Platform Principles: Unit 3. Processing & Configuration

Analytics Academy

Platform Principles: Processing & Configuration Overview

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Introduction

In this unit, we’re going to cover two parts of the Google Analytics platform that go hand-in-hand: processing and configuration. These two components work together to organize and transform the data that you collect into the information you see in your reports.

Processing data and applying your configuration settings

During processing, there are four major transformations that happen to the data. You can control parts of these transformations using the configuration settings in your Properties and Views.

• First, Google Analytics organizes the hits you’ve collected into users and sessions. There is a standard set of rules that Google Analytics follows to differentiate users and sessions, but you can customize some of these rules through your configuration settings.
• Second, data from other sources can be joined with data collected via the tracking code. For example, you can configure Google Analytics to import data from Google AdWords, Google AdSense or Google Webmaster Tools. You can even configure Google Analytics to import data from other non-Google systems.
• Third, Google Analytics processing will modify your data according to any configuration rules you’ve added. These configurations tell Google Analytics what specific data to include or exclude from your reports, or change the way the data’s formatted.
• Finally, the data goes through a process called “aggregation.” During this step, Google Analytics prepares the data for analysis by organizing it in meaningful ways and storing it in database tables. This way, your reports can be generated quickly from the database tables whenever you need them.

Conclusion

Understanding how Google Analytics transforms raw data during processing, and how your configuration settings can control what happens during processing, will help you better interpret and manage the data in your reports.

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Analytics Academy

Platform Principles: Processing Hits into Sessions & Users

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Introduction

Google Analytics uses a data model with three components -- users, sessions and interactions -- to organize the data you see in your reports. These three components are derived from the hits that the tracking code sends to Google Analytics. In this lesson, we’ll focus on how Google Analytics transforms hits into users and sessions.

How hits are organized by users

First, let’s talk about how Google Analytics creates users. The first time a device loads your content and a hit is recorded, Google Analytics creates a random, unique ID that is associated with the device. Each unique ID is considered to be a unique user in Google Analytics. This unique ID is sent to Google Analytics in each hit, and every time a new ID is detected, Google Analytics counts a New User. When Google Analytics sees an existing ID in a hit, it counts a Returning User.

It’s possible for these IDs to get reset or erased. This happens if a user clears their cookies in a web browser, or uninstalls and then reinstalls a mobile app. In these scenarios, Google Analytics will set a new unique ID the next time the device loads your content. Because the ID for the device is no longer the same as it was before, a New User gets counted instead of a Returning User.

The unique ID that Google Analytics automatically sets is specific to every device, but you can customize how Google Analytics creates and assigns the ID. Rather than using the random numbers that the tracking code creates, you can override the unique ID with your own number. This lets you associate user interactions across multiple devices.

How hits are organized into sessions

Now let’s talk about how Google Analytics creates sessions. A session in Google Analytics is a collection of interactions, or hits, from a specific user during a defined period of time. These interactions can include pageviews, events or e-commerce transactions.

A single user can have multiple sessions. Those sessions can occur on the same day, or over several days, weeks, or months. As soon as one session ends, there is then an opportunity to start a new session. But how does Google Analytics know that a session has ended?

By default, a session ends after 30 minutes of inactivity. We call this period of time the session “timeout length.” If Google Analytics stops receiving hits for a period of time longer than the timeout length, the session ends. The next time Google Analytics detects a hit from the user, a new session is started.

Here’s a simple illustration of what sessions might look like in the real world. Let’s say a user searches for something on google.com, and clicks one of the search results. When they land on the webpage, a New User is detected, a pageview hit is collected, and the session begins. If the user clicks to another page on the same site, the new pageview hit is sent to Google Analytics and processed as a part of the same session.

But let’s say the user leaves their computer for two hours. When they return to their computer and click to a new page on the same site, a new session will begin. Google Analytics automatically ends the first session because too much time passed without receiving any hits. In this scenario, Google Analytics will process the data as two separate sessions.

Session timeout length

The 30 minute default timeout length is appropriate for most sites and apps, but you can change this setting in your configuration based on your business needs. For example, you might want to lengthen the session timeout if your website visitors or app users do not interact with your content frequently during a session, like if they watch a video that’s longer than 30 minutes.

Conclusion

Users and sessions are a critical part of the digital analytics data model. All of the reports you see in Google Analytics depend on this model to organize the data. And the better you understand how Google Analytics creates users and sessions from the raw data, the more you’ll get out of your reports.

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Analytics Academy

Platform Principles: Importing Data into Google Analytics

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Introduction

The most common way to get data into Google Analytics is through your tracking code, but you can also add data from other sources. By adding data into Google Analytics, you can give more context to your analysis.

Importing data into Google Analytics

There are two ways to add data into your Google Analytics account without using the tracking code: through account linking and through Data Import. Both are managed via your Configuration settings in the Admin section of Google Analytics. Any data that you add from these sources will be processed along with all the hits you collect from the tracking code. Let’s look at account linking first.

