the HEAD section of your site.

Universal Analytics Tracking Cookies

Universal Analytics uses a single, first-party cookie to track visitors.

To track users on a website Universal Analytics uses a first party cookie, just like the previous version of Google Analytics. The big difference is that Universal Analytics uses ONE cookie while the previous version uses four (and sometimes five) cookies.

This reduction of cookies means that your site should be a bit (a tiny bit) faster.

The new Universal analytics cookie is named _ga and looks like this:

_ga=1.2.838838867.1359565579161

Note: Did you know you can actually rename the tracking cookie in Universal Analytics?

The main reason that there is a cookie is to identify the user. This is done by generating an anonymous, random number and storing it in the cookie. This number is sent on every hit to the Google Analytics server and is used to calculate visits, visitors and campaign metrics.

The cookie will last on the visitor’s computer for two years. The expiration date will get pushed back two years every time the visitor hits the site. You can change the duration of the cookie using JavaScript.

For the nerds, changing the cookie expiration is different than changing how long a duration will last. Visit duration and timeout is configured in the Admin section of Google Analytics.

It’s also important to note that this cookie will work for all of sub-domains of your website. In the previous version of Google Analytics you needed to change the code to track users across sub-domains. You do NOT need to change the code in Universal Analytics.

This can be a lot to absorb, but one more thing :)

You may want to consider using Google Tag Manager to implement Universal Analytics.

A tag management tool, like Google Tag Manager, helps you avoid any coding changes to your site. You basically manage all your tags from a handy web interface.

You can also use Google Tag Manager to track events, ecommerce transactions, etc. In general, using GTM, or any tag management tool, is strongly suggested.

If you do decide to use a tag management tool, don’t forget to consider adding a data layer.

That’s about it for the basics. But there are a lot of different types of configurations that can help you customize the data for your business. These are fodder for another post.

Tracking Websites with Universal Analytics is a post from: Analytics Talk by Justin Cutroni

The post Tracking Websites with Universal Analytics appeared first on Analytics Talk.

Just to be clear, as of March 2013, Universal Analytics is a choice when you create a new web property. You can use the current version of Google Analytics or choose the new Universal Analytics platform. Existing accounts are not transitioning to Universal Analytics yet.

When you create a new web property in Google Analytics you will now have a choice:

When you create a new Property in Google Analytics you must choose between to use the new Universal Analytics.

Before you decide to use Universal Analytics you should consider all of the features in the product and the work required to implement them.

Here’s a list of things you should consider when implementing Universal Analytics.

Currently Supported by Universal Analytics

Standard website metrics: All of your favorite metrics, like visitors, visits, pageviews, time, etc. will be in Universal Analytics. And it will also collect standard information like geographic location, referral sources, marketing campaigns, etc.

Ecommerce tracking: You can still collect ecommerce transactions with the new Universal Analytics.

Event Tracking: Event tracking, that insanely useful feature that you can use to track all sorts of visitor interactions, like reading an article, is supported by Universal Analytics.

AdWords Linking: You can still link an AdWords account (or mulitple accounts if you’re working with a bigger client) to Universal Analytics.

Custom Variables: Custom variables will be replaced by a new feature called Custom Dimensions. There are two main differences. First, you get 20 custom dimensions. Second, there is a little less coding to set up a custom dimension. More on these very, very, soon.

Custom Metrics: In addition to the new Custom Dimensions there are also new Custom Metrics. A Custom Metric is a custom count of something that is specific to your business. More on these soon.

Easier configuration: One of the main features of Universal Analytics is Server Side Sessionization. That’s a fancy way of saying that you no longer to to customize the JavaScript code. Many of the customizations, like adding additional search engines, is now done in the admin section.

Finally, another important factor is that Universal Analytics is THE Google Analytics platform. New features will be rolled out to the Universal Analytics code base.

Coming Soon to Universal Analytics

While most Google Analytics features are supported by Universal Analytics there are some features that are not yet supported. If you are a heavy user of these features you might consider waiting.

Content Experiments: At launch, the Universal Tracking code does not support Content Experiments. If you plan to run a lot of tests you may want to wait until UA supports content experiments.

Remarketing with Google Analytics: I must admit, this is one of my favorite features in Google Analytics. Unfortunately, at launch, Universal Analytics does not support the Remarketing feature.

AdSense Integration: Currently you can import your AdSense data, like revenue, into Google Analytics. This will be added soon to Universal Analytics.

Cross device tracking: This is a core component of Universal Analytics and will be launched soon. It’s going to be insanely useful and help organizations measure customers not sessions.

That’s it. Almost everything is supported by Universal Analytics.

New JavaScript Tracking Code

If you’re currently using Google Analytics you’re probably wondering if Universal Analytics uses the same tracking code. It does not.

Universal Analytics uses the new analytics.js tracking code.

For those that are just starting out this is no big deal. But for existing users you will need to consider a migration plan when Universal Analytics rolls out to existing accounts.

Check out my post on the tracking websites with Universal Analytics. It provides an overview of the new tracking code and how it’s different that the existing tracking code.

Universal Analytics and Google Tag Manager (GTM)

If you’re feeling a bit overwhelmed with all of this you may want to consider using Google Tag Manager to implement Universal Analytics.

You can use Google Tag Manager to implement Universal Analytics. Just choose a Universal Analytics tag type.

You can also use Google Tag Manager to track events, ecommerce transactions, etc. In general, using GTM, or any tag management tool, is strongly suggested. It creates a much easier way to manage your data collection.

If you do decide to use a tag management tool, don’t forget to consider adding a data layer.

It’s All About the Platform

And one more thing, Universal Analytics can be used to measure almost any type of technology thanks to the new Measurement Protocol. It’s a true platform that can give you deeper insights into your customers, their cross device usage, and their long-term value to your company. That’s a lot.

Universal Analytics is going to dominate the conversation when it comes to Google Analytics. I’ll do my best to keep everyone up to date on the features and platform updates. It’s going to be fast and furious :)

Migrating to Universal Analytics is a post from: Analytics Talk by Justin Cutroni

The post Migrating to Universal Analytics appeared first on Analytics Talk.

