"Where I think Google Analytics really shines as a product is the -what we call- custom advanced segments or just custom segments."

In a previous presentation, Nick Iyengar discussed data segmentation, providing a definition of segmentation and discussing its value for web analytics. He also went over a few reports that provide segmentation options by default on Google Analytics. In this presentation, he provides an in-depth overview of Advanced Segments, a segmentation tool on Google Analytics.

There's really no list of Top 10 types of segments, because different segments work for different businesses. Iyengar shows how to build custom segments around any attribute or dimension of your traffic. Below are a few examples provided during the presentation.

Segment Visitors by Number of Keywords Searched

Nick starts by describing what is a Regular Expression and then he gives a sample that can be used in order to create a segment of 2-3-4 or more keywords: ^(W*\w+\b){3}\W*$ If you change the number '3,' it will change the number of words in the keyphrase that is supplied by Google Analytics.

According to him, if you start looking at phrases that have multiple words – long tail keywords - and plot them out year-over-year, you can get an idea of what's successful. Shorter keyword phases get the most traffic – that's the head. Longer phrases, however, convert better for meeting goals.

#1 Money-Saving Advanced Segment.

How do you know if you're paying the right amount for specific keywords when advertising on Adwords? Start by including visits from ad position 1. Then compare them to ad slot position 2, 3, 4, and 5. You'll be able to see cost and conversion, depending upon AdWords position. The first position almost always gets the most clicks, but the conversion rate may be better in later positions. Then you can drill down and find which keywords convert better in each position.

Branded vs. Non-Branded Keywords Segment

Show me only branded keywords either by including or excluding brand terms. You can see how brand terms perform.

Traffic Sources Bringing High-Value Visitors

This will look for visitors that looked at 2 pages or more or spent greater than 200 seconds on the site. These are highly engaged people. If you look to see where highly engaged traffic comes from, you can find out where your efforts need to be.

Nick Iyengar - Senior Consultant, Cardinal Path

Nick Iyengar is a senior consultant at Cardinal Path, as well as a veteran instructor and regular speaker at analytics industry events. As a consultant, Nick has worked with organizations ranging from Fortune 500s to non-profits to design and implement customized web analytics solutions, launch and analyze website experiments, and leverage data to optimize online marketing performance.

Nick has provided in-depth guidance to hundreds of North American businesses and organizations as an instructor for Google AdWords, Google Analytics, and Google Website Optimizer seminars, and as a speaker at Google's "Get Your Business Online" series and analytics industry events like GAUGE.

He began his career in the online marketing industry in the mid-2000's, when he joined Google's AdWords team in the Mountain View, CA headquarters. There, he spent more than three years managing tens of millions of dollars in ad spend for top-tier Tech B2C clients. He also mananged Google’s “Analytics Guru” team, applying Google Analytics data to improve campaign performance for top AdWords advertisers. Nick holds a B.A. in Government from Connecticut College.

Segmentation is the mother of all website optimization. That's an extreme statement, but I believe anyone in the field would think twice before disagreeing with it. Segmentation enables us to understand who are our customers and how different groups of people behave on the website; this information is extremely valuable in order to provide a a richer and more relevant experience to customers. The result is a happier customer, a more profitable website, and more bonuses to Web Analysts!

That's why Avinash Kaushik is so vehement when he writes: The Choice Is Stark: Segment Or Die! According to him: "if you simply sit and ponder for a few minutes it will be clear that your website exists to do many different jobs and people come to your website to accomplish many different goals. So why analyze your data as one big ugly glob?"

Google Analytics provides several out of the box ways to segment your visitors into groups. For example, it is possible to segment by GEO location, Browser, Operating System, Traffic Source, Visitor Type (New vs. Returning), Content Viewed, Goal Completion, and the list goes on...

But beyond a shadow of a doubt, the most powerful segmentation feature on Google Analytics is the Custom Variable. I like to play with the idea that Custom Variables are so important that they should have their own tab in the Google Analytics sidebar navigation, and that this tab should be significantly prominent. Here is a mockup for the Google Analytics team, feel free to use!

Below I discuss a proper definition of Custom Variables, where it should be used, how to plan its implementation, and some additional tips and tricks.

Custom Variables Definition

According to the Google Analytics Code site, Custom Variables can be defined as:

Custom variables are name-value pair tags that you can insert in your tracking code in order to refine Google Analytics tracking. With custom variables, you can define additional segments to apply to your visitors other than the ones already provided by Analytics.

The important part of this definition is that you, as the website owner, can define the segment that you want to apply to specific visitors. The missing part of this definition is that you can apply it to your visitors only after they actively do something on the site. For example, you can define someone as a male or female only after he or she fills in that information on the site. That may seem obvious, but it is important.

Custom Variables Model & Hierarchy

The power of Custom Variables is partly due to its hierarchy, the possibility to define whether the segment you are creating will last only for the current page visited by a visitor, the current visit of a visitor, or "to infinity ... and beyond!" (i.e. for as long as visitors keep their cookies).

I will go over some examples below, but before it is important to understand how Custom Variables are defined, their grammar. First, as mentioned above, we can use different levels, meaning that the variable will be saved for different periods of time:

• Visitor Level— this will modify the visitors' cookies for as long as they use the same browser and device and do not erase cookies. As we will see below, we use this level to keep unique information about a visitor, such as subscriptions, purchases, and demographic characteristics provided by visitors in a form. A way to overcome the limitation of multiple browsers and devices would be to update the visitor's cookies every time he or she logs in to the website.
• Session Level— this modifies the visitors' cookies for the period of time during which the visitor is active on the site. Very useful to track internal campaigns, such as banners on the homepage.
• Page Level— this modifies the visitors' cookies for activity on a specific pageview or event. Usually it will be used to group behavior in different sections of a website.

But before you start jumping out of joy, please note that if you are using the free version of Google Analytics you have only 5 slots. That means you have to think carefully before you implement them. If you are a lucky person, and you have Google Analytics Premium, you will have 50 slots available, which is a lot!

As I mentioned above, this is not an out of the box feature, so you will need to implement code in order to collect this data. Below is the code and the explanation for each parameter:

_setCustomVar(index, name, value, opt_scope)

index (required) — The slot for the custom variable, it can range from 1 - 5 for standard accounts or 1-50 for Premium accounts.

name (required) — The name for the custom variable, a string that identifies the custom variable and appears in the top-level Custom Variables report of the Analytics reports. For example, if you are using a slot to track gender, the name would be 'Gender'.

value (required) — The value for the custom variable, it appears in the table list of the UI for a selected variable name. Typically, you will have two or more values for a given name. Using the 'Gender' example above, the values would be either 'Male' or 'Female'.

opt_scope (optional) — The scope for the custom variable. As described above, the scope defines the level of user engagement with the site. It is a number whose possible values are 1 (visitor-level), 2 (session-level), or 3 (page-level). When left undefined, the custom variable scope defaults to page-level interaction.

Where Should a Custom Variable Be Used?

As mentioned above, standard Google Analytics users have only 5 slots available. So it is important to plan Custom Variables implementation effectively. The most important thing is to be careful when overwriting slots.

For example, if you use slot 1 to track visitors who register to the website, do not use it to track visitors' gender as well, one will overwrite the other. However, you might consider using slot 1 to track Purchase, as if you know someone purchased something, you don't need to know he ius registered anymore, so overwriting it is OK. For visit and page level variables it is easier plan, but note that if you trigger two different variables in one visit, the last one will get the full credit for all the visit.

Below I provide examples of usage for each Custom Variable level.

Visitor Level Custom Variable

Let's suppose you have the following registration form on your website (screenshot below). Your visitors have to enter all that information in order to register, which means you can use any of it in your custom variables, right? Wrong! The first rule you need to know is that tracking Personal Identifiable Information is against Google Analytics Terms of Service (see section 7). Therefore, you should not add emails and names to your custom variables.

Using the example above, you would probably want to track the following using a Visitor Level Custom Variable:

1. Account Type: tracking whether a particular visitor is a 'musician' or 'personal' would provide important insight on how differently these groups behave on the website. Apparently this is an important piece of information for the business since it is high up in the form.
2. Birthday: tracking the birthday year would segment visitors by age, which could be valuable when providing targeted content to visitors.
3. Gender: important to understand which content is valuable to which gender. Men and women behave very differently on the web (here is research proving it).

Visit Level Custom Variable

Visit Level Custom Variables are extremely valuable when tracking internal campaigns. Let's suppose you have an Ecommerce website and you are advertising different products on your homepage. If you trigger a Custom Variable (using an onclick event) for each of the product in your homepage you will be able to understand which products are driving more sales from the homepage. This will be extremely valuable when defining how to organize your content in a data-driven way.

Please note that while this might sound like Science fiction to some people, the implementation should be very simple (considering standard conditions for temperature and pressure). Here is a similar code of what you would add to you HTML link tag: onclick="_gaq.push(['_setCustomVar',1,'Home Campaign','Galaxy Nexus',2]);"

Page Level Custom Variable

Page Level Custom Variables should be used in order to group actions performed in different sections of the website. Here are a few examples:

1. Aggregating authors: for content websites with multiple authors, the Custom Variable can be used to group posts by author in order to understand which authors drive engagement.
2. Aggregating categories םר subjects of interest: for all types of websites, this would be useful to learn which brands or category of content (such as tags or product type) is driving engagement.

Custom Variables for Ecommerce

When it comes to using Custom Variables for Ecommerce websites, Justin Cutroni provides a very useful list of ideas (implementation and analysis details on this link):

1. Tracking Coupon Codes and Promotional Codes
2. Tracking Payment Method
3. Tracking Shipping Method
4. Tracking Repeat Customers
5. Purchase History

Advanced Tips & Tricks

Here are four advanced issues you should keep in the back of your mind:

• Call the _setCustomVar() function BEFORE a pageview or event, otherwise it won't be sent to Google Analytics.
• Do not use duplicate key names across slots, this will mess with the data.
• You can trigger up to 5 simultaneous custom variables in a request (e.g. pageview or event ).
• The name-value combination must be less than 128 characters.

Concluding Thoughts

As mentioned above, Custom Variables are the most powerful segmentation feature on Google Analytics, it enables marketers to understand their visitors better. On The Choice Is Stark: Segment Or Die! Avinash starts by saying:

There is a reason your analytics data does not make any sense. There is a reason you are unable to find an iota of insight that you can action. There is a reason you feel data is your enemy. You see, you are not following one of the holiest of holy covenants when it comes to data analysis: Segmentation.

If you are among this group, get started now!

Related Articles

1. Audience Segmentation - Giving Clicks a Personality
2. 3 Segmentation Strategies: Intent, GEO & Conversions
3. Understand Google Analytics Advanced Segments [video]
4. Data Segmentation with Google Analytics [video]
5. Successful Website Personalization

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"It's a flexible structure within Google Analytics that allows you to have a set of data that's most meaningful to you, and your colleague to have a set of data that's most meaningful to her."

How does Google Analytics work?

The basic tenet is that you go to a website and request a web page => A JavaScript code is executed and a call is send to the Google server => Available information about the user is saved on Google Analytics (such as which browser, GEO location, referrer, time stamp and other).

A Google Analytics account can be for one person or for a team. Profiles can be set up by websites or by functional area, so that you can see sales on all sites rather than having to analyze each site's sales.

Security tip: Be sure to be careful about generic email addresses (like company @ gmail.com) where you don't know for sure who has access to the email. It's important to keep control of email addresses, so use private email addresses and always check if those people still "deserve" access.

A profile can be by site, by functional areas, or both. Page tags go on the page to make the data available. Building proper profiles is a very important part of using the Google Analytics account. Below is an image by Google on the structure of accounts, read more at this code article.

Below are two very important tips on creating profiles for an account:

1. Always have a RAW profile with no filters, and all segmentation can be done later.
2. Create at least one profile which filters out all internal traffic, to see the real customer traffic.

Alex and Charles go on to show how to analyze traffic sources and how to navigate the Google Analytics interface, both in terms of graphing and of settings. Check the video to learn more.

Alex Langshur - Co-Founder & Senior Partner, Cardinal Path

Alex Langshur is a co-founder and Senior Partner of Cardinal Path, a web analytics, search optimization and marketing consulting firm. Cardinal Path is the largest pure-play digital performance measurement firm in North America, committed to helping clients leverage their web, mobile, social media, email and search initiatives to turn convert visits to value, and prospects into customers.

Alex is a sought after speaker having keynoted and presented at digital marketing conferences around the world. He is the past-President and a current Director Emeritus of the Web Analytics Association (WAA), and a member of the Emetrics Marketing Optimization Summit Advisory Council. He teaches courses on web analytics, search optimization and digital measurement for University of British Columbia, GSA Web Manager University and WAA Base Camp, and has been published in journals and magazines including Government IT Week, Government Executive, CNN Money and others. Alex lives in Boston with his wife, two sons and golden retriever.

Charles Davis - Solutions Director, Blast Advanced Media

Charles is an entrepreneur at heart and Solutions Director for Blast Advanced Media. He works directly with clients to understand the most important details of their business and how we can best meet their demanding needs. With a background in enterprise level software systems, an understanding of the software lifecycle, and having started and operated a successful small business, Charles connects the business needs of each client with the skills of our diverse team to provide the maximum effect on the revenue and profitability of each client. Charles’ areas of special interest are project design, project management, website optimization, and developing strategic partnerships with great companies.

Web Analytics implementations are never ending stories, there is always a small fix or a new feature that requires changes to the code or to the settings of your Analytics tool, always. And in order not to lose the thread, the analyst must be extremely organized, a real pedant. In this article I will discuss some of the commonest mistakes when it comes to Google Analytics implementations and I will provide some tips on how to avoid losing data and context.