Account linking

You can link various Google products directly to Google Analytics via your account settings. This includes:

• Google AdWords
• Google AdSense
• Google Webmaster Tools

When you link a product, data from that product flows into your Analytics account. For example, if you link AdWords to Google Analytics, you’ll see your AdWords click, impression and cost data in your Analytics reports.

Data Import

In addition to account linking, you can add data to Google Analytics using the Data Import feature. This might include advertising data, customer data, product data, or any other data.

To import data into Google Analytics there must a “key” that exists both in the data that Google Analytics collects and in the data you want to import. The key is the common element that connects the two sets of data.

There are two ways to import data into Google Analytics:

1. Dimension Widening
2. Cost Data Import

Using Dimension Widening

With Dimension Widening, you can import just about any data into Google Analytics. For example, if you’re a publisher you might want to segment your data based on the author and topic of your online articles. While this data is not normally collected by Google Analytics, you might have it stored in an internal system.

With Dimension Widening, you could import author and topic as new dimensions for your content pages. You could use each article’s page URL as the “key” that links the new data to your existing Google Analytics data. Once added, author and topic would be treated just like any other dimensions in Google Analytics -- you could add these dimensions to custom reports, dashboards or segments.

You can add data using Dimension Widening either by uploading a file or by using the Google Analytics APIs. Uploading a file, like a spreadsheet or .CSV, is easy, but it can be time consuming if you need add data often. To save time, you can build a program that uses the APIs to automatically send data into Google Analytics on a regular basis.

Using Cost Data Import

The other kind of data import is called Cost Data Import. You use this feature specifically to add data that shows the amount of money you spent on your non-Google advertising. Importing cost data lets Google Analytics calculate the return-on-investment of your non-Google ads. This is helpful when you want to compare the performance of your advertising campaigns.

To import cost data for a specific advertising campaign, you have to have a file that includes both the campaign source and the campaign medium. This information provides the key that can link the two data sources together.

Conclusion

Although you’ll collect most of your data using the tracking code, account linking and data import are two powerful ways to add more context to your Google Analytics data. By choosing the right data sources to link or import into Google Analytics, you can better measure the performance of your business.

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Analytics Academy

Platform Principles: Transforming & Aggregating Data

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Introduction

An important part of processing is data transformation and aggregation. This is how Google Analytics applies your configuration settings to all of your data and prepares it for your reports.

The role of configuration settings during processing

Your configuration settings can impact your reports in one of three ways: by including data, excluding data, or modifying how data appears in a reporting View.

There are a lot of configuration options in Google Analytics, but we’re going to talk about a few of the most important ones that everyone should try: Filters, Goals, and Grouping.

Common configuration settings: Filters

Filters provide a flexible way you can modify the data within each view. You can use them to exclude data, include data, or actually change how the data looks in your reports. Filters help you transform the data so it’s better aligned with your reporting needs.

For example, you can create a filter to exclude traffic from a particular IP address or to convert messy page URLs into readable text. During processing, Google Analytics checks each data hit against your filters. If a hit matches the logic in a filter, that data is modified. If you excluded traffic from a specific IP address, for example, any hit coming from that IP address will be permanently removed from your report data.

Common configuration settings: Goals

Another way to transform your data is to set up Goals. When you set up Goals, Google Analytics creates new metrics for your reports, like conversions and conversion rates.

Goals let you specify which pageviews, screen views or other hits should be used to calculate conversions. You can, for example, set up a Goal to track newsletter sign-ups. Each time a user completes a sign-up, a conversion is logged in your Google Analytics account. Using the conversion metrics, you can analyze whether or not you’re meeting your business objectives.

Common configuration settings: Channel Grouping and Content Grouping

Grouping is another way you can transform your data. With grouping, you can aggregate certain pieces of data together so you can analyze the collective performance. You can create two types of groups in Google Analytics: Channel groups and Content groups.

A Channel Group is a collection of common marketing activities. For example, Display Advertising, Social media, Email marketing, and Paid Search are four common channel groups that are each a roll-up of several marketing activities.

Content Groups are like Channel Groups, except you use them to create and analyze a collection of content. For example, if you’re an ecommerce business, you might want to group all of your product pages together, like t-shirts, jeans, and hats, into a group call Product Pages, and group all of your content pages, like blog posts, together in another group calledContent Pages. This would let you quickly see how well the Product Pages group and the Content Pages group each performed in aggregate.

Data aggregation

All of your configuration settings, including Filters, Goals, and Grouping, are applied to your data before it goes through aggregation, the final step of data processing.

During aggregation, Google Analytics creates and organizes your report dimensions into tables, called aggregate tables. Google Analytics pre-calculates your reporting metrics for each value of a dimension and stores them in the corresponding table. When you open a Google Analytics report, a query is sent to the aggregate tables that are full of this prepared data, and returns the specific dimensions and metrics for the report. Storing data in these tables makes it faster for your reports to access data when you request it.

Conclusion

Through these final stages of processing -- applying your configuration settings and creating aggregate tables -- Google Analytics transforms your raw hits into the meaningful data you see in your reports.

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