Google Analytics Study Guide 2

(Contd from Google Analytics Study Guide 1)

Filters
As we discussed in Google Analytics for Beginners, you can set a filter on a view that can exclude particular data, only include particular data, or modify the data during processing. This helps you align the data that shows up in your reports with your business needs. Filters are essentially 'rules' that Google Analytics applies to the data during processing. If the 'filter type' is true, Google Analytics will apply the filter to the data. If the filter type is false, Google Analytics won't apply the filter. There are two reasons you might want to apply filters. You may need to transform the data that shows up in a view. For example, you might want to include only data from a particular country in a view devoted to reporting on that country. Or you might want to exclude any internal employee traffic from a view reporting on customer data.

4 types of goals in GA
There are four types of Goals in Google Analytics: Destination (or Pageview) Goals are based on when a user views a particular page on your website. Event Goals are when a particular action defined as an event is triggered. Duration Goals are based on sessions that last over a set amount of time. 'Pages or Screens per Session' Goals are based on whether a user has viewed a set amount of pages in a session.

channel vs content grouping
You may want to organize the data you collect in different ways than the standard Google Analytics reports. Channel Groupings let you organize your data into customized channels, while Content Grouping lets you aggregate metrics within reports based on the organization of your website.

custom dimensions/metrics
You learned about dimensions and metrics in Google Analytics for Beginners. But you also can create your own dimensions and metrics in Analytics called 'Custom Dimensions' and 'Custom Metrics.' Custom Dimensions help you define a group of metric data that's specific to your business and then apply that as a dimension across your reports. Custom Dimensions can be used as a secondary dimension in standard reports, a primary dimension in a Custom Report, or as a segment. We'll discuss Custom Reports and segments later in the course. 'Custom Metrics' can be collected for any standard dimension or Custom Dimension that can't be measured by any predefined metric in Google Analytics.

data import
Data Import lets you combine this offline data to the hit data that Analytics collects from your website. This will allow you to include your own business-specific data you collected independently to give you more context and insight in your reports.

metrics
All Google Analytics reports are a single dimension, and the corresponding metrics for each value of that dimension. You'll notice that most reports in Analytics use rows for dimensions, and columns for the associated metric data. When you set up configurations like Goals or Enhanced Ecommerce, those metrics will be included as well. Analytics calculates the metrics that get grouped in various dimensions in two ways. Metrics are either calculated in aggregate such as total sessions, users, or pageviews, or specific dimensions (like Sessions or New Users per country). These are based on calculations Google Analytics performed during processing when it categorized the data it collected into users and sessions.

Let's look at how a few key metrics are calculated: Analytics can derive the 'Time on Page' by taking the timestamp of a pageview hit and subtracting that from the timestamp of the next pageview hit. 'Pages per session' is simply the average of how many unique pageview hits the user generated during their session. Average session duration is the average time spent from the first hit until the last hit before a user leaves the site or the session times out. Bounce rate is calculated by looking at users who only had one interaction on your site without a second interaction to calculate the session duration or time on page. If this occurs, the pageview of a bounced visit is assigned a session duration and Time on Page of zero.

Scopes
When Analytics creates dimensions and metrics during processing, it has to determine the scope of those dimensions and metrics in order to know how broadly applicable they are to your data. Some dimensions might organize data about a single hit, while other dimensions might apply to data across an entire session or individual user. Dimensions and metrics can have one of three scopes: hit-level, session-level, user-level. During processing, Analytics will determine which scope gets applied to each dimension and metric. You can only pair metrics with dimensions if they are both in the same scope. For example, pairing a 'hit-level' dimension like 'Page Title' with a 'session-level' metric like 'total number of Sessions,' wouldn't make sense, since 'Page Title' changes with each hit, but the 'sessions' count changes with the completion of each session. While Google Analytics pairs dimensions and metrics of the same scope together for you in standard reports, you will have to manually set the scope for any Custom Dimensions or Custom Metrics you create.

aggregate data tables
When Google Analytics has determined the dimensions and associated metrics, it links this raw, unfiltered data with the unique property ID for your account. Each reporting view you've created adds the data (with filters and configuration settings applied) to 'aggregate' data tables, which are processed daily. These aggregate tables are used to quickly display the standard reports in Analytics. But you also have the ability to generate more customized reports in Analytics using features like secondary dimensions or by creating a Custom Report. When you do this, Analytics checks to see if there is an aggregate table with the appropriate data. If the table doesn't already exist, Analytics goes back to the raw session data to process and create the report from scratch. In some instances, there is so much data to be joined that Analytics will show a sample of the data in the returned report, rather than calculating all of the data that was collected.

session sampling
For standard users, session sampling occurs at the property level, not the view level. This means that the sample set will be determined at the property-level before view-level filters are applied. So views that have filters applied may have fewer sessions in the sampled calculation.