First, it is essential to have a place where you can test changes to your settings and configurations. It is also important to keep track of changes in a way that they can be used to provide a context for analysts, so that when you are looking at incomprehensible spikes in past data, you can check whether any changes were made to the data collection methods (or if an offline campaign was in place during the period analyzed).

Below I start by describing the structure of accounts on Google Analytics (important to understand the tips provided) and then I dive into some common mistakes when it comes to settings and configurations on Google Analytics. After the basics are behind us, I proceed to describe four techniques that will help analysts and marketers to improve their data accuracy. I provide examples on how this can affect the data, and share tips on ways to make them happen.

Google Analytics Accounts And Profiles

The Google Analytics code site offers an in depth explanation of the hierarchy used by the tool to manage report access and data collection. There are three important levels that we need to be aware of:

1. Account: An account is the mother of all Web properties and profiles, and has a unique account ID that can be used to track multiple websites.
2. Web Property: The web property has a unique ID, which is a combination of the account ID and additional digits. Since different Web properties have different IDs, their data cannot be merged.
3. Profile: the gateway to the website reports. It determines which data from your website appears in the reports. Filters can be applied to profiles in order to segment the data; for example, it is possible to create a profile only with visitors from USA, only from new visitors, etc. Since profiles use the same account and Web property IDs, data for multiple profiles can also be seen in aggregate.

Below is part of the scheme provided on the code from the website mentioned above. The image well represents the possibilities of data collection and management.

Making Sure The Google Analytics Code Is Safe

Google Analytics Implementation: Top 3 Common Mistakes

The list below is obviously not comprehensive, there are numerous issues that might go wrong in a real life implementation. But if the issues below do not occur in your website, chances are that your data is OK. The three points were taken from a larger 10-point checklist I wrote a few months ago. The checklist was more focused on the Google Analytics settings that should be configured from inside the tool, so if you want more tips on how to setup your Google Analytics account properly, check the full checklist.

Google Analytics tracking code missing in some pages

This should be a simple task if you follow Google Analytics recommendation: "copy and paste the code snippet into your web pages". However, commercial sites are more complicated than a handful of pages, so if you manage a complex website be sure to check both HubScan and ObservePoint, two good solutions to audit your tag implementation.

Google Analytics tracking code not customized for Multiple (sub)domains

It is a common practice to use subdomains and multiple domains in order to organize the content and properties of an organization. Unless setup correctly, this can decrease data accuracy and prevent data streamlining, especially when it comes to traffic sources, new and returning visitors, and custom variables. Use this guide to configure your Google Analytics if your site spans multiple domains or subdomains.

Internal traffic (your employees) are counted

Most often, websites are visited by a wide range of people. However, it is also visited by your employees and service providers – and these are not the visitors you want to understand and optimize for. Therefore, create filters that exclude the IP range used by your organisation and its service providers such as web development and marketing agencies.

Creating an Analytics Staging Profile

Let's suppose you read in a random blog that you should create a filter to lowercase URIs for all of your profiles (if you don’t have one yet, check point five on the 10-point checklist). And suppose you have no idea how this can impact your data.

The best way to learn how filters affect your Google Analytics data would be to have two profiles with the exact same settings (the real profile, and the test profile) and apply a new filter only to the test profile. Once it is applied, you can check the data and compare the number to learn if anything went wrong. Here is an article from the Google Analytics Help Center on how to add profiles.

Creating An Analytics Staging Account

If you work in the Web Analytics field long enough, you have certainly experienced data loss as a consequence of bad implementations. It happens, and the best we can do is to have a Web Analytics Process in place that will help us avoid it.

Code changes should be dealt with carefully. Since most websites do have a staging site where changes are tested before going live, I suggest having a different tracking code used for those environments to test code changes on the Google Analytics script (i.e. a new Google Analytics account). This technique is very similar to the one proposed above to check filter changes: it only goes one step further.

Tracking Profile Changes & Configurations

When it comes to both external and internal changes, context is one of the most important factors for analyzing data. For this reason, it is crucial to have a log of changes that affect your data, as well as changes in marketing campaigns and other company efforts. Below I present two ways to keep this data in an accurate and accessible way.

Keeping Track of Internal Changes

Changes are constantly made to Google Analytics profiles by users: website goals, improved filters, new features, and others. Every change may impact data in several ways, even when not expected. For this reason, I propose a method that will help to keep track of those changes, especially in large organizations where more than one person is involved with Google Analytics. Even when one person is involved, this is important as employees usually do not work with just one company "to infinity... and beyond!"

In order to make this task easy and centralized, I propose using a Google Docs form. Using such a form will facilitate the collection and sharing of the changes made to a Google Analytics account. The form should be created so that multiple teams will be aware of all changes. These will then be aggregated for historical knowledge that can be used by the whole team (and future teams members).

Below is an example of such a form with fields that you might want to create (learm how to build a Google Docs form).

Keeping Track of External & Overall Changes with Annotations

Back in 2010, The Analytics team had announced a feature which in my opinion is one of the most important features of the tool: Google Analytics Annotations. This feature allows website managers to provide context for where the numbers live (the graphs on the interface), allowing for richer analyses. Here are some important occasions when you should use this feature:

• New offline marketing campaigns (e.g. radio, TV, billboards).
• Major changes to the website (e.g. design, structure, content).
• Changes to tracking (e.g. changing the tracking code, adding events).
• Changes to goals or filters.

While annotations can (and should) be used for technical changes to the website (as mentioned above), it is important to keep them at a high level. This means that you shouldn't add too much information about your changes, just the overall picture; otherwise the annotations will quickly become overcrowded. Therefore, the use of both methods described above (form and annotations) should create an optimal mix. Below is a video explaining how to use the Annotations feature:

Closing Thoughts

In this post we discussed ways to avoid bad implementations by putting into place a process that requires users to report on changes made to their Google Analytics accounts. This not only helps avoiding mistakes, but also helps finding the source of problems, and solutions for fixing them quickly.

Google Analytics is a great tool, and one of its greatest qualities is that it makes Analytics ubiquitous, most people in any organization can use it; from Management to Marketing to IT. This means that many hands must deal with the tool, which requires an easy way to deal with the changes to those tool settings and configurations. Hopefully this article has provided some ideas on how to do it.

Related Articles

1. Getting Started with Google Analytics [video]
2. Google Analytics Planning [video]
3. Google Analytics for Mobile Websites and Apps [video]

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"Perform stakeholder interviews to understand what they need to know. It's important to start before your implementation so you can provide value on that and minimize future work that you're going to end up doing anyway."

Joe Christopher, Analytics Director and Partner at Blast Advanced Media, speaks on tracking e-commerce on Google Analytics. His talk focuses on:

1. The business requirements
2. Tracking recommendations
3. Tracking e-commerce transactions
4. Powerful e-commerce custom variables

1. The Business Requirements

It's important to clarify goals at this stage. This is the stage where you talk to business users and ask them what they need to know. For more information look at Blast's Business Requirements Page

2. Tracking Recommendations

1. How far do people get through the conversion process before dropping out?
2. What are visitors looking for on your site?
3. How does on-site social engagement impact conversion?
4. How are the newsletter and other channels performing?
5. Are visitors looking at the product feature tabs on my site?
6. If the visitor enters an invalid promo code, do they still convert?
7. Do visitors who chat for help end up converting?

3. Tracking Ecommerce Transactions

Some issues:

1. Where do you place the code? – On the confirmation page.
2. Be very careful with Google's requirements. You must have an SKU, and it must be unique. Commas in a number will be read as decimal points. Currency signs create problems. Don't use them.
3. Some common issues include non-unique SKUs. JS errors, programming errors, reloads of receipt page, and users who have JS or cookies turned off.
4. Since Google Analytics does not track multiple currencies, it is best to choose a master currency for overall reports, and then have the currency defined in an affiliation parameter. You can also set up different profiles for each currency and/or set up advanced segments to track the currencies.
5. Some creative uses for the affiliation parameter include tracking the user's hostname, original currency, payment method, and shipment method. This helps you perform multi-directional analysis on transactions
6. Three options for tracking discount values are tracking item prices and transaction total after discounts (per item), track an additional item with a negative value, and call it discount, or track an event to indicate the promo code, transaction ID, and integer value.

4. Powerful Ecommerce Custom Variables

Recommendations:

1. Track purchasers and purchase count (code snippet at 44 min)
2. Track Logged-in Customers (code snippet at 45 min)
3. Track Newsletter Subscribers (code snippet at 46 min)
4. Track Conversion Optimization Tests (code snippet at 47 min)

Takeaways

• Perform stakeholder interviews to understand what they need to know and what makes them successful.
• Start tagging your campaigns tomorrow!
• Audit your ecommerce transaction data to see how accurate it is.
• Leverage custom variables to extend what GA tracks and perform deeper segmentation and analysis

Joe Christopher - Partner & Analytics Director

Joe Christopher, Analytics Director & Partner at Blast Advanced Media, approaches every project with a deep understanding of Google Analytics. Joe has an extensive technological background and proven experience that has earned him the title, "Mr. Master of Everything Web." He is a master of ASP.NET, MySQL/MSSQL, JavaScript, and many other programming languages and technologies. Joe also has a keen eye for website optimization with a focus on usability.

Joe graduated from California State University, Sacramento with a degree in Digital Multimedia. Always striving to learn more and enhance his skill set, you will probably find him with his nose in a book or reading up on his favorite tech blogs. Outside of geeky stuff, Joe enjoys trying new restaurants, mountain biking, and spending time with his lovely wife and family.

So, you're minding your own business, sifting through a client's analytics data for some epic, revelatory insight that'll blow their minds. All of a sudden, you get this feeling that something weird is happening. You could've sworn your client had 3,132 visits from Canadians who found you with organic search, but you have a few advanced segments on, and now the traffic with the exact same filter conditions is actually 2,984 visits.

You flip back and forth between the two reports with a pit forming in your stomach. Is there something wrong with my profile filters? Has something gone horribly wrong with the tracking code I gave the client to put on their website? Is Google Analytics... unreliable? How am I going to explain this to my client?

Nope, none of the above - your data is being sampled.

Note: If you see any concepts or terms in this article you're not terribly familiar with, try consulting my web analytics primer

What Is Data Sampling?

Sampling is a process by which a part of a larger body of data (a sample, if you will) is used to extrapolate the rest of the data. While sampling can't produce 100% 'down-to-the-last-digit' numbers, it is accurate enough to spot trends in the data - the ups and downs in a sampled dataset will usually correspond and be proportional to the ups and downs of the full dataset. Sampling, because it processes a smaller amount of data representative of the whole, lessens the load on the computers doing the processing.

When Does Sampling Happen?

Sampling occurs when you attempt to filter or manipulate a particularly huge amount of data from a large number of visits, or a large number of visits and using a complex set of filters in custom reports or advanced segments.

Why Does Google Analytics Sample Data?

Google does this because even their servers can't always be expected to hold endlessly-large datasets in a finite amount of working memory. Imagine trying to solve a complex mathematical equation in your head. Certain numbers divide pretty easily into one another, and you can handle those pretty well. But complex numbers with decimals might make things a bit too complicated to keep in your short-term memory, and now you have to resort to a calculator or a piece of paper. Same thing goes with computers... except they don't have anything else to turn to when their 'short-term memory' gets overwhelmed.

Note: Your data isn't affected or changed in any way by sampling - the only difference is in which set of data Google has brought to the surface in its report.

Why Are My Numbers Different When The Filters Haven't Changed?

This is why you might end up looking at a number in one of Google Analytics' standard reports, and then when using custom reports or advanced segments, the number is different when it should be exactly the same.

Here, we have a volume of data which has been filtered for a particular country - my lovely homeland of Canada.

In this next image, you'll see that when I applied an additional filter, sampling was used to try and lessen the burden on Google's servers. Because of the sampling, the number of visitors from Canada is different from that seen in the previous image.

At What Point Does Google Analytics Start Sampling?

Sampling kicks in at different points depending on which version of Google Analytics you happen to be using:

• Free: More than one million unique dimension combinations in standard reports, or more than 500,000 for special queries such as custom reports, advanced segments, or inline filters, or any other such cases where the data has not been pre-stored and pre-aggregated.
• Premium: More than 50 million unique dimension combinations in any type of query. Learn more about Google Analytics Premium at analyticspremium.com

Note that sampling is done at the web property level and not at the profile level. So even if you're manipulating data at the level of the individual web property, the settings on the profile (such as profile-based filters) won't apply - the original data captured before profile-level filtering is what the decision of whether or not to sample is based on.

Can I Prevent Google Analytics From Sampling?

The level of sampling in your data can be adjusted, but never eliminated. You can tweak the balance between higher accuracy and lower processing time. If you use a larger sample to increase the accuracy of your data, you'll have to wait longer for your report to load. If you use a smaller sample because you're in a hurry, your data might not be entirely accurate - just accurate enough to spot trends in the data.

Step 1: Select the square icon, made up of a bunch of very small squares, just below the date.

Step 2: Adjust the slider to taste - further to the left means faster load but less accuracy, and further to the right means slower load but more accuracy.

Step 3: Wait for your report to load back up.

Another way to avoid sampling would simply be to select a date range which would include less than 500,000 visits. This could be done by finding a way to perform automatic daily exports, whose data could then be used in data analysis in a specialized tool outside of Google Analytics.

How Much Sampling Is Too Much Sampling?

It's necessary to stress that sometimes a difference in the exact number between reports may not be a problem. After all there are differences, then there are statistically significant differences. The statistically significant differences are the ones you need to worry about, as they can't be explained away by sampling - if the difference in numbers is large enough to be statistically significant, then you need to start worrying about the way your analytics tool is set up.