There are two big changes to user permissions.

First, there are now three different types of user permissions: Manage Users, Edit, and View.

The second major change is that permissions can now be applied at various “levels” of the Google Analytics hierarchy (Account, Property and Profile).

Let’s start by looking at the new permissions.

New Google Analytics User Permissions

There are three types of Google Analytics user permissions: Edit, Manage Users and View.

Gone are the generic Administrator and User roles in Google Analytics. There are now three types of user permissions:

Edit: This is basically the same as the old Google Analytics Administrator. A user with Edit permissions can make administrative changes and view report data. This means that a user with Edit permissions can add/edit and delete accounts, properties, profiles, filters, goals, etc. However, a user with Edit permissions can not add/edit and delete users.

Users with Edit access can also view reports.

View: A user that has View permissions can only view data. That’s it, they can’t change any setting. This is the same as the old Google Analytics User type.

Manage Users: This is a light-weight type of administrative permission. Someone that can manage users has the ability to add/delete users and assign permissions. That’s it. This does not include editing settings or viewing report data. They can just add and delete users in the account.

You’ve probably noticed that the big change is that managing users has been decoupled from from the major administrative functions. I think this recognizes that many technical account administrators should not be managing users. That’s probably a function of marketing.

If the new user permissions were the only way to control user acess then these changes would not be such a big deal. Probably more of a G+ post rather than a full blog post :)

The big news is that you can now apply the above permissions at the various levels of a Google Analytics account hierarchy.

Setting Permissions at Different Levels

This is where the real power of the new user permissions comes into play.

Google Analytics is organized in a hierarchy of Account, Property and Profile. A property (also called a web property) is really a source of data. It could be a website or a mobile app. A profile is a combination of data (from a property) and settings that you apply to that data (filters, goals, etc). There are also some settings at the Account and Property level (account linking, Remarketing, etc.).

All reports exist at the profile level.

Here’s how it looks:

Google Analytics is organized into a hierarchy of Accounts, Properties and Profiles.

User permissions is now a setting at each part of the hierarchy. So when you view the account settings, property settings, or profile settings you will have a Users tab with a list of all users and their settings at that level of the hierarchy.

Here’s is the User Permissions settings page for a web property:

You can set Google Analytics user permissions at each level of the account hierarchy.

Notice in the screen shot above a few users have no permissions and the comment “Permissions assigned at profile level”. This means that these users don’t have ANY permissions at the Property level but they do have some permissions at the Profile level.

When you apply permissions at one level of the hierarchy they will cascade down to lower parts of the hierarchy.

That’s why there are some users in the image above that have “None” for their permissions – they were not assigned any permissions at the Account or Property level. They were only assigned permissions at the profile level.

Google Analytics user permissions cascade down the hierarchy.
Photo courtesy Jesse Nichols

It’s really important to understand that last point.

Lower parts of the hierarchy will inherit permissions from upper parts of the hierarchy. If a user has Edit permission at the Account level, they will also have Edit permissions for all of the properties and profiles within the account.

Also note as you move down the hierarchy from accounts to properties to profiles you can not reduce a user’s permissions.

This is another important concept to understand.

If you grant a user Edit permissions at the account level you can not grant them View permissions at the Property level. Again, you can’t reduce their access as you go down the hierarchy.

However, you can INCREASE their access as you move down the hierarchy. This is important because you can now let people modify settings for a property or profile without changing other settings in the account.

Examples & Best Practices

Let’s look at an example.

Let’s say you’re a big company with 100 web properties and a different marketing team that uses each of those properties to measure their marketing efforts. Each marketing team needs administrative control over their individual web property. But they also need to view data from other web properties to better understand how other teams are doing.

Start by giving them View access at the Account level. This will let them view data in every Property and every Profile.

Next, give them Edit access at their individual web property level. They will be able to view all the data and make configuration changes but ONLY to the Profiles within their property.

If there are some marketing teams that should only be able to see their own data, and not data from other teams, you need to rethink your strategy. You can’t give a user View permissions at the account level or else they will be able to see data in every Property and Profile. And you can’t reduce their permissions as you progress down the hierarchy.

In this case you need to set the user permissions at the Property and Profile, NOT the account level. Apply the correct permissions based on what the user should be able to do.

Here’s another example. Let’s say you’re a web analytics consultant that helps clients setup their account and analyze their data. You should ask your client to give you Edit permissions to the appropriate Web Properties and Profile. You probably don’t need Manage User permissions unless this is part of your role.

Other user permission best practices:

• It’s a good idea to limit the number of users that have Edit permissions at the Account level.
• The Manager Users setting is great for marketers. Many times a marker will need to give other team members access to data. Or, in an ideal case, an executive that wants deeper insights into the business! Don’t be afraid to give marketers Manager User access so they can add more users.
• Audit the people that have access to your data once a year (or once a quarter depending on your data governance). Prune people that don’t need access or adjust their permissions as necessary. I find that a lot of organizations forget to do this.

Matching the new permission types with different levels of the hierarchy should give every type of organization the flexibility they need in controlling access to data and configuration settings.

Get it?

Adding a New User

Adding a new user to Google Analytics has not changed much.

the only difference is that you can now add a user at the Account, Property or Profile level. And when you add the user the permissions you assign will cascade down the hierarchy from Account, to Properties to Profiles.

When you add a new user to Google Analytics you need to specify their permissions.

Remember, the new user still needs to have a valid Google Account.

The little “Notify this user by email” check box will send an email to the user when they are added to the account.

Migrating to the new User Permissions

As Google migrates to the new user permissions you won’t notice many changes. The new permissions map fairly well to the old permissions:

Edit + Manage Users = Administrator

View = User

But there will be one change and it pertains to Annotations. From the Google Analytics help center:

If you previously had User access to a profile, you could create annotations that were shared with everyone in the profile. Former Users now have the View permission for a profile, and can create only private annotations that are not shared with anyone else in the profile. This change was made to enforce consistency and security across Analytics.