It's important to understand that when data is collected and processed, it can't be changed. For example, if you set a filter to exclude data on a view, that data will be permanently removed during processing from the reports in that view and cannot be recovered.After Google Analytics has finished processing, you can access and analyze your data using the reports. It's also possible to access your Analytics data using the Google Analytics Core Reporting API. This allows you to build your own reporting tools or extract your data directly into third-party reporting tools.

macro vs micro conversions
There are key actions that users take on websites that fulfill your business objectives like making a purchase. We call these 'macro' conversions, since they represent the broader goals of your business. But there can also be smaller goals that bring users closer to your main objectives such as signing up for an email coupon or a new product notification. We call these 'micro' conversions, since they nudge users closer to your macro-conversions.Different businesses will naturally have different macro- and micro-conversions: For an e-commerce site, the macro-conversion might be to purchase a product with a micro-conversion of subscribing to a newsletter. For a lead generation site, the macro-conversion might be filling out a contact form with a micro-conversion of following the site on social media. For a content publisher, the macro-conversion might be engaging with a particular amount of content with a micro-conversion of clicking into an article. For an online information and support site, the macro-conversion might be completing a guided support flow to successfully solve an issue with a micro-conversion of rating a support article.

measurement plan
Once you've defined your macro- and micro-conversions, you can start putting together a measurement plan. A measurement plan is a way for you to align your business objectives with your Google Analytics configuration settings. Your measurement plan should include an overall business objective, different strategies that support that objective, and tactics that will help you achieve your strategies. Each tactic will have key performance indicators (or KPIs) that help you measure your macro- or micro-conversions. Macro conversions usually measure the tactics that support your various strategies. Micro conversions are metrics that help you better understand the user behavior that leads to macro conversions. Once you've identified the macro- and micro-conversions, and created a measurement plan to measure your business, you can decide how to set up Google Analytics to collect these metrics. Keep in mind that this is just one example of a very abbreviated measurement plan. Yours will likely be richer and more detailed, depending on the complexity and ambition of your business. A measurement plan is a great way to document the data that is most important to your business. Use the interactive Google Merchandise Store measurement plan at the end of this lesson for an example.

organizations
For example, if you're an agency managing marketing for multiple companies at once, you can set up different Organizations for each company with separate Google Analytics accounts under each Organization. When you create an account in Google Analytics, the account is assigned a unique ID. You can see this ID in the Analytics tracking code. This is how the tracking code knows to send hit data to the correct Analytics account.
To better reflect how your business is organized, you can set up multiple properties under each Analytics account. For example, The Google Merchandise Store may want to view data from their website and data from their mobile app in separate properties to analyze each data set independently. We recommend tracking each company's website, mobile app, or other device in a separate property.

cross domain tracking
If you have two related websites with different URLs or subdomains that you want to track in a single property, you can set up what's called 'cross-domain tracking.' Cross-domain tracking will recognize when a user navigates between related websites in the same session. This is also known as 'site linking.' To set up cross-domain tracking, you'll need to modify the Analytics tracking code on every page of every site you want to track. Google Tag Manager can make updating that code a lot easier

roll up property
Note that roll-up properties don't include data that you import or link from another account -- like Google Ads. If you want to include linked data from your source properties into your roll-up properties, you'll need to re-link the roll-up property with the linked account. Also, when users are identified by the same Client ID across different source properties, session data for those users is usually merged; otherwise, that session data remains separate.

properties and views with multiple websites
If you use Analytics to manage multiple websites, there are a few things to keep in mind. Each Analytics account has a limited number of properties and each property has a limited number of views. For example, let's say you're the administrator of a site with multiple sub-directories based on different departments in your business. You can create different views for each department using filters and then grant access to each view for the members of those departments. To navigate to different views, in the Admin section use the View selector menu.

types of filters
Filters can help refine your data and make it more readable in your reports. For example, you can use a filter to track activity in a specific website directory or track subdomains of your website in separate views. There are two kinds of filters: 'predefined' and ' custom' filters. Predefined filters have already been created for you in Google Analytics, you just have to select the filter you wish to use. These allow you to include or exclude data based on traffic from the ISP domain, IP addresses, subdirectories, or the hostname, and designate how the filter will match that information. Custom filters let you include or exclude hits from your data collection, format data to lowercase or uppercase, search and replace data collected in the hit. Custom filters accomplish this by matching a particular filter text-pattern that you identify.