Analytics legend Avinash Kaushik has an article on statistical significance and a helpful spreadsheet to help you calculate whether or not a particular difference really matters.

As usual, please leave a comment below if you have any questions or any new analytics mysteries for me to dig into.

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When you think about redesigning your website, you need to have a plan. To come up with a plan, you need to know what's going on. Web analytics can be an invaluable tool to help you decide what to do with your website, and to do it better this time.

Analytics data can show you where your website was doing well before, so you can continue doing the same things well, and it can show you where your website was struggling, so you can find ways to make sure you correct those issues this time. While any web analytics tool can help you get the sort of information you need to redesign your website the smart way, we're going to be using Google Analytics as an example.

Here are four questions, among many, that web analytics data can help you answer, and that can give you a lot of insight into how you can make sure your next website is better than the last.

1. Is your website working properly?

Do you want to know what the best visitor and conversion repellent is? It's a slow, glitchy page that spews 404 "not found" errors every other link click and that doesn't work as a user would expect it to. But how do we know which pages are dragging down the rest of the site?

Site Speed Report
Content » Site Speed » Page Timings

This report will tell you how quickly (or slowly) your pages tend to load based on a sample of past visits. Create a custom report that measures site speed against bounce rate and exit percentage, and find out exactly what pages are costing you the most opportunities.

Customized 404 Error Reporting
Content » Site Content » All Pages
-OR- Content » Events » Overview
Note: A web developer's help may be required to implement this

By setting up a virtual pageview or an event rather than the standard _trackPageview call in the Google Analytics tracking code, you can gather a few pieces of information that will help you narrow down the reason and source of that error. Most of the time, 404 errors are caused simply by typos, either by the person who created the link from a referring website that the visitor used to get to your site, or by the visitors themselves. Other times, though, it may be symptomatic of internal linking issues and broken links from one page on your website to another. These cases can be solved much quicker when you and your webmaster know they exist.

Exit Pages Report
Content » Site Content » Exit Pages

This report will show you on which page most visitors leave your website or, more accurately, the last page they visited on your website. The pages with the highest percentage of exits (the percentage of exits compared to all pageviews is known as the 'Exit Rate'), are the most likely to have issues which are most likely to be repulsive to visitors. Have a look at the Site Speed report for that page, as it might be taking too long to load. Also, try selecting 'Browser' and/or 'Browser Version' to determine whether one type of browser is producing a higher exit rate than others - that webpage may have elements on it that are incompatible with particular web browsers.

2. How is your website attracting visitors right now?

Learning where your visitors are coming from is essential in several ways. You may find out whether a particular advertising channel has been working or has been wasted.

You could find out whether your SEO needs to be improved on your next website by looking at the number and percentage of visitors arriving through organic search. If you're attracting a lot of organic traffic, you'll need to make sure you have an SEO expert's advice on how not to lose your 'link juice' when you redesign your website: if the URLs are different after the redesign, you may lose your traffic, and you may have to build up your SEO ranking all over again.

You might even stumble on whole online communities of potential customers, donors, or adopters (as the case may be) where someone has posted a link to your website; you could start participating in conversations and answering spontaneous questions in order to prove your worth to their members, who are already predisposed to taking a look at your content due to their existing and relevant interests.

Traffic Sources
Traffic Sources » Overview

The traffic sources overview will give you a high-level view of the types of sources feeding traffic into your website. For example, Search traffic (traffic from search engines), Referral traffic (traffic from other non-search engine websites), Direct traffic (traffic from the address bar or bookmark in a visitor's browser), and Campaigns (traffic from your own online marketing or advertising initiatives.

Referrals Report
Traffic Sources » Referrals

This report will go deeper into just the traffic your website has received from other websites, and will also help you understand the quality of traffic each site is providing you with. For example, website entries with a noticeably and significantly higher Pages/Visit, Avg. Visit Duration, and a noticeably and significantly lower Bounce Rate are feeding you high quality traffic from visitors who are very likely well aligned with your content, your product, your service, or your cause.

3. What are your visitors looking for?

If you don't know what your visitors are looking for, chances are good that you don't know what to give them. Whenever your visitors come to your website by organic search or use your on-site search box, they are explicitly telling you what they want. Listen to your visitors, and you're well on your way to making them much happier.

Site Search Report
Set Up: Admin » *Your Main Profile* » Profile Settings tab » Site Search menu near bottom
To View: Content » Site Search » Search Terms

If your website has a search bar, you need to set up Site Search in Google Analytics. If you thought organic search gave you huge insights into what your visitors were looking for when they found your website, you're going to love this. Not only are the visitors who use your on-site search bar already on your website, they're also telling you what they're looking for! All you have to do in order to listen in on this and gather some important information is to set up Site Search. Here is how:

1. Do a search on your website and pay attention to the URL in your address bar. Look for a question mark and then either a 'q' for 'query' or 's' for 'search'. The term after the equals sign and whichever letter is in your address bar should match your search term.
2. Go into your Google Analytics administration panel, select your main profile, and click 'Profile Settings'.
3. Scroll all the way down to the bottom and find the header 'Site Search Settings'. Select 'Do Track Site Search', and add just the letter that came before your search term to the field 'Query Parameter'.
4. Check the box 'Strip query parameters out of URL', which will keep you from getting a huge number of separate entries in your reports for 'search.php' or whatever your search page is, and hit 'apply'.

You should be able to, in a few hours or up to a day, depending on your usual traffic volume, head to Content » Site Search » Search Terms to find out what your visitors have been searching for.

Make sure to keep the most-searched-for items in mind when you go to design your new website: you should make sure these are easier to access once you're done.

Organic Search Report

Traffic Sources » Search » Organic

This report will show you what your visitors were looking for when they performed the Google search on which your website was listed, and which provided the link they clicked to get to your website. If you find your website associated with terms that don't apply to or accurately describe your website, its purpose, or its content, you may need to seek an SEO specialist to go over your website content and determine a course of action that will properly align your content with your intended audience. When you get to the point where you need to write content for your redesigned website, you will know which terms to sprinkle into your content, and which terms to avoid.

If you happen to be seeing a large proportion of organic search keywords recorded as '(not set)' or '(not provided)', this article by Scott Shannon provides some advice on how to deal with it and mitigate its effect on your digital intelligence somewhat.

4. Which parts of your website are working?

It's important, when redesigning your website, that you don't throw the good parts of your website away along with the bad. Determine which parts of your website most helped you achieve your goals, and learn from that to do those same things the right way in the planned redesign. Here are a few ways you can use Google Analytics to help you spot your website's best pages, content and attributes.

When you decide to redesign your website, you also have to decide what role it plays in your overall business strategy. Do you want to get large sales leads because you supply products to distributors, or provide services to enterprise customers? Do you want to sell product to anybody and everybody who comes to your website? Do you want to get a message out to the world, and your website is the billboard? Without a clear goal and a clear mission for your website, your redesign project will simply be a waste of time and money.

These reports will help you find out what your website is doing right:

Event Tracking
Content » Events » Top Events
Note: A web developer's help may be required to implement this.

Events are a powerful and flexible tool provided by Google Analytics to track visitor actions that aren't easily covered by the standard reports. With events, you can find out whether there was a particular link or button your visitor used that tends to start conversion processes more often than usual. With a series of events and some code to trigger them when someone changes a user input form (like a contact page or a shopping cart), you can even find out exactly which part of the form causes visitors to abandon the conversion process most often, and take measures to correct how that field works or how that question is asked. To learn more about event tracking read Really Understanding Google Analytics Part 4 by Kent Clark.

Goal Reports and Funnels
Set Up: Admin » *Your Main Profile* » Goals tab
To View: Conversions » Goals » Funnel Visualization

Determine what visitor actions lead to or contribute to a successful conversion or microconversion, then set up a goal and a funnel for it. Head to the administration panel, select your profile, click the 'Goals' tab, and add the URI (the part of the URL that comes after your domain name, starting with a backslash) that your visitor would ONLY see after a successful conversion (ie. subscription confirmation page, purchase receipt page), select 'Head Match', click 'Use Funnel' then enter in the URIs of the pages that a visitor would have to visit in order to get to that conversion below. Make sure you click the checkbox that says 'required step' next to the first step of the funnel to avoid anomalies in your goal data. This sets up your goal funnel. Now you can see at which step your visitor abandoned the conversion process, and check that step to see if it can be made easier (or more psychologically palatable) for your visitor to complete.

Pro tip: to check whether the right goal URLs are being tracked, head over to Conversions » Goals » Goal URLs to check which URIs are triggering your goals. If you don't see the goal URI you set earlier down there, but that particular goal has been completed, your goal funnel may not be configured correctly. If you see unfamiliar URIs in the Goal URLs report, this is another sign that your goal funnel may not be configured properly.

E-Commerce Reporting
Conversions » E-commerce » Transactions
Note: A web developer's help may be required to implement this

If your website is selling a product or accepting donations or money of any sort, you have to set up e-commerce tracking. It's very important that you ensure you're able to correlate transaction data with regular Google Analytics visit data. With e-commerce tracking set up, you can find out which landing page, campaign, traffic source, or other event resulted in the largest, quickest, or most profitable transactions.

E-commerce setup requires you to put code on a couple of parts of your page and ensure that they work with the server-side code for your shopping cart. Some shopping cart vendors won't make additions or changes to their code or services, but some will, and still others are quite familiar with Google Analytics e-commerce tracking and have built up easy ways to use it with their shopping cart. Make sure you look into this when looking for a new shopping cart for your e-commerce website. Learn more about e-commerce tracking on Nick Iyengar's post on the subject.

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It is considered obvious and necessary for businesses to have an online presence of some sort. Accepted business practice also dictates that website performance is measured in some way, usually through Digital Analytics data. For example, you may ask "how many visitors came to my site last month?" Thinking a little deeper you may also ask "I've spent x dollars on this new website... what's my return?" This is where digital analytics should tell you the answer.

But... It's often not that simple. Think about a popular site such as Amazon. How does Amazon determine the performance of their site(s)?

Hard cash is a reasonable example and who would argue? Smart analysts will not focus on the absolute value of the revenue metric though. Absolute metric values are not actionable and, as such, while they can answer your ROI questions they can't help you grow your business.

The key here is to look at the data trend - the change over time. Metrics become actionable and therefore useful when you apply context. Seeing how a metric has changed is informative, but seeing how a metric has changed over time when applied to a dimension such as traffic source is more than informative... it's actionable.

Think how much more powerful the revenue metric is regarding site performance, ROI and site optimisation when you analyze it per traffic source over a month and then compare it to the same month in the previous year - golden!

Now, this is all well and good for transactional sites; but not all sites exchange goods and services for money, so how can these sites extract meaningful and actionable metrics when revenue data is not as readily available? You may not be able to extract precise economic data but what if you could at least extract value trends? Actionable economic trend data for a non-transactional site, that would be powerful!

Capturing Value As Data Points In Analytics

We need to break this down to grasp the idea: the first challenge is to capture value as data points in your digital analytics. How? Whilst a site might not conduct transactions there is definitely going to be a set of desired outcomes. These are the activities users preform on the site that the business model depends on. Here are some examples of what those activities might be:

• Sign up for a newsletter
• Register
• Download (an app, wall paper, brochure or case study)
• Click to a partner site (affiliate relationship model)
• Read a blog article

It's worth noting that these outcomes may well be true of transactional sites too and the techniques described in this missive are still applicable regardless of the transactional nature of the site but we'll focus on non-transactional sites for this discussion.

Now, the business owner must build a site with these outcomes in mind. He or she should know that these are the actions that users need to take to make the business work. The difficult question is "what are the outcomes worth?" Two thought models are useful here. Either:

1. You know the monetary value of each site action.
2. Use relative engagement scoring as the best estimation.

If you're running an affiliate business model, you'll know that a click on a link to your partner sites will be worth x dollars. For example, if your site offers comparison deals on broadband offerings from your partners, they'll be able to tell you what a click is worth. The fictional example in the right shows how each click might be valued:

If this site was running Google Analytics, the clicks on these links would be measured using goals with each goal being assigned a value based on the economic worth of the click (see step 8 of this help center article to learn how set up goal values). Hence, no transactions take place but value data is captured.

What if you can't use this model? Your outcomes might be somewhat nebulous in their nature and the economic value is just too darn hard to work out. Then you could use relative engagement scoring.

Using Relative Engagement Scoring To Measure Economic Value

First, decide which outcomes need to happen, this will help understanding which outcomes are most valuable - not exactly what they're worth, but as compared to other outcomes. For example, a sign up is worth more than a brochure download. Using this knowledge, you will be able to apply a value of $100 to the most valuable outcome and score the others in descending value down to the least valuable (if possible, using a ratio, such as using $100 for a really important goal, $90 for a very important goal, $50 for an average goal and so on).

The table on the left shows an example of the result of such an exercise. The meaning of the absolute number is less important. You could look at the score as a grade, an economic proxy, a value analogue, a percentage or a weight of bananas! It doesn't matter. What does matter is that the outcomes have relative values that give you value data from which to extract contextual trend data and therefore actionable insights.

If you use Google Analytics, applying these values to goals will expose three super exciting and powerful metrics:

• Goal value (total value delivered by the site/action)
• Per visit goal value (OMG!)
• Page value (I need to sit down...)

Think about what having these metrics means: You don’t need to transact to see visitor value. You have actionable economic metrics on a non-transactional site. You can monetise your content using Page Value. Wow.

Consider the available metrics before and after applying relative engagement scores.

Before

After

Closing Thoughts

While the metrics available previously were essentially vanity metrics, now the metrics are actionable and they demonstrate return. You can see return on investment for campaigns. You can see the value your content represents - great for bloggers and content sites.