If you’re a Google Analytics manager User Permissions is a critical tool. It help you push more data into the hands of more people in a safe way.

New: Managing Google Analytics User Permissions is a post from: Analytics Talk by Justin Cutroni

The post New: Managing Google Analytics User Permissions appeared first on Analytics Talk.

Automatically add the Google Analytics PR dashboard to your Google Analytics account

This dashboard is focused on the visitors to a website, where they came from and what they do on the site.

It starts with a simple count of people and how many sessions they created. This is a good way to get an idea of the normal trend of traffic and people. If there any abnormal? Did you do something to cause a bump?

Visitors and Visits Widget: Helps you understand the number of people and visits to your site.

Next we move on to goal conversions. This is a widget that you should update to reflect the goals you have configured on your site.

PR Dashboard Widget: Mesure Business Goal Completions

For me, this is the number of people that follow me on Twitter. For you it might be a lead or a product sale. The goal is to see how your actions change this trend (hopefully) for the better!

To configure this goal, just click on the little Pencil in the top right corner of the widget. Then choose your own goal from the list of Goal metrics.

Configure the dashboard widget to measure one of your specific business goals.

Moving down, it’s more about the behavior of the visitors.

Frequency measures how often the visit. It’s one of my favorite metrics. Most people only visit once. But you should check to see if your activities are bring people back multiple times.

The Frequency widget measures how often people visit the site. People who visit often are more valuable.

In addition to how often people visit, I also want to look at how engaged they are. For this I use the Depth of Visit or how deep people go into the site. In general people that move deeper into the site are more engaged.

The Page Depth widget measures how how many pages deep a visitors navigates on your site.

Next we move into where people came from, or the traffic sources. Each bar represents a different traffic channel, like organic, referral, etc.

The Traffic Sources widget identifies the top driver of traffic for your site. Does this align with your PR activities?

Notice how each bar is both green and blue? Green represents the returning visitors and blue represents the new visitors. This is giving me just a bit more information about my audience and if I’m attracting new people from these channels or returning people.

I now want to look at social channels. This widget is very similar to the previous. The only difference is that each bar is a different social network. Google Analytics will automatically create these grouping.

The Referring Social websites helps you identify traffic coming from social media activities.

And again, the green is new people coming from social networks and the blue is returning people.

Next is the top referring websites. Again, very similar to the two previous widgets. When you look at this data you should have some expectations. Do you see traffic from a referral you were not expecting? Use the context of your PR tactics to understand the data.

The referring sites widget shows which websites refer traffic to your website. Do you see any websites that you target in your PR activities?

Now let’s focus on visitor behavior on the site.

One thing that is really important in PR, and marketing, is amplifying your message and brand. Social Media is good at that. The next widget shows all social actions that happened on your site.

The social actions widget shows how often people engage with your content using social sharing tools, like Like buttons, Tweet Buttons, etc.

Each row is the name of a social network and the type of social action that happened on your site (+1, Like, Tweet, etc.)

Moving on we drill deeper into on-site sharing.

Now we’re looking at the most shared content on your site.

The Shared Content widget is similar to the social sharing widget. This is a specific list of all the pages that had some type of social action.

I like this because you can get a sense for how much people are sharing and WHAT they like to share. Are they sharing the stuff you are promoting?

NOTE: You need to make sure you have Google Analytics configured to track social activities for the previous two widgets to work.

Now let’s look at some keywords.

The next two widgets are Organic search terms. There is one widget for branded search terms and one for non-branded terms. Again, you need to customize these for your brand terms.

Non-branded organic search terms provide insight into what people are interested in. Remember, this excludes the (not provided) keyword.

Click on the little icon in the top of the widget to change my brand terms to your own.

Change the branded search term wizard to include the terms that are specific to your business.

The data in these tables is only the top 10 rows from the reports. Based on your actions and tactics there might be little change in the top 10 rows. Click on the name of the widget to jump to full report.

That makes 11 dashboard widgets. You’re allowed 12, but I wanted to give everyone space to add their on. So there you have it, a (somewhat rough) PR dashboard for Google Analytics.

A PR Dashboard for Google Analytics is a post from: Analytics Talk by Justin Cutroni

The post A PR Dashboard for Google Analytics appeared first on Analytics Talk.

Let me be clear, I’m not a PR expert. You should follow KD Paine, Nicole Ravlin, PR News and other experts to learn about PR. But hopefully some of these Google Analytics tips will help those in PR measure better.

Have A Measurement Plan

Before you do any measurement you should have a plan. This is where my lack of PR knowledge hurts me because I don’t know all of the tactics that a PR pro uses :) But thanks to the students at Champlain, I learned that PR deals with controlling the flow of information between a company and the public, and they try to persuade the public regarding a certain viewpoint, idea or product.

At the end of the day PR activities should help drive business outcomes :)

I love this quote from Measure what Matters which is all about measuring PR:

The ultimate test of the effectiveness of your efforts is whether the behavior of the target audience has changed as a result. This is also the most difficult to measure because all of the various factors that can affect the results of programs a company undertakes. The most effective way to measure behavior change attributable solely to your efforts is to study specific programs carried out by your team.

“Looking at the effects of your efforts.” I love that part.

But what effects?

The effects are just the data in a tool like Google Analytics. Here are a few things that you can measure:

1. How many people did I get and how much traffic?

Measure Unique visitors and Visits. Unique visitors is a rough count of the people on your site and Visits is the number of sessions they create.

2. Where did my site visitors come from? Twitter? Blogs?

Measure the different traffic sources that drive traffic to your site.

3. Did people like the stuff on my website? Were they engaged? How often do they come back? How long are then on the site?

Measure the frequency of visits, the recency (amount of time between visits) and how many pages they view. How much time did they spend and which pages they look at is also very useful

4. How did they change my business? Did I sell more products? Did I get more leads?

Measure Goals and Conversions. Make sure you’ve got Google Analytics configured to track transactions or other conversions on your site.