For example, let's say your business was making a push into mobile and only wanted to analyze mobile traffic in a specific view. You can set up a custom 'include-only' filter on the view for Device Category and specify a value of 'Mobile.' The filter will look at the criteria specified and match it to any relevant hits that Google Analytics has collected for that view. If the filter can't match the criteria, the filter will not be applied to that data.Similarly, you may want to show only data for a specific campaign in a view. You can set up a custom filter to include only campaign data with the campaign name or type parameter you specified. Using view permissions, you can then share this campaign data with partners that you designate. If there was data you wanted to specifically exclude such as Paid Search (or CPC) traffic, you can set a custom 'exclude' filter that will exclude all paid traffic in a particular view, as well.

You can also use filters to normalize the data in your reports to make them easier to use. Google Analytics data isn't case sensitive, so pages in the All Pages report may show the same URL multiple times.You can quickly combine rows that differ only by case, by using a Lowercase or Uppercase filter. These filters will force the case to all lowercase or all uppercase, thus eliminating duplicate data.This will consolidate that page reporting and make the data in those reports a little neater.

regular expressions filters
In addition to include, exclude, and lowercase filters, there are other advanced filters that allow you to remove, replace, and combine filter fields in more complex ways using what are called 'regular expressions.' Regular expressions (or 'reg ex' for short) are characters that you can use to identify matching text in order to trigger an action. A basic regular expression on a filter can be something as simple as a word or a more complicated combination of characters. Let's say the Google Merchandise Store wants to set up a view with a filter to see all the keywords users searched for on their website for Android dolls. Because users might search for variations like 'Android plush doll,' or 'Android stuffed doll,' we can create a regular expression that identifies each of these variations. We can add an advanced filter with a regular expression that recognizes any Site Search queries that contain the terms 'android' and 'doll.' if you have technical query parameters passed in the URL of your website, you might have identical pages with different addresses.Because the URL is different, this page will show up multiple times in your reports. But since they're the same page, it may make sense to filter out the query parameters, so that it doesn't appear multiple times in a report. You can include a regular expression that recognizes the main part of the URL before the query parameter, puts it in a variable, and overwrites the entire URL with that variable. This renders these page URLs identical in reporting.For businesses that collect data from multiple domains, it can be hard to distinguish page names in Google Analytics. In the 'All Pages' report, 'googlestoreamerica/index.axd' and 'googlestoreeurope/index.axd' will both show up as 'index.axd.' You can use a regular expression to add the hostname in Analytics so that you can distinguish between multiple domains.

order you apply filters
Each filter passes filtered data to the next filter in the sequence, so you'll want to be thoughtful about the order in which you apply your filters.You can adjust the order of your filters by going into 'Admin' and selecting 'Filters'. Then select 'Assign Filter Order.'Note that you can use filters across multiple views, but be careful. If you edit the filter, those changes will be applied across all the different views to which you've applied that filter.Once you have set up your configuration, Google Analytics processes the data by checking each hit against your filters. If a hit matches the logic in a filter, then that filter will be applied.Remember to test out your filters in a 'test' view before you apply them to the 'master' view. Also, be sure to test out your filters on real-time reports to make sure they're working because they may take several hours to filter all of your data.

custom dimensions
Custom dimensions are similar to default dimensions except that you define what they are and their value. This let's you collect data that's customized specifically for your business. This can be incredibly powerful because it enables you to report on particular characteristics of your users or their behavior within the Google Analytics data you've collected. You collect data for a Custom Dimension using JavaScript tracking code that's implemented on a page. When a user lands on that page or performs a specific action, the Custom Dimension will capture that data and send it over as an additional parameter attached to the existing hit. You can then use these Custom Dimensions in your reports.

You'll first have to name the Custom Dimension and then define its scope. For example, if you want the dimension to include every time a user visited a particular page or performed a singular action, you will need to set a hit-level scope. If you want the dimension to group data associated with a particular product, you will set a product-level scope. If the dimension was organizing data for the duration of a session or for a particular user, you can set session- and user-level scopes, respectively. Like standard dimensions and metrics, Custom Dimensions and Metrics can only be paired with dimensions or metrics from a similar scope.