Using these techniques you will be able to analyse your data as Amazon does - using value as a KPI. Okay, the absolute figures are not great to use in your tax return, but the value trend data is seriously powerful stuff.

Related Articles

1. Tracking E-Commerce on Google Analytics [video]
2. Using Google Analytics to Lift Sales in E-Commerce Sites
3. Optimize Websites for Engagement: 5 Lessons [video]

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In this post I go over the new Google Analytics Content Experiments, a tool that can be used to create A/B tests from inside Google Analytics. This tool has several advantages over the old Google Website Optimizer, especially if you are just starting the website testing journey. Content Experiments provide a quick way to test your main pages (landing pages, homepages, category pages) and it requires very few code implementations.

Here is a quick overview of the most prominent features that will help marketers get up and running with testing:

1. Only the original page script will be necessary to run tests, the standard Google Analytics tracking code will be used to measure goals and variations.
2. Google Analytics advanced segments can be used to segment results based on any advanced segment.
3. Improved statistical engine for analyzing experiments, which will help making decisions faster about the winning/loosing pages.
4. Tests results will not appear for at least 2 weeks, a mechanism to encourage statistical significance.
5. Tests will automatically expire after 3 months to prevent leaving tests running if they are unlikely to have a statistically significant winner.
6. "Dynamic Traffic Allocation" functionality: traffic will be shifted away from low-performing variations, over to higher performing ones. This feature can't be turned off. This is to prevent poor-performing variations from doing extensive damage)

Below is a step-by-step guide on how to use Content Experiments to create A/B tests.

Create A New Experiment

In order to create a new experiment, navigate to the Content reports and click on the Experiments link on the sidebar. You will see a page that shows all your existing experiments. Above this table you will find a button Create experiment. Once you click on it you will reach the following page.

In this page you can add all the URLs of your original page and the variations you would like to test. You will see thumbnails of the page, which helps you making sure the URLs are correct.

Click Next.

Set Experiment Options

In the page above you will be offered a drop down with a list of all your profile goals, which can be used as a goal for your tests. If there is not an existing goal that is also the conversion you want to measure for this specific test, you will have to create a new goal to use in the test.

In addition to that, you can set a percentage of your visits that will be included in the test. If you are testing radical alternatives to an important page, it is recommended you don't try it on 100% of your traffic, it can damage your conversions... You can choose 100%, 75%, 50%, 25%, 10%, 5%, 1%. You can also add notes to your experiment.

Click Next.

Add And Check Experiment Code

As mentioned above, you will need to implement one code in order to use this tool. In the page above you can chose to either get the code to implement immediately or to send it to your webmaster.

Click Next and your pages will be verified. If they are not you will see the following error message.

Note that you will be able to skip validation if you want, just click on skip validation and continue. But it is recommended that you check the code to understand why you are getting an error and then try validating again.

Review Experiment

This page is a review of your article, showing all the decisions you took along the process. You can either Save and run later or Run experiment now.

Experiment Results

In the screenshot above we see how you can navigate through a running test. We have the following capabilities:

• Advanced Segments: as mentioned above, this is an extremely valuable feature, it enables you to understand better how each variation performs for each segment of visitors on your website.
• Stop Experiment
• Re-validate
• Disable Variation
• Conversion Rate: gives you the option to check the test results using alternative metrics.

And below we see the results page of a test with a winning version, the Road Runner (purple line), with a lift of 28.5% lift in conversions as compared to the original.

Reviewing All Experiments

Any time you want to review your experiments just visit http://onbe.co/IXznAO

Concluding Thoughts

All in all, Google Analytics has made a great job out of this new testing capability, especially for marketers that are still not testing often. For marketers that are more advanced there are still quite a few features missing. Here is a wishlist for future versions of this tool:

• Multivariate Testing capability
• E-commerce transactions as goals
• Remove limit of 5 variations per test
• Remove limit of 12 tests per profile at this time
• Option to use the standard tracking code for everything (one ring to rule them all)

What are you waiting for, start testing!

Related Articles

• Split Testing Framework
• Advanced A/B Testing Tactics That You Should Know
• Analytics And Testing: What Comes First?
• A/B Testing SEO friendly

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In a previous article at Online Behavior, Auria Moore discussed the Inevitability of the Tag Management System, where she described the irony that the more useful and relevant your site becomes, the worse it may actually perform. This happens because websites are continuously adding tags to enhance their tracking, optimization or other functionality; this ends up crowding pages with third party tags and may slower the website.

For this reason, the Google Tag Manager, a free solution for tag management, is very welcomed, especially by Marketers. In the past, everything was controlled by webmasters, including website tracking, which was highly technical as it required dealing with log files. Then, in 2005, Google made the JavaScript method widely available with Google Analytics, but it still required a technical integration by webmasters as it required adding codes to the website.

With Google Tag Manager, marketers will be able to add, edit or remove marketing and measurement tags without the intervention of webmasters. This will speed the process from the marketing perspective and will free webmasters to work on other important tasks.

Google Tag Manager Structure - Accounts, Containers & Tags

The structure used by Google Tag Manager is very similar to the one used by Google Analytics. In Analytics we have accounts that can contain several Web Properties (usually one per website) which can contain several profiles. Below is the hierarchy used by Google Tag Manager:

Accounts

The top-most level of organization. Typically, only one account is needed per company. Tags for all the company’s websites can be managed from this account by creating new containers. In order to create an account visit google.com/tagmanager and signup for the product.

To create additional accounts, sign in to your existing account, click on "Accounts List" (found on the drop-down on the top-left corner of the page) and click on the "New Account" button shown in the screenshot below.

You will be asked the account name, the container name, the time zone and the domains that will be used. As a best practice, the account name should always be the name of the company while the container name should always be the website using this container.

Containers

A container holds all the tags for a specific website; as mentioned above it should be named after the website it is being used for. To create a new container select an account and click on the "New Container" button on the top-right corner of the page as shown in the screenshot below.

You will need to add the container name, time zone and domain.

Tags

Once you create a container, you can start creating tags for your website. Google provides a few templates for its own tags, but it also allows custom tags to be used. Here is a list of tags supported by the tool, for an explanation about each of them check this help center article:

• AdWords Conversion Tracking
• DoubleClick Floodlight Counter
• DoubleClick Floodlight Sales
• Google Analytics
• GDN Remarketing
• Custom Image Tags
• Custom HTML Tags

In order to add a tag to a container, navigate to the container, choose the tags section and click on the "New Tag" button on the top-right corner of your screen. See indications in screenshot below:

You will then reach the tag page. In this page you will be able to chose between the different tag types described above. As I wrote above, Google created templates for its own tags in order to minimize the possibility of errors.

Once you decide which tag to add, you will be prompted with a few details to fill for each tag type: e.g. Conversion ID and Conversion Label for AdWords Conversion Tracking or Web Property ID, Cookie Path and Track Type for Google Analytics. You will also be able to click on "Add Rule to Fire Tag" as seen below:

The rule will define the conditions under which the tag is fired. For tags that should appear in all pages of the website (like Google Analytics) a rule can be created to URLs that match a Regular Expression ".*" Another common usage would be to add tags to the conversion page only, so you might want to add a rule for an URL that equals the page address. For more information on rules and macros check this help article.

Publishing & Versions

Once tags are added to a container they are not automatically published, they must be "manually" published. Publishing is always linked to a container version, which is a snapshot of the container that can be made at any time.

Clicking on the version number will lead you to a page summarizing all tags included in this version and the rules applied to them. This is an excellent way to keep the tags organized for troubleshooting tagging problems.

Users & Permissions

Users are added to accounts and, by default, have "No access" to all containers in the account. For each container, there are four types of user access:

1. No access: user does not see the container listed in the account.
2. View only: user sees the container listed and may browse the tags, rules, and macros in the container, but cannot edit them.
3. View and Edit: user may add and edit tags, rules, and macros in the container.
4. View, Edit, Delete and Publish: user may add, edit, and delete tags, rules, and macros in the container as well as publish changes to the live site.

In order to manage user access see the indications in the screenshot below.

Closing Thoughts

In summary, the Google Tag Manager is an important tool for online businesses, it makes marketing and measurement easier and more secure. In the words of Google:

"Google Tag Manager allows you to conveniently manage tags (such as tracking and marketing optimization tags) on your site. You can add and update AdWords, Google Analytics, Floodlight and non-Google tags from the Google Tag Manager user interface instead of editing site code. This reduces errors, frees you from having to involve a webmaster, and allows you to quickly deploy tags on your site."

Related Tag Management Content

1. Tag Management Systems and Website Innovation
2. The Inevitability of the Tag Management System
3. Tag Management Systems by Josh Manion (Ensighten) [video]

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Editor Note: this article discusses Universal Analytics, the new generation of Google Analytics, which is currently in closed beta. You can request access to the beta or read more on the official help articles. Also note that the screenshots are from the public existing reports, not from a new set of reports, they were added to help exemplify the concepts explained.

So, you are amazed at how Amazon personalizes their customer experience. And you have read article after article on how effective 1-to-1 marketing can be. Yet, when you review your own marketing initiatives, there is suddenly that realization that this personalization element is totally missing. When assessing your current digital marketing initiatives, you may find the following.

You are doing well at

• Your paid search campaigns, you are targeting searchers at various stages of the buying cycle with relevant ad copy and landing pages.
• Your e-mail lists are well segmented into leads and customers.
• Your media buys are very targeted to sites with visitors with affinity for your products.
• You have your analytics in order and your reporting in place for your campaign performance, site engagement and ecommerce.

You are not doing so well at

• You have little or no visibility into the behavior of repeat members and buyers.
• You have no idea how your customers interact across your different platforms (website, mobile, in-store, loyalty cards, etc.)
• The user-centric view of your customers (that you fantasized about after finishing your MBA) is a bit harder to get than you thought.

Google Analytics - User Centric Analytics

Whether you are doing great in some areas and poorly in others, there is always room for improvement. And having the ability to understand how users engage with your content across various devices, platforms, etc. is important. Focusing on creating a user-centric environment will vastly improve your 1-to-1 marketing with customers.

According to Google, their analytics platform "...provides tools that allows you to measure your effectiveness in advertising, sales, product development and retention across all your touchpoints." With the help of Google Analytics, reaching that user-centric environment isn't that hard to do!

In this post, I hope to shed some light on applying the user-centric concept to your online retail business. Note: this post is not meant to be a technical guide. For the technical implementation details, please refer to this article. I'll examine the four following scenarios:

1. One Visitor - Multiple Browsers
2. One Visitor – Multiple Devices
3. Visitors Buying Cycle Stages
4. Online and Offline Purchases

Let's get started!

1. One Visitor - Multiple Browsers

I don't know about you, but when I'm at work, not only do I have many tabs open on my default Chrome browser, I have at least two other browsers running at any given time. Then, when I get home, I grab a laptop to browse and shop, while watching TV. Now, to web analytics, all of these visits from the various browsers and computers look like different users, when in reality, it's just yours truly. It turns out I'm not the only one with this "multiple" browsing behavior. It's actually quite common these days. So how do retailers know if it's me, or multiple users? Let's examine this closely on the online retailer end of things.

Look at the snapshot below. This is a Google Analytics report for ecommerce, and is segmented by browser. You can see what appears to have three transactions that come from three different visitors (browsers). However, when you look at the first column with the unique customer ID, you'll notice the three transactions come from the same visitor.

For the site shown above, registration is required before you browse products and get exclusive offers. (And once the site owner has your email, you can rest assured that you'll receive "amazing deals" in your inbox going forward :-). This retailer has done his homework and knows how to create a unique ID for each visitor, and stores a Custom Variable which enabled us to stitch together the visits and get this new "user-centric" perspective. Now going forward, Universal Analytics Measurement Protocol will be our best friend!

What other insights can be derived? One of the KPIs for this retailer is the time elapsed between a customer registration and the first purchase. As you can see here, this visitor registered on September 19th, and they had 27 visits and then two transactions.

For the visitor below, this graph shows that it took 12 visits before the first transaction (and then just three visits before the second transaction).

With this type of visitor-level data that is now available, you can easily map the registration dates to the transaction dates and you'll have full visibility into the buying cycle. Now, you have the ability to use this information to your advantage and find creative marketing messages and offers that may help shorten the buying cycle. Plus, you can reach out to those who are not buying (and run all sorts of Lifetime Value analyses, cohort, RFM and others).

2. One Visitor – Multiple Devices

Another powerful use case for user-centric analysis is the ability to stitch together data across different devices, including mobile devices. One of the challenges for the site owner mentioned above is measuring campaign and site performance for users who are accessing the sites from mobile device, tablets and desktops.

Let's say you have visited their "Exclusive Offers" site on your desktop, you registered and now have an account. But, you decide not to buy now and want to wait for an "End of Year" special. Later, you are presented with a mobile ad with that special you've been waiting for. So you click on the ad.

Unfortunately, the site owner again requires you to sign up or sign in. Since you already have an account, you simply sign in. Without applying the user-centric concept discussed previously, this visitor will appear as a different user. You wouldn't be able to tell if the same person registered on one device, and then bought on another. Nor will you be able to report on their total purchase across devices.

Examine the screenshot below. Here, you see how the user-centric concept is being put to work. The user visited on mobile, and then visited on non-mobile as well. Again, this is super insightful, since you see the full buying cycle and activities across devices.

3. Visitors Buying Cycle Stages

Following the same ecommerce model (sites with exclusive offers that require logins and assigns user IDs to customers), you may want to consider taking it a step further and create your own user segmentation "flow". Start off with members (those who registered), then returning members and then buyers.