Where to Find This Data

Almost all of these metrics are in the standard GA reports. Most are columns in the data table as shown in the image below. There’s a column for each metric that covers traffic (visits) and engagement (avg time on site, pages per visit and bounce rate).

This is important: you can change the columns in the reports to view your goal conversions. Use the links at the top of the report, outlined in blue.

Click on the links that say Goal Set 1, Goal Set 2, etc. and the table will refresh with your conversion data. Remember, you need to set up goals or track ecommerce transactions.

This means that you can view goals (i.e. conversions) in EVERY reports. Just keep that in mind as we move through some example.

Metrics, and how to change metrics, in a Google Analytics report.

Measure Where People Come From

Use the Traffic Sources > Sources > All Traffic. This is a list of every traffic sources for your site.

Sources, also called Channels can be email, social media (Twitter, YouTube, Facebook), other websites (blogs, news sites, etc.), search engines, paid search (Google AdWords) or other online advertising (display ads, etc.).

Here’s some data for my blog.

Google Analytics Traffic Source: Essential for PR people.

Let’s talk about what some of these things mean. The Medium is the channel and the Source gives you a bit more information about the Medium (like the name).

Google/Organic is organic search from Google. t.co/referral is a referral from t.co (Twitter), koozai.com/referral is a referral from the site koozai.com, (direct)/(none) is traffic that came directly to your site.

This should give you some overall context as to where people came from.

If you are expecting traffic from a certain channel then use the search box at the top of the table to search for it. Just type in the name.

If you want to know which websites are sending you traffic then check out the Traffic Sources > Sources > Referrals report.

Use the Referring Sites report to identify which websites (blogs, news sites, etc. are sending you traffic.

Let’s pivot this a bit and just look at social referrals. Here I’m using the Traffic Sources > Social > Network Referrals report. This is a list of traffic from all social sites. Google Analytics automatically creates the list for you.

The Google Analytics Social Sources automatically groups social sources of traffic together.

One more traffic source. In the Traffic Sources > Sources > Search > Organic you can see the organic keywords that people searched for on Google and other search engines.

The Organic Search reprot show traffic from organic keywords.

You might be looking for brand terms or product terms. It depends on what you are promoting and how you’re doing it.

Note: You may have noticed that the #1 organic keyword from Google is (not provided). Google blocks the search query for those that are logged into Gmail, G+ and other services. Here’s a technique you can use to analyze what users from (not provided) are searching for.

If you want a bit more detail about your traffic sources you can use Google Analytics Campaign Tracking and a process called Link Tagging. This is the creation of special links that help you track more details about your traffic sources.

Measure Visitor Behavior

Now that you know how to identify where people come from, let’s look at visitor behavior on the site.

I like to look at how often they come to the site. This is called Frequency and can be found in the Audience > Behavior > Frequency & Recency report. Usually we want people to come back more than one time.

The Google Analytics Frequency report shows how often people visit the site.

The frequency will depend on your marketing and PR activities. But You’ll probably see a lot of people visiting once. Observe how this changes especially when you take some action.

Now let’s look at engagement (Audience > Behavior > Engagement). I like to use Page Depth as a metric. How deep into the site do people go? The deeper into the site the more they are engaged.

Use the Page Depth report to measure the engagement of site users.

I also like Bounce Rate. Bounce rate measure how many people land on your site and then immediately leave after viewing the landing page. Normally a high bounce rate means that people really don’t like the content you’re showing them and there is some disconnect with the content they clicked on.

You can find bounce rate in almost EVERY report. I like to look at Bounce Rate in the Traffic Sources reports we discussed above because I can see which external sources may be linked to the wrong pages on my site.

Review the bounce rate for your traffic sources to understand which ones are ‘sticky.’

A couple other metrics worth mentioning because they’re easy to find: pages per visit and average time on site. Both of these are in most reports, like the Traffic Sources report above. But they’re averages, and can be easily skewed. That’s why I mentioned page depth and frequency.

Measure What They’re Interested In

What are people looking at?

Use the Content > Site Content > All Page report to view which pages on the site get the most traffic. This simple view of site content can give you insights into what people are interested it.

Use the Top Pages report to identify what topics or products people are interested in.

Again, try using the search box at the top of the report to isolate something specific that that relates to your effort (a product, brand, etc.).

You can also use the Organic report mentioned above to look at keywords, but that’s getting more challenging with (not provided).

Segment Your Data for Deeper Insights

Your website gets a lot of traffic. You need a way to filter all that traffic and only look for what’s important to you. If you can isolate the results of your activities in Google Analytics you’ll have a better chance of showing your impact.

That where segmentation comes in.

The easiest way to isolate data is to use an Advanced Custom Segment. This is a filter that will filter all of the data in all of the reports in real time.

Let’s look at a quick example.

I want to filter my reports and only look at the behavior of people from the referring website analytics-ninja.com. I start by Clicking on the Advanced Segment button on the gray bar at the top GA.

Create an Advanced Segment to isolate effects of your activities for deeper PR insights.

I don’t want to get into to many details about how to set up the advanced segment. Check out this great article on segmentation.

You have to tell the advanced segment what piece of data to filter on. This is called a Dimension or a Metric. Then you have to tell the filter what values to allow or exclude.

A quick example. Let’s say I’ve got a release posted on analytics-ninja.com. I can filter all of my traffic and view just the data for people coming from analytics-ninja.com.

Here are the settings for my advanced segment.

Use an Advanced Segment to isolate certain data on your site.

When I apply this segment all of the data in all of the report will be for people coming from analytics-ninja.com.

I can look at all of the data we discussed above (behavior, content, engagement, conversions) and evaluate the success of traffic from analytics-ninja.com.

You might also want to check out the Google Analytics Solutions gallery for some pre-made advanced segments.

Tip: Try creating some segments. YOU WILL NOT BREAK ANYTHING :) Segments are specific to you and your account. I promise, Google Analytics will still work if you make a bad segment.