When you create a Custom Dimension for the first time, you'll be taken to a screen with JavaScript to include on your website. You can copy the code, then click 'Done.' You'll be taken to an overview screen where you can see all of the Custom Dimensions that you have set up in that property. Notice that, similar to Goals, Google Analytics assigns an index (or slot number) for each Custom Dimension you create. Note that you cannot choose which index number is assigned; they are assigned in the order you created them. After you've set up the Custom Dimension, you must implement the JavaScript tracking code you copied from Analytics into your website code to collect the custom data. Different businesses will do this in different ways, depending on their data collection method and what data they wish to collect. Google Tag Manager is a great option for managing Custom Dimension tracking code more easily.
You can use Custom Dimensions as secondary dimensions in standard reports or as primary dimensions in Custom Reports (which we'll discuss later). For example, if the Google Merchandise Store wanted to see which products were most popular among employees and retail customers, we can open the Product Performance report under Conversions in Ecommerce and add the secondary dimension we set up of 'User Category.' Note that you won't be able to apply a Custom Dimension to data you have previously collected. You'll have to create the Custom Dimension first and let it be applied to your data during processing in order to use it in reports.

custom metric
Custom Metrics let you collect metrics in Google Analytics that are specific to your business. This can be the number of ads that loaded on a page, the bandwidth that the page consumed when it loaded, or the total number of brand pageviews that each of your marketing channels leads to. Similar to Custom Dimensions, you collect Custom Metric data using JavaScript that's implemented on a page. When a user lands on that page or performs a specific action, the Custom Metric will be sent as an additional parameter attached to the hit.

You first have to name the Custom Metric. Then you have to define its scope. This is based on how this metric data will be generated. Unlike dimensions, Custom Metrics can only have a scope of 'hit,' or 'product.' If you select 'hit,' the Custom Metric will be incremented with each hit sent over by the tracking code and totalled up in Google Analytics. If you select 'Product,' the Custom Metric can increment by whatever cost you assign to the product. We'll select 'hit,' since we want the Custom Metric sent over with each pageview hit of Android merchandise pages. Next, we'll need to specify the format of the Custom Metric. You can select a basic integer, a decimal value, or a time-based value. Since we want to total up pageviews, we can send a basic integer of 'one' with each hit. This will then increment the Custom Metric in Google Analytics by 'one' each time a pageview hit fires. You can also specify minimum and maximum values that determine whether Analytics will process this metric and include it in your reports. This can help prevent accidental large or small values from being collected and affecting your reporting. Since we know we don't want our range to exceed 1, we can set a minimum value of 0 and a maximum value of 2.

When you save a Custom Metric for the first time, you'll be taken to a screen with JavaScript to include on your website. You'll need to copy the code to include on each page you want the Custom Metric to be sent. Then click 'Done.' You'll be taken to an overview screen where you can see all of the Custom Metrics that you have set up in the property. Notice that, similar to 'Goals' and 'Custom Dimensions,' Google Analytics assigns an index (or slot number) for each Custom Metric you create. Notice that you cannot choose which index number is assigned; they are assigned in the order you created them. After you set up the Custom Metric, you must add the JavaScript tracking code you copied from Analytics to your website to collect the data with the hit. Like Custom Dimensions, each Custom Metric appears as a parameter of index-value pairs. 'Index' refers to the index number of the Custom Metric you created in Analytics. Value is the metric that will be attached to the hit.

event tracking
Event tracking is a great way to know if users are engaging with your website and performing intended actions. The Google Merchandise Store, for instance, can track clicks on the global navigation bar to better understand how users navigate their website.To collect Event data from a website, you'll need to add JavaScript to the individual elements on the site you wish to track.
When a user performs an action on an element with event tracking, the event tracking code will pass four parameters along with the event hit. These parameters are: 'Category,' 'Action,' 'Label,' and 'Value.'

You can define these parameters in your JavaScript to organize the data in your event reports. 'Category' lets you organize the events you track into groups. For your website, this might be 'Videos' or 'Social Shares.' 'Action' is the action the user took when they initiated the event. If you were tracking when users click a video play-button, you might have a category called 'Videos' with an associated action of 'Play.' 'Label' is an optional value used to further describe the element you're tracking like the name of a video. This can help you make your event reports more readable. 'Value' is an optional numerical value like the amount of time it takes a video to load or how much a specific event action is worth. You can use Value to assign a specific dollar amount when a specific action occurs. Once the event tracking code has been added to the navigation element, every time a user interacts with that element it will pass the parameters that were assigned to Google Analytics, which will appear in the Events reports.

