Creating an account

 

Study Guide

• Learn where to implement your Google Analytics code

Transcript

Understanding your account structure

 

Study Guide

• Understand the hierarchy of a Google Analytics account and the meaning of the terms “Account," “Property” and “View”
• Recognize how account structure affects which data can be viewed together in Google Analytics

 

Transcript

 

In this lesson we will discuss how a Google Analytics account is structured,and the implications this has for data collection, processing and reporting.

 

A Google Analytics account is simply a logical way for a business to group data from all of its digital assets together. There are also certain configuration settings that you apply to your entire account, like managing the users who have access. 

 

Within each account, you can have one or more properties that independently collect data.

 

 

Each property is assigned a unique tracking ID that tells Google Analytics exactly which data should be collected, stored and reported together. Typically you create separate accounts for unique businesses or distinct business units.

 

 

Then you can create unique properties within that account for the different websites, mobile applications, or other digital assets that belong to the business. This approach makes it easy to view the data from an individual digital asset. However, it doesn't allow you to view data in aggregate for multiple assets since the data for properties is physically stored and reported in different places.

 

If you do decide you want to view data for two assets together instead of separately, you must adjust your implementation to collect the data together in a single property using the same tracking code across both assets.

 

 

For example, if you use this technique to track two different websites with the same tracking code, Google Analytics collects, and reports on, both websites in the same property. This is commonly called rollup reporting. 

 

Refer to the resources in this lesson for more details on rollup reporting. For each property, you have the option to create different views of your data. A view lets you define a unique perspective of the data from a parent property. You use the configuration settings in your account to define each view.

 

 

Usually you create multiple views for each property in order to protect and manage your data. For example, if your organization has different sales teams for various geographic regions, you might want to create a specific view for each region. Then give each sales team access to the relevant view. That way, your sales team members can easily see just the data that is important to them.

 

 

We recommend that you have at least three views for each property.

 

• By default, you have one unfiltered view that is automatically generated when you create a property. Don't apply any settings or configurations to this view since it is the backup for your data.
• Second, you should have a master view. This view should have all of the settings needed to transform your data into useful information.
• Finally, you should have a test view.

 

If you need to make changes to your configuration test them using this view first. Once you know the impact to the data you can then apply the same change to your master view. This type of account and view structure helps protect your data. Remember, once Google Analytics processes data from your website or app the data can never be changed.

 

 

So, if you have a bad configuration setting, and Google Analytics processes your data, you could have inaccurate data. It's also important to know that once you delete a view it's gone forever. So having a backup view, like the unfiltered data view, is very useful. 

 

One more tip: when you create a new view Google Analytics does not automatically copy any of the historical data in the original view to the new view.  You'll only have data from the date you create the view onward.

 

To help understand how you would organize your account structure in real life, let's take a look at an example for our fictional outdoor company. Let's say our company has developed a website and a mobile application for visitors to shop. In this scenario, we would create one property for the mobile store and one property for the website.

 

 

Remember, within each property, we should always have an unfiltered data view. This is the view that collects all of the data for the property and has no configuration settings. 

 

In addition to the unfiltered data view, we should create a master view that has been configured to match our business needs from our measurement plan. For example, for the store website, we could create a filtered view that excludes any data from our internal IP address. This data represents traffic from our employees, which we don't want mingled with our customer data. And, as mentioned previously, we should have a test view to try new settings.

 

For the mobile app, we would also have an unfiltered data view, a master view and a test view. In addition, we could create one view that only shows tablet traffic and another that only shows smartphone traffic.

 

Again, the structure of your reporting views is entirely up to you, but always remember to keep an unfiltered view that you never delete. Because the structure of your properties and views affects your ability to achieve your long term reporting goals, we strongly recommend that you develop your measurement plan prior to setting up your account, properties and views.

 

Now, let's have you practice working with properties and views in Google Analytics. Visit the account you created previously, and set up your properties and views according to the instructions in the activity.

 

Setting up basic filters

 

Study Guide

• Understand how filters act on data
• Practice creating a basic filter to exclude internal traffic from your business
• Become familiar with other basic examples of using filters for GA

Transcript

In this lesson we will cover the basics of filters including some common filters that can help improve the quality of your reports.

 

Filters provide a flexible way of modifying the data within each view.