Real Time Monitoring

Now let’s take all of the data we just talked about and make it measurable in real time. That means as it happens, second by second.

Often PR has to deal with unanticipated events, like a kind word from an influencer or a crisis. You need to understand the impact now.

With the real time reports you can get a count of the active users on your website, where they came from (geographically), what brought them to the site (traffic sources) and what they’re looking at (content).

PR Pros can use the Real Time reports to monitor what’s happening second by second on a site.

These are all things we discussed above. The only difference is that you’re seeing it second by second.

Want more? How about the ability to segment the real time reports? Just click on any piece of data, like a page name or a traffic source, and the Real Time reports will be segmented.

Things to make Your Analytics Life Easier

OK, here are a few tips to make all of this measurement easier.

Customize Google Analytics. You can use Custom Reports or Custom Dashboards to aggregate all of the information that we talked about above.

In fact, I created a Custom Dashboard using many of the reports above.

Automatically add the Google Analytics PR dashboard to your Google Analytics account.

Use the Google Analytics PR dashboard to monitor your Public Relations efforts.

You can take my customization one step further by applying an advanced segment to it. Then you’ll have all the important data, segmented by your action, in one place.

Love the dashboard? You can use the Short Cut feature to add these reports (including the custom dashboard) directly to the left-hand navigation in Google Analytics.

What, you’re too busy to log into Google Analytics and look at your dashboard? How about creating an automated email and Google will send you the dashboard every day.

What, you get too much email? How about creating some automated data alerts to automatically monitor the data in the reports and email you (or send you a text message) when something changes? If you don’t know about Custom Alerts you can read how they might save your ass :)

Google Analytics for PR

So that’s it. A brain dump about PR and Google Analytics. After writing this I can think of a lot more things that PR can do with Google Analytics. But I think I’ll save them for an Advanced post.

A big thanks to the students at Champlain College for the great questions.

Google Analytics for PR is a post from: Analytics Talk by Justin Cutroni

The post Google Analytics for PR appeared first on Analytics Talk.

There are lots of paid tools that will track where your content ranks in your search results. But my friend AJ Kohn wanted to try and develop a free way to measure rank with Google Analytics.

Actually, he had a brilliant idea: measuring the individual keyword rank, group keywords together into indexes and then track the average rank of those indexes over time – all with Google Analytics.

Track the SEO rank of keyword groups, or indexes, using Google Analytics.
Image courtesy of AJ Kohn.

Here’s how AJ describes a rank index:

A rank index is the aggregate rank of a basket of keywords that represent a type of query class that have an impact on your bottom line. For an eCommerce client you might have a rank index for products and for categories. I often create a rank index for each modifier class I identify for a client.

It becomes not about any one term but the aggregate rank of that index. That’s a better conversation to have in my opinion. A rank index keeps the conversation on how to move the business forward instead of moving a specific keyword up.

After a little brainstorming and testing we think we have a method to do this and would love your feedback.

How This Works

Here’s the general idea:

1. Someone clicks on an organic search results
2. The user lands on your site
3. A custom piece of code that you install on your site collects the keyword and the rank of the result using a Google Analytics event
4. Google Analytics will automatically calculate the average position for the result
5. You create indexes of keywords and analyze the data using the Event reports OR Excel and the GA API

That’s it. Nothing more.

Let’s dig into some of the details of how this work.

The Code

The code is relatively simple. All it does is looks at the referring URL and, if it’s from Google Organic, plucks out the location of the search result and sends it to Google Analytics using an Event.

Here’s the code – feel free to copy it and use it. You do not need to customize the code for your site.

This section of the code parses out the keyword and the search result location. The search result location is stored in a query string parameters named cd.

var myString = document.referrer;
var r = myString.match(/cd=(.*?)&/);
var rank = parseInt(r[1]);
var kw = myString.match(/q=(.*?)&/);


Next there is a check to see if the keyword is (not provided). If it is (not provided) then we make sure that we track the keyword as (not provided).

if (kw[1].length > 0) {
var keyWord = decodeURI(kw[1]);
} else {
keyWord = "(not provided)";
}


Finally we send the data to Google Analytics using an event:
_gaq.push(['_trackEvent', 'RankTracker', keyWord, p, rank, true]);

For those of you that have not used events, there are 5 parts to an event:

• Category: This helps you categorize the events and separate them in the reports.
• Action: What happened that you want to track?
• Label: Provides more information about the Action.
• Value: Some numerical value for this event.
• Noninteractive: This is a flag, it tells Google Analytics if this event should impact bounce rate and time calculations. If you have no idea what this means read about how Google Analytics calculates time.

For this tracking technique we’re sending the following data with the event:

• Category: RankTracker – all of the data will be grouped under the Event named RankTracker.
• Action: [keyword] – the action will collect the search term the user entered on Google.
• Label: [landing page] – the label will be the landing page that the user landed on.
• Value: [SERP rank] – rank of the search result will be collected for each keyword. More on this in a second.
• Noninteractive: TRUE – this event will NOT change your bounce rate calculations.

The most important thing to understand is that the rank of the search engine result will be tracked as the value of the event. So as you get more and more visits from a keyword the TOTAL VALUE of the event for that keyword will increase.

Google Analytics will also create an Average Value metric for each keyword event. This value will actually represent the Average Rank for each search result.

Let me say that again.

Using this technique Google Analytics will automatically calculate the average position of your content in the search results.

I also want to point out that this data is generated from real clicks to your site. It’s actual people visiting your site and (hopefully) converting. To me, this makes it a lot more valuable than just tracking ranking that does not result in clicks or conversions.

So that’s how this technique actually works. As I mentioned, all you need to get the data is install the code on your site. Just make sure it appears AFTER the standard Google Analytics page tag.

Raw Ranking data in Google Analytics

Ok, let’s look at some data. It’s all event data, so let’s go to the Content > Events > Top Events report. I’m looking for the RankTracker event category.

You can find rank tracking data in the Google Analytics Event report.

Clicking on RankTracker I see all the actions for that event, which are really just the search terms.