If you click into the Category, you can see the associated Actions. This can help you view the various interaction states that were tracked for a Category in one place. If you click into the action, you can see the labels associated with that action. Another great use for events is tracking outbound link clicks that lead away from your site. For example, the Google Merchandise Store has a live chat button in their top navigation bar that opens a pop-up window when clicked. However, this pop-up window was implemented by a third-party vendor and goes to a different URL that the Google Analytics tracking code won't track by default. We can set up event tracking on this button with the category 'Outbound links,' an action of 'Live Chat,' and a label of 'Home' (or wherever the live chat button was clicked from). That way, we can tell how many times the live chat button was clicked and from what page. We can then know which web pages were causing users to seek help and work to better optimize those pages.

total events
Total Events are calculated as the total number of interactions with the tracked element, while Unique Events are how many users have triggered that event. So if a user clicks on the Google Merchandise Store's navigation for 'Bags' five times in a single session, the total number of link clicks for that event will be 'five,' but the number of Unique Events will be counted as 'one.' Events reports are found under Behavior. When you open the 'Top Events' report, events are organized by category.

segmentation
Segmentation in Google Analytics is a way to view a subset of data in a report. You can create user segments or session segments. User segments can span multiple sessions with a maximum date range of 90 days. For example, you can build a user segment that shows data only for a specific age range, date range, gender, or a combination of these. Session segments are confined to user behavior within a single session. For example, you can create session segments for a Goal users completed during the session or the amount of revenue a user generated.

A powerful part of segments is the ability to add multiple segments to a single report for comparison. You can compare segments of users who made a purchase with those that didn't, to better understand what influences people to buy. Or you might choose to build segments based on a specific traffic source like paid search and compare that to sessions that originated from email campaigns. This helps you see which types of users each source delivers.
Default segments: If you select Tablet traffic, for instance, and click Apply, you'll be able to compare Tablet traffic with all of the traffic in any of your reports. These segments will be applied to every report you open until you remove the segment or exit Google Analytics.To remove a segment, click the down arrow and select 'Remove.'To compare new and returning users in reports, you can select the New Users and Returning Users segments. Notice that these segments will show up at the top next to the All Users segment. If we want a cleaner report for comparison, we can turn off the All Users segment and click Apply. This now compares only new and returning users.

For example, under Demographics you can choose age '25 to 34' and language contains 'es' for Spanish, which will filter the data for users between the ages of 25 and 34 who have their browsers set to Spanish. You can also create segments based on sequences of user interactions. For example, you can segment users that viewed a specific page and then watched a video. Sequences can be a mixture of pageviews or events.

Note that segments are applied after sampling. So if the data being shown in your reports is a sample, the data shown in your segments will also be a sample. As you encounter more complex questions about your customers' behavior, you can create segments to isolate subsets of data and find opportunities to improve your website's performance.

Remarketing
Remarketing is a powerful tool that lets you target ad content to users who have already visited your website. When a user visits your site and doesn't make a purchase, you can use remarketing to show them relevant ads on the Google Display Network, on mobile apps, or on Google Search. This can bring them back to your website and encourage them to make a purchase. To enable Remarketing in Google Analytics, you need to first enable Advertising Features in your Analytics property settings.

Once you've set up remarketing, you can create specific 'Audiences' that let you target groups of users based on common attributes. Audiences are made up of browser cookies from users that visited a site with Google Analytics implemented and the remarketing tracking code enabled. Audiences allow you to target ads to those users. For example you can create a Remarketing audience that includes users who visited a specific page of your website or clicked to play a video. Since website remarketing utilizes browser cookies, creating remarketing audiences in Analytics doesn't require any additional tagging on your website. But note that if a user clears their browser cookies, they will no longer be a part of the remarketing audience you created until they visit your site again.You can set how long users are eligible to be served remarketing ads using the membership duration. You can set Membership duration for your audience from 1 to 540 days.
If you wish to design a more specific audience for your business, you can import a Segment to use as the basis for that audience. Click Import Segment and choose from the segments that are available in the current property or create an audience directly from the segment picker itself.

dynamic remarketing
Dynamic Remarketing with Analytics lets you target remarketing ads more precisely. It enables you to target based on content or products users previously viewed on your site, related and top-performing content and products, and purchase histories and demographics. For example, the Google Merchandise Store can collect product IDs from the merchandise that users viewed on their website and later advertise those products to those same users to bring them back to the Store website and make a purchase.

To set up Dynamic Remarketing, you first need to link your Google Ads and Analytics accounts, and enable Advertising features, as we've discussed previously. Retail businesses will also need to link their Google Ads accounts to their Google Merchant Center. The Merchant Center is a website that lets shoppers see your online and in-store inventory. Dynamic remarketing campaigns can use this product data to better customize ads. To enable Dynamic Remarketing, you will need to: Find your vertical attributes for Dynamic Remarketing, create your Custom Dimensions, and update your website tags, Create audiences for Dynamic Remarketing, Create attributes for Dynamic Remarketing, And create your Dynamic Remarketing campaign in Google Ads