You can use them to

• exclude data,
• include data,
• change how the data looks in your reports.

Filters help you transform the data so it's better aligned with your business needs. During processing, Google Analytics applies your filters to the raw data collected from your website or app. This transformed data is what you see in the reports for each view.

 

Let's look at an example: From a business perspective you probably only want to analyze data from your customers and potential customers. You don't want data from your employees mixed in with data from your customers. You can use a filter to exclude traffic from your internal employees.

 

 

The easiest way to do this is to create a filter that excludes all of the data from the IP address for your business.  As Google Analytics processes your data it will ignore any data coming from that IP address. 

 

You can also use a filter to clean up your data. For example, sometimes a website will show the same page regardless of the case of the URL -- uppercase, lowercase or mixed case. Since Google Analytics treats data as case-sensitive, this can result in the same page showing up multiple times, based on case, in your reports. To prevent this separation and see the page data in aggregate, you can set up a lowercase filter to force all of the URLs to a single case.

 

 

Now let's dig a bit deeper into the details of setting up filters. Filters are instructions to Google Analytics to transform the data within a view. We call these rules "conditions." If the condition is true, then Google Analytics will take some type of action. If the condition is false Google Analytics won't do anything.

 

 

First, you need to identify the type of data you want to evaluate or change. You do this by selecting the Filter Field.

 

 

Some common fields include User IP address, Device type or Geographic location.

 

Next, you specify the condition, or set of rules for the filter. During processing, when Google Analytics finds the field, it applies the condition. There are many types of conditions. Common ones include "matches a certain pattern," or "does not match a certain pattern."

 

 

For example, if you are using a country filter, your condition could be "matches United States."

 

Finally, you choose the action that Google Analytics takes if the condition is true. You can select to include the data that meets the condition, exclude it, or change it from its raw form into some new and more useful.

 

 

Remember, filters, like all configuration settings, are not applied retroactively to your data. They are only applied from the moment you create them.

 

To help simplify filter setup, we've divided filters into two categories:

• Predefined Filters
• Custom filters.

Predefined filters are templates for some of the most common filters.

 

 

Custom filters let you truly customize filters to fit almost any unique situation.

 

 

Let's go back to the examples in this lesson to see how you set up a filter field, condition, and action. We'll start with excluding internal traffic from your company. To set up this filter, you'd select "Predefined filter."

 

For the filter field, you'd select "traffic from the IP addresses," and enter an IP address from your company that you want to exclude. Select the condition "that are equal to."

 

Or, if you need to exclude a range of IP addresses, you might select "that begin with."  Then, for the action, select "Exclude.". Once you've saved this filter and applied it to a view, Google Analytics starts checking the IP addresses of traffic to the web property. Any traffic data from the IPs that you've excluded in the filter will be thrown out of the views to which the filter has been applied.

 

 

To add a filter that forces all the URLs to lowercase, you can use a custom Lowercase filter. First, choose the custom filter type, then choose the Lowercase filter option. Next, tell Google Analytics which filter field should be transformed to lowercase. That's it!

 

 

For this particular type of filter, the condition and action are both implied and don't need to be set up. There are many other types of predefined and customized filters that you can use in Google Analytics.

 

In addition to exclude filters and lowercase filters, there are "include" filters,  "uppercase" filters and other advanced filters that allow you to remove, replace, and combine filter fields in more complex ways.

 

It's very common to apply multiple filters to the same view. But keep in mind that filters are applied in the order that they appear in your configuration settings. Filter order matters, because the output from one filter becomes the input for the next filter.

 

 

Let's say you want to modify the data in a view to only include data from the United States and Canada. Your first reaction might be to create two Custom include filters. One filter to include traffic from the United States and one to include traffic from Canada. Actually, this won't work, and here's why. During data processing, Google Analytics will apply the filters in the order that you set them. If the first filter is set to only include United States traffic, the data that is output from that filter will not contain any data from Canada.

 

The solution is to instead create a single include filter that will include data from United States OR Canada. Once you create a filter it's added to the Filter Library for your whole account. This means that you can reuse filters you've already created and apply them to any view.

 

 

Remember, you should always try any new filter on your test view first. This helps ensure that you understand the result of your filter before applying it to your master data view.