The search terms and ranking are stored as Google Analytics Events.

Here’s the cool thing. Remember that the Average Value metric is actually the Average Position for each of those keywords. So for the time range that I’m looking at I’m seeing the average position for each keyword.

Let’s play with this.

Change the data over time graph to plot the metrics Average value. Now you can see the average ranking for all your search terms over time.

Change the plotted metric to see your average search result rank change over time.

You can also select multiple rows and plot the Average Value to see how the ranking changes for specific keywords over time.

You can view the changing rank of your keywords by plotting multiple rows.

Another cool type of analysis you can do is use a secondary dimension to view the landing page for each keyword. The landing page is stored in the Label portion of the event. This is something a lot of people do to measure (not provided)

View keyword, landing page and Average SEO rank in Google Analytics.

Now we have keyword, landing page and the average ranking in one report.

We can also group keywords together into an index. The easiest way to do this in Google Analytics is using an Advanced segment. Again, a lot of credit to AJ for explaining how he does this.

For something simple like my blog I can create a segment based on my name and the Analytics Talk brand. That makes it easy to view the performance of that group of keywords.

Create an Google Analytics Advanced Segment to group keywords together into an index.

While I can do analysis within Google Analytics, the real value is exporting this data to excel and producing more detailed report. AJ covers that nicely in his post. I’ll post some other methods next week.

Actionability

The entire reason you should collect this data is to measure how your actual content-creation tactics are working. It’s granular data that helps support your overall strategy of “content marketing.”

If you’re not getting valuable traffic from content and keywords that you are optimizing for then you need to re-evaluate your tactics and change.

Caveats & Things to Remember

First, this data is only for traffic coming to your site. It does not give you the ranking for search terms if you are not getting traffic.

As mentioned above I actually like this approach. If I’m not getting traffic/conversions then my ranking sucks. I need to create better content.

Second, Google blocks the referring keyword data for secure search queries. This includes logged in users, people using Safari on iOS 6 and Firefox. There are no analytics tools in existence that will provide the blocked keywords. In Google Analytics you’ll see the keyword (not provided) in your data. You’ll also see (not provided) in this data.

So there you have it. We (me and AJ) hope that this techniques helps people track rank freely and more effectively. Feel free to innovate and develop on top of it and let us know what you think.

A New Method to Track Keyword Ranking using Google Analytics is a post from: Analytics Talk by Justin Cutroni

The post A New Method to Track Keyword Ranking using Google Analytics appeared first on Analytics Talk.

Cohort analysis has never been a core part of Google Analytics – it’s always been a bit of a hack. I wanted to discuss how to do cohort analysis with Google Analytics today and what it might look like in the future.

What is a Cohort?

Let’s use a definition from Wikipedia:

A cohort is a group of people who share a common characteristic or experience> within a defined period (e.g., are born, are exposed to a drug or vaccine or pollutant, or undergo a certain medical procedure). Thus a group of people who were born on a day or in a particular period, say 1948, form a birth cohort.

Side note: you’ll notice the medical tone of the definition. Medical studies use cohorts a lot.

I like this definition as it clearly points out the key parts of a cohort: users who share a common characteristic that occurs within a defined period of time.

A cohort is really just another type of segmentaiton, the key is that a cohort is based on date.

Here’s an ecommerce example. If I was an ecommerce business owner I would want to create a cohort of customers who make their first purchase on Black Friday. This cohort is important because they made their first purchase during a very important time, the holiday buying season.

The common characteristic/experience that they share is that they all made their first purchase, and the defined period of time is Black Friday.

From an analysis perspective we want to segment this group to observe their behavior over a longer period of time.

• Do these customer behave differently?
• How do they differ from customers that buy at other times of the year?
• Do they buy multiple times? Do they spend the same amount?

Here’s another example, pretend I’m Evernote. How do customers who signed up for my premium service in October, 2012 differ from customers who signed up for my service in February, 2012? Again, do these users behave differently over time?

Cohorts are groups of people that share a common trait within a certain time.

I think a lot of people use the term cohort to mean a segment of users and ignore the date part of the definition. I know I’ve done this in the past. But segmenting by users is really user segmentation, which is slightly different. I want to stay focused on cohorts in this post.

So now that we know what a cohort is, why can’t we create a cohort in Google Analytics?

Why creating Cohorts with Google Analytics is hard.

Cohort analysis is hard to do with Google Analytics because activity dates are not accessible in the reports or segmentation. To create a cohort we need to access the date that a conversion, or some other action, happens. In our examples above we talked about transactions on Black Friday and the date a user became a premium subscriber.

Another reason reason that cohort analysis is hard in Google Analytics is that the segmentation is based on visits, not users. So even if we had some type of date dimension and used it to create a segment, that segment would only represent visits that matched the criteria. It would not return data from all the visits for the users that matched the criteria.

In order to do cohort analysis in Google Analytics we need to inject a date, usually the date when a conversion happens, into Google Analytics and attach it to a user. Some of the most popular dates to collect include:

• first purchase date
• most recent purchase date
• first time the user signed up for membership
• the date when user upgraded membership

Once we have a date in Google Analytics we can do some cool analysis.

Let’s look at two different ways to collect the data we need for cohort analysis..

Using Custom Variables to Create Cohorts

You can use a custom variable to create a cohort in Google Analytics. The general idea is that you set a visitor level custom variable with the date of the action when the user completes the action.

Because the data is stored in a visitor level custom variable (i.e. a cookie) whenever the user visits the site the data will be sent to Google Analytics. The cookie helps us attach the date to the user.

Here’s an example. We want to create a cohort based on first purchase date. We would need to set a visitor scoped custom variable that contains the date when a visitor made their first purchase.

We would need to use the following code:

_gaq.push(['setCustomVar', 1,'FPD', 'YYYYMMDD', 1]);

The custom variable is named FPD (for First purchase Date). The date is formatted using the four-digit year, two-digit month and two-digit day. This format will let us create a lot of different cohorts using advanced segments. More on this below.