Look at the site references below. Instead of just reporting on some aggregate conversion rate (that looks ok but is really useless), the site owner can create this amazing view/funnel of the user segments and the associated conversion rates of moving from one customer segment to another.

Site visitors who become members (and now you know what drives more membership).

Then build a report on returning members and see what keeps them coming.

And last but not least, you can measure the true conversion rate of members to buyers.

4. Online and Offline Purchases

Another use case for Universal Analytics and user-centric analytics is integrating offline purchases with your online Web Analytics data. Depending on your environment, you might want to do the integration in your own database or data warehouse, but that's for another discussion. In this use case, we want to show how to bring this data into Google Analytics.

Let's take, for example, a retailer that accepts orders over the phone, in addition to online sales. During this process, the sales rep logs in the client info, orders the product(s), takes the credit card and then processes the transaction.

Here is how we now can tie it all together: when the client calls and is authenticated, a hit is registered and is associated with the client's unique ID in analytics. When the sales rep completes the transaction, the associated ecommerce transaction data is registered in analytics as well.

Since we assigned a unique ID for each transaction, we can later report on the visitor's purchases whether they completed the purchase online or offline. Again, the 360 view is completed.

This approach can be extended to include point-of-sale transactions in-store or from mobile apps and data loyalty programs. Each can be integrated in the mix.

Closing Thoughts

As you can see, user-centric analytics can be extremely helpful. It can tell you a lot about your visitors. But, it can go beyond that to tell you more in-depth information about their visits and their habits. It essentially gives you an overview of their behavior, which in turn, provides you with a better understanding of your customers.

Do you feel you have a solid understanding of your online visitors and customers? If you're a site owner or marketer, maybe it's time to evaluate what user-centric approach you're using, and see if they are being used to the full advantage. Identify what types of users and segments you have, and create a measurement plan that can give you a complete 360 degree view.

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This post was authored by Scott Shannon, a Senior Analyst and Google Analytics Certified Seminar Leader and by Melaine Fuentes, a Sr. Consultant of Analytics and Insights, both at Cardinal Path, a premier Digital Intelligence and Optimization firm with offices throughout North America and some of the top minds in the Digital Intelligence community.

"Half the money I spend on advertising is wasted; the trouble is I don't know which half."
John Wanamaker

In the digital age, everything should be measurable, from consumer behavior to the performance of advertisements. This means transparency in advertising spending is approaching unprecedented levels, and the expectation of accountability and proven ROI is coming to the forefront of marketing departments - far different from the days of Mr. Wanamaker, in which it was acceptable for a successful capitalist to waste only half of the advertising budget.

Customer Insights & Data Informed Decision Making in Marketing

The traditional mass media advertising system is being challenged by digital advertising, which has made great strides in recent history. Our ability to measure digital channels is also unfolding, and is now seducing marketers and advertisers who believe in data informed decision making. At the center of this rapidly-changing space is a familiar business constant: the customer.

Another reason why advertisers are pursuing digital strategies is the promise of new customer insights, in real time. What color cars are people building online? Which products are piquing the interest of consumers this week? What is the geographic profile of individuals who add an item to their cart, but fail to convert? What else can we learn about these customers or potential customers? Which advertisement produced the best click through rate, and ecommerce purchase rate?

Our ability to better understand the digital experience of customers requires that we measure multiple touch points with our digital assets and piece together information from a multitude of data sources. The customer experience can include any number of the following: visits to a site, paid search ads and organic search engine results, visits directed from other sites and marketing tactics such as display impressions, email marketing and social networking. It is because of this complex consumer experience that there are many ways to calculate advertising ROI and attribute "conversion credit".

After all, a customer may be exposed to many advertisements, and may learn about your products and services on your site, and on other sites - so how do we determine which parts of the visitor experience actually influenced a given purchase?

Multi-Channel Funnels

Currently, web analytics tools like Google Analytics, CoreMetrics and Site Catalyst use a "last touch" attribution model to assign credit for conversions. What this means is the last point of interaction prior to conversion is credited for generating the online sale, or "conversion".

Since it is possible that a consumer's decision was influenced by multiple touch points prior to conversion, Google Analytics took the step of introducing Multi-Channel Funnels officially on August 24, 2011. This feature can be used to better understand the value of upstream advertising that may be catching the attention of consumers early and influence a subsequent conversion.

Whether you are a small, medium, or large enterprise (or private, public, or NGO), the chances are that you are doing more than one type of promotion – from email campaigns, to paid search (cpc), to social media and display advertising. Even offline channels like TV, radio, billboards, and print can be measured using free services like Google Analytics, by directing visitors to your website – a "drive to site" strategy.

So, which promotional effort is the most effective? How do your promotions interact with each other? Do people first search for you on Google based on a need, or do people hear about your products and services on Facebook or Twitter before visiting your site? Once people hear about your offerings do they buy immediately (e.g. Justin Bieber posters), or do they talk it over with a friend or partner before purchasing (e.g. vacation travel packages). How do they come back to your site a second and third time before making a purchase, or converting against a key goal?

Measurement across different channels (email, social, cpc, display, TV, print) has been notoriously difficult, until recently. Since Multi-Channel Funnels were introduced into the free version of Google Analytics, new power and new insights are in the hands of all marketers, both large and small. So, how easy is it to set up Multi-channel Funnels? Pretty easy, just complete the following five steps.

5 Steps to Get Started with Multi-Channel Funnels in Google Analytics

1. Ensure your promotions are measurable (campaign links are tagged).
2. Ensure Google Analytics is installed on your landing pages (tagging pages).
3. Ensure you have clear calls to action and a clear sense of what you want your visitors to do on your website (what goals do you have when visitors come to your site for a given promotion?)
4. Ensure your conversion goals are configured in Google Analytics (setting up goals in Google Analytics). You may also use Ecommerce conversions as well, but it is still a good idea to create a conversion funnel for the checkout process using Google Analytics Goals.
5. Test the tagged links and landing pages to ensure your clicks show up in Google Analytics under Traffic Sources > Sources > Campaigns, and that your goal conversions are also registering under Conversions > Goals.

Once you have all of the above in place, launch your promotions and wait a few days for data to collect. Next, login to Google Analytics and navigate to Conversions > Multi-Channel Funnels.

Out of the box, Google Analytics will show you which promotions and channels are driving your desired business outcomes. Often this will reveal the "true value" of social media or display advertising, which are called "upper funnel" channels, because they often result in a customer's first exposure to a company, product, or service in the longer path to purchase.

This initial exposure may be achieved by spreading general awareness in social media, or by posting advertising images and videos across the Internet using display advertising, and this may be the beginning of a longer path to purchase, or "Goal Conversion".

The interaction of channels can be visualized with a Venn diagram (screenshot in the right), where the overlap in the circles shows the degree to which visitors had both paid search and organic search, and where the size of each sphere indicates the volume of converting online visitors coming through each digital channel at some time prior to conversion.

Things start to get really interesting when we can see the historical touch points of our customers across our digital channels. The following image shows the pattern of visitor interactions with our digital assets (the "Channel Grouping Path"), and the number of conversions for each combination, as well as the resultant ecommerce dollar value (conversion value may be a combination of ecommerce and dollar values assigned to goals).

With a large volume of display advertising we see that ad impressions are common to many paths to purchase ("display" with an eye symbol is an ad impression, and "display" without an eye symbol is a click on a display ad that resulted in a site visit). After seeing an ad in the last 30 days, many visitors come back to the site using a bookmark or by typing the URL (direct). In addition we see returning traffic using search engines (paid and organic) and other websites that link to ours (referrals).

In order to see the total impact of each digital channel the "Assisted Conversions" report is the best, because it allows us to see what we thought was true prior to the Multi-Channel funnels reports existed and the degree to which we were wrong (screenshot below). The "Last Touch Interaction Conversion Value" column is the "original" last touch attribution model, and the columns labeled "Click Assisted Conversion Value" and "Impression Assisted Conversion Value" each reveal the history of the visitor experience, and the real influence of each channel.

In the example below we see that display advertising would have been seriously undervalued using a last click attribution model - at only 8% of the click assisted dollar value, and at less than 1% of the impression assisted dollar value (impression assisted being the maximum measurable value of display advertising in this case).

Comparing the last interaction and the click assisted conversion values we can quickly see which channels were being undervalued, and also which forms of advertising tend to lead to immediate conversion. In this example, paid search has a higher last interaction value than assisted, which is important to know when determining your advertising channel mix over the course of a large marketing campaign.

Attribution Modeling In Google Analytics

To further understand how various marketing programs are working together Google Analytics provides an Attribution modeling tool that allows for building, customizing and comparing attribution models. Attribution modeling is in many ways the "Holy Grail" in fine-tuning marketing future marketing activities. This feature allows you to rapidly compare attribution models, and ultimately tailor your preferred attribution model to fit your business needs the best. There are four huge benefits to using this feature:

1. No Additional Tagging

Much like Multi-Channel Funnel reporting, attribution modeling does not require additional tagging or implementation, and is currently available with Google Analytics Premium Edition.

2. Easy To Compare

The ability to view up to three different attribution models side-by-side, and use the percent change to measure variance across channels, sources, campaigns and even specific keywords.

3. Customizable

The custom model builder allows credit weighting based on specified rules including site engagement and type of interaction.

Any of the "out of the box" attribution models can be used as a starting point for building a custom attribution model. As explained on the official Attribution help article, the default models are:

• Last Interaction model: attributes 100% of the conversion value to the last channel with which the customer interacted before buying or converting
• First Interaction model: attributes 100% of the conversion value to the first channel with which the customer interacted.
• Linear model: gives equal credit to each channel interaction on the way to conversion.
• Time Decay model: most heavily credits the touch points that occurred nearest to the time of conversion.
• Position Based model: allows you to create a hybrid of the Last Interaction and First Interaction models. Instead of giving all the credit to either the first or last interaction, you can split the credit between them. One common scenario is to assign 40% credit each to the first interaction and last interaction, and assign 20% credit to the interactions in the middle.

The advanced options under "adjust credit for impressions", allows us to weight all display advertising impressions, and apply an even greater weight if the impression occurs X minutes in advance of an online visit that ultimately converts. This means that we decide which ad exposures will be valued more based on what we believe makes the most sense.

4. "Rapid prototyping"

Custom attribution models can be created in minutes and viewed within the interface for comparison against all the other attribution models. "Rapid prototyping" is the most compelling feature of the custom model builder, because it allows us to test and compare various models and learn.

Attribution modeling is the Holy Grail for advertisers, because it allows us to weight different touch points visitors have with your Website according to what makes the most sense for a given business or marketing initiative.

Closing Thoughts

In the digital age everything is measurable, but it is important to "bake-in" measurement when starting to plan an advertising campaign. We simply have more options today than we had in the pre-digital age... and no longer should we accept that half our spending cannot be measured, because it can. Using systems like Google Analytics Multichannel Funnels, and attribution modeling allows us to reclaim control over our ROI, and our ability to effectively use these new measurement tools results in a competitive advantage, and greater efficiencies in marketing spending.

Learn more about Attribution models in this Tutorial Video

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When it comes to analytics, I am a big fan of pan-session analysis. Pan-session analysis provides insights across multiple visits by the same person. For almost every website it is an incredibly, powerful way to understand your visitors better.

By applying the insights you derive from this analysis, you can further optimize your visitor's experience and conversion rate.

In the following paragraphs I will describe five different pan-session analysis techniques that deliver great insights.

1. Frequency and recency analysis
2. Time and visits to purchase analysis
3. Pan-session funnel analysis
4. Multichannel analysis
5. Customer analysis

Let's start with frequency and recency reporting.

1. Frequency And Recency Analysis

Frequency and recency metrics and distributions show you how loyal your audience actually is. Do your visitors only come once and never return? Or do you have a great deal of visitors who come to your website even more than three times a week?

Let's take a closer look at two reports. First, the 'count of visits' report, which shows the frequency of visitors (direct link to report):

Almost 80% of the visitors to this website visit the website just once and don't come again. That doesn't look very good. Let's dive into the recency metric, in the 'days since last visit' report (go to this report and click on 'days since last visit' tab).

The recency graph on itself doesn't look bad. Almost 90% of the visitors visited the website within the last day. But, we have to subtract the new visitors to get a good overview of the returning visitors percentage and how often they visit the site.

With the combined overview of recency and frequency metrics I would conclude that this website really needs to invest in building a stable base of loyal subscribers / visitors.

Posting new content (a blog?) and offers on a more regular base would definitely help to keep the visitors engaged with this website.

2. Visits And Time to Purchase Analysis

In order to find out more about the product buying decision cycle, the visits and time to purchase reports deliver very useful information. You can find these reports in the E-commerce module of Google Analytics (if you have E-commerce implemented, here is a direct link to report). An example of a "Visits to Purchase" report is shown below:

In this case roughly 40% of the visitors convert within the first visit and 70% needs one to four visits to come to a buying decision. Wow, 20% of the conversions on this website take place after seven or more visits. What's happening here?

A lot of websites contain landing pages that are too much focused on making a direct sale. It is extremely important to segment your visitors and apply different tactics to make them convert. The "time to purchase" data shows a similar distribution as we saw earlier:

Now it's time to segment your data further and find out which visitors convert the highest (visitors from a geographical region, certain campaign traffic, etc.) and which visitors don't. You can use different strategies for each visitor segment.

3. Funnels: Pan-Session Analysis

A conversion funnel is a graphical report representing a series of steps that a user must take in order to convert. The conversion could be anything from purchase to sign-up, subscribe to newsletter, watch a demo etc.

Google Analytics and many other web analytics tools do have funnel capabilities, but they lack certain functionality:

• The possibility to measure across different sessions
• The possibility to segment the funnel data

This is where a tool called Paditrack can help. It's free and delivers great insights in how your visitors are flowing through your funnels in multiple sessions.