 

 

The filters you choose to implement will depend on your specific measurement objectives, so it's important to plan first before you start setting up your filters.

 

Setting up goals and ecommerce

 

Study Guide

• Understand how to set up goals once you’ve put together your measurement plan
• Recognize which types of actions can be tracked as goals
• Understand how funnels can be used to analyze the path to conversion
• Recall the steps to set up ecommerce tracking

Transcript

 

In this lesson we'll talk about tracking macro and micro conversions in Google Analytics. We do this using a feature called "Goals."

 

We'll walk through how Goals are set up and then have you practice on your own test account. Just a note - we're going to walk through creating Goals for a website, not an app. But the app process is very similar.

 

Setting up Goals in Google Analytics is one of the most important parts of implementation. Goals are the way that we map the data in Google Analytics to the key performance indicators that you defined in your measurement plan. Anytime you think of conversions, you should think about "macro conversions" and "micro conversions". Macro conversions are your primary business objectives.Micro conversions are the relationship building activities that lead up to a macro conversion.

 

As an example, for our fictional outdoor company, the macro conversion is to sell products. A related micro conversion could be a sign-up for a promotional newsletter. When a user signs up for a newsletter it's an indicator that she wants to stay connected. It gives us, as a business, an opportunity to continue to market to the user and hopefully convert her into a customer. Once you enable Goals, you get metrics like the number conversions and the conversion rate. These metrics are always available in the Conversion section of your standard reports. But you can also find these metrics in almost every other report in Google Analytics.

 

This is useful because the reports allow you to segment your conversion data. For example, if you look at the conversion rate metrics in the Traffic Sources report you can instantly see which marketing channels drive the most value for your business.

 

 

Now let's talk about the specific features of Google Analytics Goals. Goals are configured at the view level. That means you can create different Goals for each view. 

 

There are four types of Goals. Each Goal is slightly different to set up, but they all do the same thing -- they track conversions on your website or in your app.

 

 

First let's talk about Goals that track user actions. A destination Goal is a page on your website that users see when they complete an activity. For an account sign-up, this might be the "thank you for signing up" page. For a purchase this might be the receipt page.

 

 

A destination Goal triggers a conversion when a user views the page you've specified. If you're setting up a Goal for an app, you'd set up a screen view Goal rather than a destination Goal. 

The second type of Goal you can use to track a user action is an Event Goal. This kind of Goal is triggered when a user does something specific like downloading a PDF or starting a video.

You need to have Event Tracking implemented on your website in order to use this type of Goal.

 

 

The last two types of Goals can be used to measure user engagement. Engagement can be measured based on the amount of content that people see or the amount of time that they spend on your site.

 

A Pages per Visit Goal is triggered when a user sees more or fewer pages than a threshold that you specify. A Time on Site Goal is triggered when a user's visit exceeds or falls below a threshold that you set.

 

 

Let's talk through how to set up a destination Goal for a website. When you select "Destination" as the Goal type, you specify the Goal page. You don't have to enter the entire URL for the page. Just enter the request URI - that's what comes after the domain or hostname. So, if the complete URL is  myoutdoorstore.com/confirmation.html, you only need to enter /confirmation.html. Make sure that the URI corresponds to a page that the user only sees once they complete the conversion activity. So, pick something like the "Thank You" page or a confirmation page for your Goal.

 

 

You can also enter a name for the Goal, like "Completed Order."  This name will appear in your reports. When you set up a destination Goal, you have three choices for the Match Type option: Equals, Begins with, and Regular Expression.

 

 

The Begins with match type indicates that the page URI must begin with what you specified as the Goal URI. So, if you specify "/thankyou" as your Goal, any page URI that begins with "/thankyou" will count as a conversion. For example, visits to "/thankyou.html" or "/thankyou.php" would both count as a Goal conversion. 

• "Begins with" provides flexibility with limited complexity. It works to track most destination Goals.
• The Equals match type means that the URI of the page visited must exactly match what you enter for the Goal.

 

In contrast to Begins With, which can be used to match multiple versions of a URI, an Exact Match only matches one single URI.

 

The Regular Expression match type gives you the most flexibility, but requires you have expertise in Regular Expressions. For example, if you want to count different pages, all with different URIs as a Goal, you can create a regular expression pattern that will give Google Analytics the set of rules to match any of these URIs.