Remember, the code above needs to be placed on the site’s thank you page. But, it can only be added when a person makes their first purchase. We don’t want to set this cookie when people make their second or third purchase.

Obviously you’ll need some code to identify if the purchase is a first purchase or a repeat purchase.

It is possible that this data could be erased if the user deletes their cookies. You could always create some more code to keep resetting this cookie when the user logs into the site.

Using Events to create Cohorts

Another way to add user data to Google Analytics is via an event. You can send the same data, but you just need to do it more often.

Here’s the event code:

_gaq.push(['trackEvent', 'Cohorts','FirstPurchase', 'YYYYMMDD']);

Because an event is not stored as a cookie you need to fire an event every single time the user visits the site. This means that you need to know that the user is a customer and insert the event code on a page the user will see. You might put it on the login page or you might just include it in the standard page tag. Depending on your site, this could be a lot of coding.

I’m not a fan of this method. It takes too much work.

I prefer to use a custom variable because it will persist and provide more reliable data.However, I would hedge my bets and try to add a little code that identifies when a user logs in and checks and resets the cookie.

Analyzing Cohorts

So now that we have a cohort defined, how do we analyze the performance in Google Analytics? Using advanced segmentation!

Let’s assume I used the custom variable approach to identify cohorts.

I want to create a cohort of all the people that first purchased from my company in October, 2012. The purchase date is stored in a custom variable named FPD. Here’s how that Custom Advanced Segment would look:

Creating a Cohort using a Custom Advanced segment in Google Analytics.

The first condition specifies the name of the custom variable, the second condition matches the date stored in the custom var.

That’s it.

Notice, that using the YYYMMDD format let’s me create many different date based cohorts. I could create a quarterly cohort using the following advanced segment:

Creating a quarterly cohort in Google Analytics

Remember, these segments will return all of the data, from all of the visits, that include the appropriate custom variable. This means that all of the data will be from users that made their first purchase in my desired date range.

So what am I looking for once I apply this segment? What kind of insights am I looking to find? A few things:

• How does this group convert at macro and micro conversions? It’s really, really important to look at any revenue metrics. Are they spending money? Are they generating a lot of value for my business? If they are not generating value, when did they stop generating value? Was it after one week, one month of one year?
• If you are an ecommerce website, what products do these people purchase? Are they buying products in the same general category or are they buying a broad range of things?
• What traffic sources generate repeat visits? Are they responding to retention based marketing activities? Are they coming directly to the site because they love you?
• If you are a publisher analyzing members, what content do these people read? Do they read the same type of content? Are they looking in different categories? What do their loyalty metrics look like?

The easiest way to visually identify the behavior of cohorts is by applying multiple advanced segments at the same time and observing the metrics. How do they change? Do they all show the same trend?

Whenever I’m doing a cohort analysis I’m always trying to figure out when this group of people no longer provides value to my company. If I can figure this out they I can be more proactive in retaining them as a customer.

The Future of Cohorts in Google Analysis

Cohort Analysis is going to get a lot better in Google Analytics.

You may have used the new Remarketing feature. This tool does true user segmentation. This includes the option to create a cohort using various date dimensions. This is going to be the future of segmentation in analytics.

User segmentation, and the ability to create cohorts, will be the gateway to many different types of new analysis.

Cohort Analysis with Google Analytics is a post from: Analytics Talk by Justin Cutroni

The post Cohort Analysis with Google Analytics appeared first on Analytics Talk.

Digital analytics is all about finding insights and making changes to your business. One of the easiest changes to make is to optimize your digital marketing based on things like bounce rate the performance of your campaigns. But there’s an awesome way to use your Google Analytics data to create new marketing campaigns specifically targeted to the needs of your customers. It’s called Google Analytics Remarketing.

I really like this feature because you can take action on your data directly from Google Analytics. And, what’s even better, you can make marketing changes based on the real behavior, of real people, on your site.

Let’s dig a bit deeper.

What is Google Analytics Remarketing?

GA Remarketing is a way to reach an audience using the Google Display Network. The magic is that you define that audience based on people’s behavior on your website.

Here’s how it works.

1. You segment your website users in Google Analytics based on some behavior. This can be anything from looking at a page on the site to buying a certain product.

2. You create a remarketing list in Google AdWords based on the segment that you created. In reality, the list of users is automatically sent from Google Analytics to Google AdWords as long as the list has at least 100 users.

3. You create and run campaigns for users that are on the remarketing list using Google AdWords. The campaign can have it’s own creative that specifically targets the behaviors of people on the list. Your remarketing messages won’t be shown to people who are not on the list.

A few really common remarketing examples include:

* If you’re a publisher, creating a list of users that viewed a certain piece of content
* If you’re ecommerce, creating a list of users that bought a certain product

Setting up Google Analytics Remarketing

You don’t need to do a lot of configuration to get remarketing up and running.

First, you need to make sure you’re Google AdWords account is linked to your Google Analytics account. The reason is that your remarketing list will be automatically added to your Google AdWords account. If there is no link between your accounts – well you get the idea.

Second, you need to change the JavaScript library in your Google Analytics page tag. Here’s how the standard Google Analytics page tag looks. Notice the part in BOLD, this is the ga.js library that holds all of the functionality for the tracking:

< script type="text/javascript" >

var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-91817-11']);
_gaq.push(['_trackPageview']);

(function() {
var ga = document.createElement(‘script’); ga.type = ‘text/javascript’; ga.async = true;
ga.src = (‘https:’ == document.location.protocol ? ‘https://ssl’ : ‘http://www’) + ‘.google-analytics.com/ga.js‘;
var s = document.getElementsByTagName(‘script’)[0]; s.parentNode.insertBefore(ga, s);
})();

< /script >

Now, here’s how the code should look after it’s been changed for remarketing. Notice that the JavaScript library has changed from ga.js to dc.js and is served by a different domain.