4. Multichannel Analysis

Google Analytics provides you with an amazing opportunity to capture and analyze pan-session behavior using their multichannel reporting interface. Very often your website visitors won't convert in their first visit.

In the report below, the 'top conversion paths' (direct link to report) you can see the distribution of the top 10 conversion paths of an E-commerce website:

In this particular case website visitors tend to come back very often before they decide to purchase. It is a challenge to try to optimize your conversion with the help of multichannels, but it is more than worth it to put in the effort!

Think about redistributing your online budget based on how your different marketing channels perform in the customer journey. Here are some analysis ideas based on this feature.

5. Customer Analysis

With Google Analytics custom variables, you can define extra segments to apply to your visitors. In one word you could name it "labeling". Custom variables can be set on three different levels:

• Page level
• Visit level
• Visitor level

In order to do pan-session analysis we need to set the custom variable at the visitor level. Here you can find technical instructions on how to implement custom variables.

By setting a custom variable on the visitor level, we can follow our website visitors through multiple sessions.

So let's assume I run an E-commerce site and I am interested in the behavior of customers. In this case I define a customer as someone who made an earlier purchase on my website.

I can analyze the the behavior of this segment by simply setting a custom variable on the thank you page after an order is placed. By doing this I can analyze:

• The behavior of website visitors who placed an order in the past
• The behavior of website visitors who didn't place an order before

You can set custom variables on visitors who sign up through a lead generation form, log in as a member, donate money to your charity etc. Please remember that if you set the custom variable per visitor, you cannot use Personal Identifiable Information, it is against Google Analytics ToS.

Last Remarks

The deletion of cookies and people accessing your site through multiple devices and browsers (tabs) will affect your pan-session analysis. If you are interested in learning more about measuring across multiple devices and browsers, you should read: Google Universal Analytics: A User-Centric Approach.

Paul Koks is the owner and editor of OnlineMetrics, a blog about Analytics and Conversion Optimization. You can follow him on Twitter @OnlineMetrics or connect on LinkedIn.

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"Where I think Google Analytics really shines as a product is the -what we call- custom advanced segments or just custom segments."

In a previous presentation, Nick Iyengar discussed data segmentation, providing a definition of segmentation and discussing its value for web analytics. He also went over a few reports that provide segmentation options by default on Google Analytics. In this presentation, he provides an in-depth overview of Advanced Segments, a segmentation tool on Google Analytics.

There's really no list of Top 10 types of segments, because different segments work for different businesses. Iyengar shows how to build custom segments around any attribute or dimension of your traffic. Below are a few examples provided during the presentation.

Segment Visitors by Number of Keywords Searched

Nick starts by describing what is a Regular Expression and then he gives a sample that can be used in order to create a segment of 2-3-4 or more keywords: ^(W*\w+\b){3}\W*$ If you change the number '3,' it will change the number of words in the keyphrase that is supplied by Google Analytics.

According to him, if you start looking at phrases that have multiple words – long tail keywords - and plot them out year-over-year, you can get an idea of what's successful. Shorter keyword phases get the most traffic – that's the head. Longer phrases, however, convert better for meeting goals.

#1 Money-Saving Advanced Segment.

How do you know if you're paying the right amount for specific keywords when advertising on Adwords? Start by including visits from ad position 1. Then compare them to ad slot position 2, 3, 4, and 5. You'll be able to see cost and conversion, depending upon AdWords position. The first position almost always gets the most clicks, but the conversion rate may be better in later positions. Then you can drill down and find which keywords convert better in each position.

Branded vs. Non-Branded Keywords Segment

Show me only branded keywords either by including or excluding brand terms. You can see how brand terms perform.

Traffic Sources Bringing High-Value Visitors

This will look for visitors that looked at 2 pages or more or spent greater than 200 seconds on the site. These are highly engaged people. If you look to see where highly engaged traffic comes from, you can find out where your efforts need to be.

Nick Iyengar - Senior Consultant, Cardinal Path

Nick Iyengar is a senior consultant at Cardinal Path, as well as a veteran instructor and regular speaker at analytics industry events. As a consultant, Nick has worked with organizations ranging from Fortune 500s to non-profits to design and implement customized web analytics solutions, launch and analyze website experiments, and leverage data to optimize online marketing performance.

Nick has provided in-depth guidance to hundreds of North American businesses and organizations as an instructor for Google AdWords, Google Analytics, and Google Website Optimizer seminars, and as a speaker at Google's "Get Your Business Online" series and analytics industry events like GAUGE.

He began his career in the online marketing industry in the mid-2000's, when he joined Google's AdWords team in the Mountain View, CA headquarters. There, he spent more than three years managing tens of millions of dollars in ad spend for top-tier Tech B2C clients. He also mananged Google’s “Analytics Guru” team, applying Google Analytics data to improve campaign performance for top AdWords advertisers. Nick holds a B.A. in Government from Connecticut College.

Segmentation is the mother of all website optimization. That's an extreme statement, but I believe anyone in the field would think twice before disagreeing with it. Segmentation enables us to understand who are our customers and how different groups of people behave on the website; this information is extremely valuable in order to provide a a richer and more relevant experience to customers. The result is a happier customer, a more profitable website, and more bonuses to Web Analysts!

That's why Avinash Kaushik is so vehement when he writes: The Choice Is Stark: Segment Or Die! According to him: "if you simply sit and ponder for a few minutes it will be clear that your website exists to do many different jobs and people come to your website to accomplish many different goals. So why analyze your data as one big ugly glob?"

Google Analytics provides several out of the box ways to segment your visitors into groups. For example, it is possible to segment by GEO location, Browser, Operating System, Traffic Source, Visitor Type (New vs. Returning), Content Viewed, Goal Completion, and the list goes on...

But beyond a shadow of a doubt, the most powerful segmentation feature on Google Analytics is the Custom Variable. I like to play with the idea that Custom Variables are so important that they should have their own tab in the Google Analytics sidebar navigation, and that this tab should be significantly prominent. Here is a mockup for the Google Analytics team, feel free to use!

Below I discuss a proper definition of Custom Variables, where it should be used, how to plan its implementation, and some additional tips and tricks.

Custom Variables Definition

According to the Google Analytics Code site, Custom Variables can be defined as:

Custom variables are name-value pair tags that you can insert in your tracking code in order to refine Google Analytics tracking. With custom variables, you can define additional segments to apply to your visitors other than the ones already provided by Analytics.

The important part of this definition is that you, as the website owner, can define the segment that you want to apply to specific visitors. The missing part of this definition is that you can apply it to your visitors only after they actively do something on the site. For example, you can define someone as a male or female only after he or she fills in that information on the site. That may seem obvious, but it is important.

Custom Variables Model & Hierarchy

The power of Custom Variables is partly due to its hierarchy, the possibility to define whether the segment you are creating will last only for the current page visited by a visitor, the current visit of a visitor, or "to infinity ... and beyond!" (i.e. for as long as visitors keep their cookies).

I will go over some examples below, but before it is important to understand how Custom Variables are defined, their grammar. First, as mentioned above, we can use different levels, meaning that the variable will be saved for different periods of time:

• Visitor Level— this will modify the visitors' cookies for as long as they use the same browser and device and do not erase cookies. As we will see below, we use this level to keep unique information about a visitor, such as subscriptions, purchases, and demographic characteristics provided by visitors in a form. A way to overcome the limitation of multiple browsers and devices would be to update the visitor's cookies every time he or she logs in to the website.
• Session Level— this modifies the visitors' cookies for the period of time during which the visitor is active on the site. Very useful to track internal campaigns, such as banners on the homepage.
• Page Level— this modifies the visitors' cookies for activity on a specific pageview or event. Usually it will be used to group behavior in different sections of a website.

But before you start jumping out of joy, please note that if you are using the free version of Google Analytics you have only 5 slots. That means you have to think carefully before you implement them. If you are a lucky person, and you have Google Analytics Premium, you will have 50 slots available, which is a lot!

As I mentioned above, this is not an out of the box feature, so you will need to implement code in order to collect this data. Below is the code and the explanation for each parameter:

_setCustomVar(index, name, value, opt_scope)

index (required) — The slot for the custom variable, it can range from 1 - 5 for standard accounts or 1-50 for Premium accounts.

name (required) — The name for the custom variable, a string that identifies the custom variable and appears in the top-level Custom Variables report of the Analytics reports. For example, if you are using a slot to track gender, the name would be 'Gender'.

value (required) — The value for the custom variable, it appears in the table list of the UI for a selected variable name. Typically, you will have two or more values for a given name. Using the 'Gender' example above, the values would be either 'Male' or 'Female'.

opt_scope (optional) — The scope for the custom variable. As described above, the scope defines the level of user engagement with the site. It is a number whose possible values are 1 (visitor-level), 2 (session-level), or 3 (page-level). When left undefined, the custom variable scope defaults to page-level interaction.

Where Should a Custom Variable Be Used?

As mentioned above, standard Google Analytics users have only 5 slots available. So it is important to plan Custom Variables implementation effectively. The most important thing is to be careful when overwriting slots.

For example, if you use slot 1 to track visitors who register to the website, do not use it to track visitors' gender as well, one will overwrite the other. However, you might consider using slot 1 to track Purchase, as if you know someone purchased something, you don't need to know he ius registered anymore, so overwriting it is OK. For visit and page level variables it is easier plan, but note that if you trigger two different variables in one visit, the last one will get the full credit for all the visit.

Below I provide examples of usage for each Custom Variable level.

Visitor Level Custom Variable

Let's suppose you have the following registration form on your website (screenshot below). Your visitors have to enter all that information in order to register, which means you can use any of it in your custom variables, right? Wrong! The first rule you need to know is that tracking Personal Identifiable Information is against Google Analytics Terms of Service (see section 7). Therefore, you should not add emails and names to your custom variables.

Using the example above, you would probably want to track the following using a Visitor Level Custom Variable:

1. Account Type: tracking whether a particular visitor is a 'musician' or 'personal' would provide important insight on how differently these groups behave on the website. Apparently this is an important piece of information for the business since it is high up in the form.
2. Birthday: tracking the birthday year would segment visitors by age, which could be valuable when providing targeted content to visitors.
3. Gender: important to understand which content is valuable to which gender. Men and women behave very differently on the web (here is research proving it).

Visit Level Custom Variable

Visit Level Custom Variables are extremely valuable when tracking internal campaigns. Let's suppose you have an Ecommerce website and you are advertising different products on your homepage. If you trigger a Custom Variable (using an onclick event) for each of the product in your homepage you will be able to understand which products are driving more sales from the homepage. This will be extremely valuable when defining how to organize your content in a data-driven way.

Please note that while this might sound like Science fiction to some people, the implementation should be very simple (considering standard conditions for temperature and pressure). Here is a similar code of what you would add to you HTML link tag: onclick="_gaq.push(['_setCustomVar',1,'Home Campaign','Galaxy Nexus',2]);"

Page Level Custom Variable

Page Level Custom Variables should be used in order to group actions performed in different sections of the website. Here are a few examples:

1. Aggregating authors: for content websites with multiple authors, the Custom Variable can be used to group posts by author in order to understand which authors drive engagement.
2. Aggregating categories םר subjects of interest: for all types of websites, this would be useful to learn which brands or category of content (such as tags or product type) is driving engagement.

Custom Variables for Ecommerce

When it comes to using Custom Variables for Ecommerce websites, Justin Cutroni provides a very useful list of ideas (implementation and analysis details on this link):

1. Tracking Coupon Codes and Promotional Codes
2. Tracking Payment Method
3. Tracking Shipping Method
4. Tracking Repeat Customers
5. Purchase History

Advanced Tips & Tricks

Here are four advanced issues you should keep in the back of your mind:

• Call the _setCustomVar() function BEFORE a pageview or event, otherwise it won't be sent to Google Analytics.
• Do not use duplicate key names across slots, this will mess with the data.
• You can trigger up to 5 simultaneous custom variables in a request (e.g. pageview or event ).
• The name-value combination must be less than 128 characters.

Concluding Thoughts

As mentioned above, Custom Variables are the most powerful segmentation feature on Google Analytics, it enables marketers to understand their visitors better. On The Choice Is Stark: Segment Or Die! Avinash starts by saying:

There is a reason your analytics data does not make any sense. There is a reason you are unable to find an iota of insight that you can action. There is a reason you feel data is your enemy. You see, you are not following one of the holiest of holy covenants when it comes to data analysis: Segmentation.

If you are among this group, get started now!

Related Articles

1. Audience Segmentation - Giving Clicks a Personality
2. 3 Segmentation Strategies: Intent, GEO & Conversions
3. Understand Google Analytics Advanced Segments [video]
4. Data Segmentation with Google Analytics [video]
5. Successful Website Personalization

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"It's a flexible structure within Google Analytics that allows you to have a set of data that's most meaningful to you, and your colleague to have a set of data that's most meaningful to her."

How does Google Analytics work?

The basic tenet is that you go to a website and request a web page => A JavaScript code is executed and a call is send to the Google server => Available information about the user is saved on Google Analytics (such as which browser, GEO location, referrer, time stamp and other).

A Google Analytics account can be for one person or for a team. Profiles can be set up by websites or by functional area, so that you can see sales on all sites rather than having to analyze each site's sales.

Security tip: Be sure to be careful about generic email addresses (like company @ gmail.com) where you don't know for sure who has access to the email. It's important to keep control of email addresses, so use private email addresses and always check if those people still "deserve" access.

A profile can be by site, by functional areas, or both. Page tags go on the page to make the data available. Building proper profiles is a very important part of using the Google Analytics account. Below is an image by Google on the structure of accounts, read more at this code article.