 

 

 

To learn more about Regular Expressions, check out the resources in this lesson. Another part of the Goal setup process is the verification tool. This simple tool let's you quickly test your Goal settings before completing the process. Just click the "Verify this Goal" link and Google Analytics will give you an expected conversion rate for this Goal, based on your settings, for the last 7 days.

 

If the conversion rate seems too high or too low then there's probably an issue with your settings. The "Goal Value" field allows you to specify a monetary value for a Goal. This is really useful because Google Analytics will interpret the Goal value as actual revenue. By setting a Goal value, you make it possible for Google Analytics to calculate metrics like advertising ROI and average per visit value.

 

These metrics will help you measure the monetary value of a non-ecommerce site. If you're having trouble calculating a Goal value think about how much each user action, or Goal conversion, is worth to your business.Let's say your website generates sales leads. If your sales team closes sales on 10% of the leads, and your average transaction is $500, you might assign $50 or 10% of $500 to your Goal value.

 

 

For each destination Goal that you create, you can also set up a funnel. A funnel is the defined process that you expect users to complete prior to conversion. A sales checkout process is a good example of a funnel. To complete a transaction, the user must add items to a cart, enter shipping information, and submit payment information.

 

To define a funnel, you specify the pages leading up to the Goal. Notice that there is no match type for the funnel steps. Google Analytics uses the same match type that you used for the Goal page.

 

You can also provide a name for each step in the funnel, which will show in your reports. Defining a funnel is valuable, because it allows you to see where users enter and exit your define process. Using the Goal Flow report you can identify where users might be having issues with an important step. For example, if you notice that many of your users never go further than the shipping information page, you might focus on redesigning that page so that it's simpler.

 

 

Knowing where customers drop off during the process allows you to eliminate bottlenecks and create a more efficient conversion path.

 

In addition to conversion metrics, setting up Goals also activates the Multi-Channel Funnels reports. These reports show you the path a user took toward conversion across multiple visits to your site.

 

 

Conversion path data is generated for each Goal conversion and ecommerce transaction recorded by Google Analytics. If your site sells products or services online, you can also use Google Analytics ecommerce reporting to track sales activity and performance. The ecommerce reports show you transactions, revenue, and many other commerce-related metrics.

 

 

Some examples of the kind of information you can get from the ecommerce reports include the products that were purchased from your online store, a list of transactions, and the number of times people visited your site before purchasing.

 

In order to use ecommerce reporting, you need to do the following three things.

 

• First, enable ecommerce reporting within your Analytics website view.
• Second, add the Google Analytics Tracking Code to your receipt page or "Transaction Complete" page.
• Finally, add some additional ecommerce tracking code to your receipt page so that you can capture the details of each transaction.

 

 

Your development team needs to be involved with this set-up process.

Please refer to the resources in this lesson to find more details on how to implement ecommerce tracking.

 

If you do use ecommerce tracking in Google Analytics, there is an important difference between Goal conversions and ecommerce transactions that you should be aware of. A Goal conversion can only be counted once during a visit, but an ecommerce transaction can be counted multiple times during a visit.

 

Here's an example: Let's say that you set one of your Goals to be a PDF download and you define it such that any PDF downloaded is a valid Goal conversion. And let's also say that the Goal is worth $5. In this case, if a user comes to your site and downloads five PDF files during a single session, you'll only get one conversion worth $5. However, if you were to track each of these downloads as a $5 ecommerce transaction, you would see five transactions and $25 in ecommerce revenue.

 

 

 

One more thing about Goals and ecommerce transactions.

As a best practice, you should only add a Goal value for non-ecommerce Goals. The reason is that Goal value is cumulative. If you add a Goal value, and you track transactions with the ecommerce tracking code Google Analytics will add the value of the transaction to the value of the Goal. Now it's your turn to try setting up a Goal in your test account. Follow the instructions in this lesson to complete your Goal set-up.

 

 

Collecting campaign data

 

Study Guide

• Recognize standard acquisition dimensions
• Understand the importance of correctly categorizing your acqusition sources in Google Analytics
• You don't require to study the 2nd half of the video: campaign link tagging and practice using the URL builder to create manual tags for an example campaign