< script type="text/javascript" >

var _gaq = _gaq || [];
_gaq.push(['_setAccount', 'UA-91817-11']);
_gaq.push(['_trackPageview']);

(function() {
var ga = document.createElement(‘script’); ga.type = ‘text/javascript’; ga.async = true;
ga.src = (‘https:’ == document.location.protocol ? ‘https://’ : ‘http://’) + ‘stats.g.doubleclick.net/dc.js‘;
var s = document.getElementsByTagName(‘script’)[0]; s.parentNode.insertBefore(ga, s);
})();

< /script >

The dc.js library holds all of the logic of ga.js, in addition to some special functionality just for remarketing. The data is still sent to Google via an image request. And Google Analytics still uses a first party cookie to track the visitor.

The only difference is you enable Google Analytics to collect its normal information as well as the DoubleClick cookie.

You don’t need to change any of your event tracking code, custom variables or ecommerce tracking. All of that functionality stays the same.

Finally, you must update the privacy policy on your site. This is really important, we all need to keep users updated to how the web works and how we use it as a business. You must change your privacy policy to include:

• How you’re using remarketing to advertise across the web
• How third-party vendors, like Google, show your ads online
• How you and third-party vendors, like Google, use first-party cookies (like the Google Analytics cookie) and third-party cookies (like the DoubleClick cookie) together to inform, optimize, and serve ads based on someone’s past visits to your website

Check the Google Analytics help center for more information about the privacy policy requirement for Remarketing.

Once you’ve got all of this set up it’s time to start creating a remarketing list!

Creating a Google Analytics Remarketing List

This is actually pretty easy.

Navigate to the Rearketing List tab of the Admin Section and click New Remarketing List.

You can create a Google Analytics remarketing list from within the admin section.

The first thing you’ll need to choose is which profile this remarketing list should be attached to. This is really important because profile filters will affect the data. Ideally you’ll use a profile that only includes your customers and not internal employees.

You must choose a Google Analytics profile as the source data for your Remarketing List.

You’ll notice that there are some default marketing list types to get you started. You could choose to create a list of user that saw a specific page (by specifying a page URI), a list of users that completed an existing goal (by choosing the goal from a drop down) or you can create your own custom list (more on this later).

For example, we might want to create a remarketing list around all of the people that viewed the Factory Tours page. Perhaps we want to target that group of users with information about why they should visit our factory! Just choose the appropriate list type and enter that page URL in the field.

To create a remarketing list based on users viewing a page, just click the correct radio button and enter a page URL.

You’ll also need to specify how long a user will be part of this list. The default is 30 days, but you should adjust this based on the activity you expect from the user.

You can specify how long a user remains part of a remarketing list. Make sure it aligns with your campaign objectives.

For example, if you’re creating a remarketing list around some date-specific event, like a holiday, make sure that the remarketing list will expire after the holiday.

If the remarketing list is based on long-term user behavior, like ownership of a certain product, you might want to extend the duration longer.

The maximum time that someone can be on a list is 540 days.

NOTE: that the duration will be extended forward every time the user visits the site and qualifies for the list. So, if I’m part of the Sports Enthusiasts list because I read sports content, and I view sports content every day, my inclusion on the list will be extended every time I visit the site.

Once you’ve set your condition it time to view how many users meet the criteria. Click the Get Estimate number and Google Analytics will return the number of users that meet your condition.

Get the number of users in your Google Analytics remarketing list. Remember, you need at least 100 users in the list.

Remember a remarketing list must have at least 100 users.

That’s it. Just click save and the remarketing list will be saved to your AdWords account.

Creating a Custom Google Analytics Remarketing List

Let’s look at how to create a custom list based on our own user segment.

Segments are created using filters. No, not profile filters. User filters. There are two kinds of filters: Dimension & Metric filters and Sequence Filters.

There are two types of Google Analytics Remarketing Filters: Dimension & Metrics filters and Sequence Filters.

A Dimension and Metric filter is just list a standard Advanced Segment. You choose a combination of dimensions and metrics and Google Analytics will fiter the data and create a remarketing list based on the users that meet your criteria. Remember, this is real, user segmentation, not visit level segmentaiton like the regular Advanced Segments in Google Analytics.

Google Analytics Remarketing Dimension & Metric Filters are very similar to Advanced Segments.

Dimension and Metric filters are applied at the user level, regardless of time. So if you create a segment based on Revenue, it would be Revenue for the user over all time.

Sequence filters are almost the same. The biggest difference is that you can create conditions that must happen in sequence. You still specify dimensions and metics, but now you connect your conditions in a time sequence. This is the type of filter to use if you want to create a remarketing list of people who abandoned your shopping cart.

When you create a Google Analytics Remarketing Sequence Filter you must specify that your conditions happen in a certain order.

It’s really important to understand that the Sequence Filters apply to user actions that occurred any time in the 7 days prior to the current visit. It’s not applied to any time in the past. There is a limit.

When you add multiple filters to a visitor segment Google Analytics adds an AND condition to the filters. This means that all of the filter conditions must be met in order for a user to be in the segment.

You can apply multiple filters to a user segment.

That’s all there is to creating a custom remarketing list. Don’t forget you need to choose a profile, set a duration and make sure that you have at least 100 users in the segment.

Some Remarketing List Ideas

So what remarketing lists should you create? That’s fodder for another post. But here are some quick ideas:

Publishers:

• User who view a certain category of content. Create campaigns based on the same content or similar content.
• Interested in Membership. Does your site offer membership? Create a list of users that looked at membership benefits page but did not sign up for membership.
• High-value users: Create a segment of users that spend a lot of time on your site and

Ecommerce:

• High-value customers: Create a segment based on the customer’s total lifetime revenue.
• Product buyers: Create a segment based on the customers that purchased a certain product and market accessories for that product to them.
• Brick & Mortar Visitors: Create a list of people who view information about your store(s) and create campaigns around incentives to visit the store.

Love Remarketing? Hate Remarketing? Post your thoughts below.

Getting Started with Google Analytics Remarketing is a post from: Analytics Talk by Justin Cutroni

The post Getting Started with Google Analytics Remarketing appeared first on Analytics Talk.