Below are two very important tips on creating profiles for an account:

1. Always have a RAW profile with no filters, and all segmentation can be done later.
2. Create at least one profile which filters out all internal traffic, to see the real customer traffic.

Alex and Charles go on to show how to analyze traffic sources and how to navigate the Google Analytics interface, both in terms of graphing and of settings. Check the video to learn more.

Alex Langshur - Co-Founder & Senior Partner, Cardinal Path

Alex Langshur is a co-founder and Senior Partner of Cardinal Path, a web analytics, search optimization and marketing consulting firm. Cardinal Path is the largest pure-play digital performance measurement firm in North America, committed to helping clients leverage their web, mobile, social media, email and search initiatives to turn convert visits to value, and prospects into customers.

Alex is a sought after speaker having keynoted and presented at digital marketing conferences around the world. He is the past-President and a current Director Emeritus of the Web Analytics Association (WAA), and a member of the Emetrics Marketing Optimization Summit Advisory Council. He teaches courses on web analytics, search optimization and digital measurement for University of British Columbia, GSA Web Manager University and WAA Base Camp, and has been published in journals and magazines including Government IT Week, Government Executive, CNN Money and others. Alex lives in Boston with his wife, two sons and golden retriever.

Charles Davis - Solutions Director, Blast Advanced Media

Charles is an entrepreneur at heart and Solutions Director for Blast Advanced Media. He works directly with clients to understand the most important details of their business and how we can best meet their demanding needs. With a background in enterprise level software systems, an understanding of the software lifecycle, and having started and operated a successful small business, Charles connects the business needs of each client with the skills of our diverse team to provide the maximum effect on the revenue and profitability of each client. Charles’ areas of special interest are project design, project management, website optimization, and developing strategic partnerships with great companies.

Editor Note: this article discusses Universal Analytics, the new generation of Google Analytics, which is currently in closed beta. You can request access to the beta or read more on the official help articles. Also note that the screenshots are from the public existing reports, not from a new set of reports, they were added to help exemplify the concepts explained.

So, you are amazed at how Amazon personalizes their customer experience. And you have read article after article on how effective 1-to-1 marketing can be. Yet, when you review your own marketing initiatives, there is suddenly that realization that this personalization element is totally missing. When assessing your current digital marketing initiatives, you may find the following.

You are doing well at

• Your paid search campaigns, you are targeting searchers at various stages of the buying cycle with relevant ad copy and landing pages.
• Your e-mail lists are well segmented into leads and customers.
• Your media buys are very targeted to sites with visitors with affinity for your products.
• You have your analytics in order and your reporting in place for your campaign performance, site engagement and ecommerce.

You are not doing so well at

• You have little or no visibility into the behavior of repeat members and buyers.
• You have no idea how your customers interact across your different platforms (website, mobile, in-store, loyalty cards, etc.)
• The user-centric view of your customers (that you fantasized about after finishing your MBA) is a bit harder to get than you thought.

Google Analytics - User Centric Analytics

Whether you are doing great in some areas and poorly in others, there is always room for improvement. And having the ability to understand how users engage with your content across various devices, platforms, etc. is important. Focusing on creating a user-centric environment will vastly improve your 1-to-1 marketing with customers.

According to Google, their analytics platform "...provides tools that allows you to measure your effectiveness in advertising, sales, product development and retention across all your touchpoints." With the help of Google Analytics, reaching that user-centric environment isn't that hard to do!

In this post, I hope to shed some light on applying the user-centric concept to your online retail business. Note: this post is not meant to be a technical guide. For the technical implementation details, please refer to this article. I'll examine the four following scenarios:

1. One Visitor - Multiple Browsers
2. One Visitor – Multiple Devices
3. Visitors Buying Cycle Stages
4. Online and Offline Purchases

Let's get started!

1. One Visitor - Multiple Browsers

I don't know about you, but when I'm at work, not only do I have many tabs open on my default Chrome browser, I have at least two other browsers running at any given time. Then, when I get home, I grab a laptop to browse and shop, while watching TV. Now, to web analytics, all of these visits from the various browsers and computers look like different users, when in reality, it's just yours truly. It turns out I'm not the only one with this "multiple" browsing behavior. It's actually quite common these days. So how do retailers know if it's me, or multiple users? Let's examine this closely on the online retailer end of things.

Look at the snapshot below. This is a Google Analytics report for ecommerce, and is segmented by browser. You can see what appears to have three transactions that come from three different visitors (browsers). However, when you look at the first column with the unique customer ID, you'll notice the three transactions come from the same visitor.

For the site shown above, registration is required before you browse products and get exclusive offers. (And once the site owner has your email, you can rest assured that you'll receive "amazing deals" in your inbox going forward :-). This retailer has done his homework and knows how to create a unique ID for each visitor, and stores a Custom Variable which enabled us to stitch together the visits and get this new "user-centric" perspective. Now going forward, Universal Analytics Measurement Protocol will be our best friend!

What other insights can be derived? One of the KPIs for this retailer is the time elapsed between a customer registration and the first purchase. As you can see here, this visitor registered on September 19th, and they had 27 visits and then two transactions.

For the visitor below, this graph shows that it took 12 visits before the first transaction (and then just three visits before the second transaction).

With this type of visitor-level data that is now available, you can easily map the registration dates to the transaction dates and you'll have full visibility into the buying cycle. Now, you have the ability to use this information to your advantage and find creative marketing messages and offers that may help shorten the buying cycle. Plus, you can reach out to those who are not buying (and run all sorts of Lifetime Value analyses, cohort, RFM and others).

2. One Visitor – Multiple Devices

Another powerful use case for user-centric analysis is the ability to stitch together data across different devices, including mobile devices. One of the challenges for the site owner mentioned above is measuring campaign and site performance for users who are accessing the sites from mobile device, tablets and desktops.

Let's say you have visited their "Exclusive Offers" site on your desktop, you registered and now have an account. But, you decide not to buy now and want to wait for an "End of Year" special. Later, you are presented with a mobile ad with that special you've been waiting for. So you click on the ad.

Unfortunately, the site owner again requires you to sign up or sign in. Since you already have an account, you simply sign in. Without applying the user-centric concept discussed previously, this visitor will appear as a different user. You wouldn't be able to tell if the same person registered on one device, and then bought on another. Nor will you be able to report on their total purchase across devices.

Examine the screenshot below. Here, you see how the user-centric concept is being put to work. The user visited on mobile, and then visited on non-mobile as well. Again, this is super insightful, since you see the full buying cycle and activities across devices.

3. Visitors Buying Cycle Stages

Following the same ecommerce model (sites with exclusive offers that require logins and assigns user IDs to customers), you may want to consider taking it a step further and create your own user segmentation "flow". Start off with members (those who registered), then returning members and then buyers.

Look at the site references below. Instead of just reporting on some aggregate conversion rate (that looks ok but is really useless), the site owner can create this amazing view/funnel of the user segments and the associated conversion rates of moving from one customer segment to another.

Site visitors who become members (and now you know what drives more membership).

Then build a report on returning members and see what keeps them coming.

And last but not least, you can measure the true conversion rate of members to buyers.

4. Online and Offline Purchases

Another use case for Universal Analytics and user-centric analytics is integrating offline purchases with your online Web Analytics data. Depending on your environment, you might want to do the integration in your own database or data warehouse, but that's for another discussion. In this use case, we want to show how to bring this data into Google Analytics.

Let's take, for example, a retailer that accepts orders over the phone, in addition to online sales. During this process, the sales rep logs in the client info, orders the product(s), takes the credit card and then processes the transaction.

Here is how we now can tie it all together: when the client calls and is authenticated, a hit is registered and is associated with the client's unique ID in analytics. When the sales rep completes the transaction, the associated ecommerce transaction data is registered in analytics as well.

Since we assigned a unique ID for each transaction, we can later report on the visitor's purchases whether they completed the purchase online or offline. Again, the 360 view is completed.

This approach can be extended to include point-of-sale transactions in-store or from mobile apps and data loyalty programs. Each can be integrated in the mix.

Closing Thoughts

As you can see, user-centric analytics can be extremely helpful. It can tell you a lot about your visitors. But, it can go beyond that to tell you more in-depth information about their visits and their habits. It essentially gives you an overview of their behavior, which in turn, provides you with a better understanding of your customers.

Do you feel you have a solid understanding of your online visitors and customers? If you're a site owner or marketer, maybe it's time to evaluate what user-centric approach you're using, and see if they are being used to the full advantage. Identify what types of users and segments you have, and create a measurement plan that can give you a complete 360 degree view.

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This post was authored by Scott Shannon, a Senior Analyst and Google Analytics Certified Seminar Leader and by Melaine Fuentes, a Sr. Consultant of Analytics and Insights, both at Cardinal Path, a premier Digital Intelligence and Optimization firm with offices throughout North America and some of the top minds in the Digital Intelligence community.

"Half the money I spend on advertising is wasted; the trouble is I don't know which half."
John Wanamaker

In the digital age, everything should be measurable, from consumer behavior to the performance of advertisements. This means transparency in advertising spending is approaching unprecedented levels, and the expectation of accountability and proven ROI is coming to the forefront of marketing departments - far different from the days of Mr. Wanamaker, in which it was acceptable for a successful capitalist to waste only half of the advertising budget.

Customer Insights & Data Informed Decision Making in Marketing

The traditional mass media advertising system is being challenged by digital advertising, which has made great strides in recent history. Our ability to measure digital channels is also unfolding, and is now seducing marketers and advertisers who believe in data informed decision making. At the center of this rapidly-changing space is a familiar business constant: the customer.

Another reason why advertisers are pursuing digital strategies is the promise of new customer insights, in real time. What color cars are people building online? Which products are piquing the interest of consumers this week? What is the geographic profile of individuals who add an item to their cart, but fail to convert? What else can we learn about these customers or potential customers? Which advertisement produced the best click through rate, and ecommerce purchase rate?

Our ability to better understand the digital experience of customers requires that we measure multiple touch points with our digital assets and piece together information from a multitude of data sources. The customer experience can include any number of the following: visits to a site, paid search ads and organic search engine results, visits directed from other sites and marketing tactics such as display impressions, email marketing and social networking. It is because of this complex consumer experience that there are many ways to calculate advertising ROI and attribute "conversion credit".

After all, a customer may be exposed to many advertisements, and may learn about your products and services on your site, and on other sites - so how do we determine which parts of the visitor experience actually influenced a given purchase?

Multi-Channel Funnels

Currently, web analytics tools like Google Analytics, CoreMetrics and Site Catalyst use a "last touch" attribution model to assign credit for conversions. What this means is the last point of interaction prior to conversion is credited for generating the online sale, or "conversion".

Since it is possible that a consumer's decision was influenced by multiple touch points prior to conversion, Google Analytics took the step of introducing Multi-Channel Funnels officially on August 24, 2011. This feature can be used to better understand the value of upstream advertising that may be catching the attention of consumers early and influence a subsequent conversion.

Whether you are a small, medium, or large enterprise (or private, public, or NGO), the chances are that you are doing more than one type of promotion – from email campaigns, to paid search (cpc), to social media and display advertising. Even offline channels like TV, radio, billboards, and print can be measured using free services like Google Analytics, by directing visitors to your website – a "drive to site" strategy.

So, which promotional effort is the most effective? How do your promotions interact with each other? Do people first search for you on Google based on a need, or do people hear about your products and services on Facebook or Twitter before visiting your site? Once people hear about your offerings do they buy immediately (e.g. Justin Bieber posters), or do they talk it over with a friend or partner before purchasing (e.g. vacation travel packages). How do they come back to your site a second and third time before making a purchase, or converting against a key goal?

Measurement across different channels (email, social, cpc, display, TV, print) has been notoriously difficult, until recently. Since Multi-Channel Funnels were introduced into the free version of Google Analytics, new power and new insights are in the hands of all marketers, both large and small. So, how easy is it to set up Multi-channel Funnels? Pretty easy, just complete the following five steps.

5 Steps to Get Started with Multi-Channel Funnels in Google Analytics

1. Ensure your promotions are measurable (campaign links are tagged).
2. Ensure Google Analytics is installed on your landing pages (tagging pages).
3. Ensure you have clear calls to action and a clear sense of what you want your visitors to do on your website (what goals do you have when visitors come to your site for a given promotion?)
4. Ensure your conversion goals are configured in Google Analytics (setting up goals in Google Analytics). You may also use Ecommerce conversions as well, but it is still a good idea to create a conversion funnel for the checkout process using Google Analytics Goals.
5. Test the tagged links and landing pages to ensure your clicks show up in Google Analytics under Traffic Sources > Sources > Campaigns, and that your goal conversions are also registering under Conversions > Goals.

Once you have all of the above in place, launch your promotions and wait a few days for data to collect. Next, login to Google Analytics and navigate to Conversions > Multi-Channel Funnels.

Out of the box, Google Analytics will show you which promotions and channels are driving your desired business outcomes. Often this will reveal the "true value" of social media or display advertising, which are called "upper funnel" channels, because they often result in a customer's first exposure to a company, product, or service in the longer path to purchase.

This initial exposure may be achieved by spreading general awareness in social media, or by posting advertising images and videos across the Internet using display advertising, and this may be the beginning of a longer path to purchase, or "Goal Conversion".

The interaction of channels can be visualized with a Venn diagram (screenshot in the right), where the overlap in the circles shows the degree to which visitors had both paid search and organic search, and where the size of each sphere indicates the volume of converting online visitors coming through each digital channel at some time prior to conversion.

Things start to get really interesting when we can see the historical touch points of our customers across our digital channels. The following image shows the pattern of visitor interactions with our digital assets (the "Channel Grouping Path"), and the number of conversions for each combination, as well as the resultant ecommerce dollar value (conversion value may be a combination of ecommerce and dollar values assigned to goals).

With a large volume of display advertising we see that ad impressions are common to many paths to purchase ("display" with an eye symbol is an ad impression, and "display" without an eye symbol is a click on a display ad that resulted in a site visit). After seeing an ad in the last 30 days, many visitors come back to the site using a bookmark or by typing the URL (direct). In addition we see returning traffic using search engines (paid and organic) and other websites that link to ours (referrals).

In order to see the total impact of each digital channel the "Assisted Conversions" report is the best, because it allows us to see what we thought was true prior to the Multi-Channel funnels reports existed and the degree to which we were wrong (screenshot below). The "Last Touch Interaction Conversion Value" column is the "original" last touch attribution model, and the columns labeled "Click Assisted Conversion Value" and "Impression Assisted Conversion Value" each reveal the history of the visitor experience, and the real influence of each channel.

In the example below we see that display advertising would have been seriously undervalued using a last click attribution model - at only 8% of the click assisted dollar value, and at less than 1% of the impression assisted dollar value (impression assisted being the maximum measurable value of display advertising in this case).

Comparing the last interaction and the click assisted conversion values we can quickly see which channels were being undervalued, and also which forms of advertising tend to lead to immediate conversion. In this example, paid search has a higher last interaction value than assisted, which is important to know when determining your advertising channel mix over the course of a large marketing campaign.

Attribution Modeling In Google Analytics

To further understand how various marketing programs are working together Google Analytics provides an Attribution modeling tool that allows for building, customizing and comparing attribution models. Attribution modeling is in many ways the "Holy Grail" in fine-tuning marketing future marketing activities. This feature allows you to rapidly compare attribution models, and ultimately tailor your preferred attribution model to fit your business needs the best. There are four huge benefits to using this feature:

1. No Additional Tagging

Much like Multi-Channel Funnel reporting, attribution modeling does not require additional tagging or implementation, and is currently available with Google Analytics Premium Edition (Editor Note: This feature is now in public beta for all Google Analytics accounts, including the standard version).

2. Easy To Compare

The ability to view up to three different attribution models side-by-side, and use the percent change to measure variance across channels, sources, campaigns and even specific keywords.

3. Customizable

The custom model builder allows credit weighting based on specified rules including site engagement and type of interaction.

Any of the "out of the box" attribution models can be used as a starting point for building a custom attribution model. As explained on the official Attribution help article, the default models are:

• Last Interaction model: attributes 100% of the conversion value to the last channel with which the customer interacted before buying or converting
• First Interaction model: attributes 100% of the conversion value to the first channel with which the customer interacted.
• Linear model: gives equal credit to each channel interaction on the way to conversion.
• Time Decay model: most heavily credits the touch points that occurred nearest to the time of conversion.
• Position Based model: allows you to create a hybrid of the Last Interaction and First Interaction models. Instead of giving all the credit to either the first or last interaction, you can split the credit between them. One common scenario is to assign 40% credit each to the first interaction and last interaction, and assign 20% credit to the interactions in the middle.

The advanced options under "adjust credit for impressions", allows us to weight all display advertising impressions, and apply an even greater weight if the impression occurs X minutes in advance of an online visit that ultimately converts. This means that we decide which ad exposures will be valued more based on what we believe makes the most sense.

4. "Rapid prototyping"

Custom attribution models can be created in minutes and viewed within the interface for comparison against all the other attribution models. "Rapid prototyping" is the most compelling feature of the custom model builder, because it allows us to test and compare various models and learn.

Attribution modeling is the Holy Grail for advertisers, because it allows us to weight different touch points visitors have with your Website according to what makes the most sense for a given business or marketing initiative.

Closing Thoughts

In the digital age everything is measurable, but it is important to "bake-in" measurement when starting to plan an advertising campaign. We simply have more options today than we had in the pre-digital age... and no longer should we accept that half our spending cannot be measured, because it can. Using systems like Google Analytics Multichannel Funnels, and attribution modeling allows us to reclaim control over our ROI, and our ability to effectively use these new measurement tools results in a competitive advantage, and greater efficiencies in marketing spending.

Learn more about Attribution models in this Tutorial Video

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When it comes to analytics, I am a big fan of pan-session analysis. Pan-session analysis provides insights across multiple visits by the same person. For almost every website it is an incredibly, powerful way to understand your visitors better.

By applying the insights you derive from this analysis, you can further optimize your visitor's experience and conversion rate.

In the following paragraphs I will describe five different pan-session analysis techniques that deliver great insights.

1. Frequency and recency analysis
2. Time and visits to purchase analysis
3. Pan-session funnel analysis
4. Multichannel analysis
5. Customer analysis

Let's start with frequency and recency reporting.

1. Frequency And Recency Analysis

Frequency and recency metrics and distributions show you how loyal your audience actually is. Do your visitors only come once and never return? Or do you have a great deal of visitors who come to your website even more than three times a week?

Let's take a closer look at two reports. First, the 'count of visits' report, which shows the frequency of visitors (direct link to report):

Almost 80% of the visitors to this website visit the website just once and don't come again. That doesn't look very good. Let's dive into the recency metric, in the 'days since last visit' report (go to this report and click on 'days since last visit' tab).

The recency graph on itself doesn't look bad. Almost 90% of the visitors visited the website within the last day. But, we have to subtract the new visitors to get a good overview of the returning visitors percentage and how often they visit the site.

With the combined overview of recency and frequency metrics I would conclude that this website really needs to invest in building a stable base of loyal subscribers / visitors.

Posting new content (a blog?) and offers on a more regular base would definitely help to keep the visitors engaged with this website.

2. Visits And Time to Purchase Analysis

In order to find out more about the product buying decision cycle, the visits and time to purchase reports deliver very useful information. You can find these reports in the E-commerce module of Google Analytics (if you have E-commerce implemented, here is a direct link to report). An example of a "Visits to Purchase" report is shown below:

In this case roughly 40% of the visitors convert within the first visit and 70% needs one to four visits to come to a buying decision. Wow, 20% of the conversions on this website take place after seven or more visits. What's happening here?

A lot of websites contain landing pages that are too much focused on making a direct sale. It is extremely important to segment your visitors and apply different tactics to make them convert. The "time to purchase" data shows a similar distribution as we saw earlier:

Now it's time to segment your data further and find out which visitors convert the highest (visitors from a geographical region, certain campaign traffic, etc.) and which visitors don't. You can use different strategies for each visitor segment.

3. Funnels: Pan-Session Analysis

A conversion funnel is a graphical report representing a series of steps that a user must take in order to convert. The conversion could be anything from purchase to sign-up, subscribe to newsletter, watch a demo etc.

Google Analytics and many other web analytics tools do have funnel capabilities, but they lack certain functionality:

• The possibility to measure across different sessions
• The possibility to segment the funnel data

This is where a tool called Paditrack can help. It's free and delivers great insights in how your visitors are flowing through your funnels in multiple sessions.

4. Multichannel Analysis

Google Analytics provides you with an amazing opportunity to capture and analyze pan-session behavior using their multichannel reporting interface. Very often your website visitors won't convert in their first visit.

In the report below, the 'top conversion paths' (direct link to report) you can see the distribution of the top 10 conversion paths of an E-commerce website:

In this particular case website visitors tend to come back very often before they decide to purchase. It is a challenge to try to optimize your conversion with the help of multichannels, but it is more than worth it to put in the effort!

Think about redistributing your online budget based on how your different marketing channels perform in the customer journey. Here are some analysis ideas based on this feature.

5. Customer Analysis

With Google Analytics custom variables, you can define extra segments to apply to your visitors. In one word you could name it "labeling". Custom variables can be set on three different levels:

• Page level
• Visit level
• Visitor level

In order to do pan-session analysis we need to set the custom variable at the visitor level. Here you can find technical instructions on how to implement custom variables.

By setting a custom variable on the visitor level, we can follow our website visitors through multiple sessions.

So let's assume I run an E-commerce site and I am interested in the behavior of customers. In this case I define a customer as someone who made an earlier purchase on my website.

I can analyze the the behavior of this segment by simply setting a custom variable on the thank you page after an order is placed. By doing this I can analyze:

• The behavior of website visitors who placed an order in the past
• The behavior of website visitors who didn't place an order before

You can set custom variables on visitors who sign up through a lead generation form, log in as a member, donate money to your charity etc. Please remember that if you set the custom variable per visitor, you cannot use Personal Identifiable Information, it is against Google Analytics ToS.

Last Remarks

The deletion of cookies and people accessing your site through multiple devices and browsers (tabs) will affect your pan-session analysis. If you are interested in learning more about measuring across multiple devices and browsers, you should read: Google Universal Analytics: A User-Centric Approach.

Paul Koks is the owner and editor of OnlineMetrics, a blog about Analytics and Conversion Optimization. You can follow him on Twitter @OnlineMetrics or connect on LinkedIn.

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Over the years I have met countless analysts around the globe, be it in conferences, training programs, client meetings or online. Some were true analytics geeks and others were just starting their careers, but one thing that I have always felt was missing, especially for the beginners, is a place centralizing the top resources and programs available.

As a followup of my workshops I have always used a cheat sheet with some good resources to learn more about Analytics in general and Google Analytics in particular. So I decided to work further on this cheat sheet and make it available here at Online Behavior. In addition, I am embedding in the end of this article a list created by my friend Stephane Hamel where he provides a short explanation and links to all major academic programs available in the field of Analytics.

Learning Google Analytics

Google Analytics is an amazing tool, relatively simple for beginners but full of surprises for the literate Analyst. I am not saying that every one can implement the tool and start extracting insights right away, but for someone willing to learn it, it does not require a PhD; and for the super advanced analyst Google provides a wealth of customizations to make the tool work for him/her own purposes.

Below I provide the resources I recommend to get started with Google Analytics, but since the list is long I prepared a free PDF for those that prefer to download and/or print. Click the button below to download the PDF.

Download the PDF

Web Analytics Basics

• The Web Analytics Process - onbe.co/WAprocess by Daniel Waisberg
• The Ultimate Definition of Analytics - onbe.co/xOTxPQ by Stephane Hamel
• A Web Analytics Primer: What does it all mean? onbe.co/WAprimer by Jordan Louis
• Book: Web Analytics 2.0 - onbe.co/14N1y1E by Avinash Kaushik

Google Analytics Basics

• Getting Started with Google Analytics [video] - onbe.co/LRMQO4 by Alex Langshur and Charles Davis
• Google Analytics Tips: 10 Data Analysis Strategies That Pay Off Big! - onbe.co/UWXkEi by Avinash Kaushik
• Official Google Analytics Training videos: onbe.co/KEyjIO by Google
• URL Builder: A Tool to Create Campaign Tags - onbe.co/LRKhf7 by Google
• eBook: Google Analytics Integrations - onbe.co/RR5yXq by Daniel Waisberg
• Book: Advanced Web Metrics with Google Analytics - onbe.co/KExzTT by Brian Clifton
• Book: Google Analytics - onbe.co/WZ9qe5 by Justin Cutroni

Google Analytics Features Analyses

• Introduction to Advanced Segments [video] - onbe.co/Khase2 by Nick Iyengar
• Step-by-Step Guide to Multi Channel Funnels - onbe.co/w9sJJ6 by Yehoshua Koren
• Introduction to Attribution Modeling & Multi-Channel Funnels - onbe.co/TFvuMb by Scott Shannon and Melaine Fuentes
• Content Experiments: A Guide to Creating A/B Tests - onbe.co/JHdQg0 by Daniel Waisberg
• A Guide to Google+ Analytics - onbe.co/IQArVQ by Daniel Waisberg
• Understanding Custom Variables - onbe.co/JKK7TK by Daniel Waisberg
• Tracking E-Commerce on Google Analytics [video] - onbe.co/12FDSO9 by Joe Christopher
• Getting Started with Google Analytics Remarketing - onbe.co/12a7fbv by Justin Cutroni

Advanced (Technical) Users

• How to Manage an Analytics Implementation - onbe.co/LRKQW9 by Daniel Waisberg
• Advanced Content Tracking with Google Analytics - onbe.co/12kypI8 by Justin Cutroni
• Google Analytics Sampling Demystified - onbe.co/10ZEEoX by Jordan Louis
• Google Tag Manager: A Step-By-Step Guide - onbe.co/VpCIV7 by Daniel Waisberg
• Universal Analytics - onbe.co/VEgzyw by Feras Alhlou
• eBook: Understanding Regular Expressions – onbe.co/JL6Ga7 by Lunametrics
• Google Analytics Code Website - onbe.co/KEyiVp by Google
• Google Analytics Help Center - onbe.co/U4ywIS by Google

If you want to download or print the list to check later, here is a free PDF.

Download the PDF

Digital Analytics Academic Resources And Conferences

However, there is only so much a person can learn at his/her own by sitting at home, reading articles and using a tool. That's why conferences and academic courses have an important place in the learning process. Below is a list of conferences that deal mainly with Analytics & Optimization:

• eMetrics Summit - http://onbe.co/X4ra4H
• Conversion Conference - http://onbe.co/X4rd0v
• X Change Conference - http://onbe.co/X4rh04
• Strata Conference - http://onbe.co/X4ryQJ
• Adobe Summit - http://onbe.co/X4rrVx
• Webtrends Engage - http://onbe.co/X4ruAB
• Google Analytics - while there is no official conference, check the Google Analytics Certified Partners, most of them organize local events with in-depth training.

And when it comes to formal education, the Analytics field is far from where it should be, with only a few universities providing courses and diplomas on it. Please find below the list of academic resources created by Stephane Hamel and feel free to add to it or rate the courses if you have taken them.