Google Analytics

Google Analytics

Using data to drive change

DIGITAL ANALYTICS FUNDAMENTALS

2.1 The Importance Of Digital Analytics

Measure -> Report -> Analyze -> Test

It is important to study the 'customer.'

It is important to determine the desired outcome.

Notes:

Digital analytics is the analysis of qualitative and quantitative data from your business and the competition to drive a continual improvement of the online experience that your customers and potential customers have which translates to your desired outcomes (both online and offline).

One of the most important steps of digital analytics is determining what your ultimate business objectives or outcomes are and how you expect to measure those outcomes. In the online world, there are five common business objectives:

• For ecommerce sites, an obvious objective is selling products or services.
• For lead generation sites, the goal is to collect user information for sales teams to connect with potential leads.
• For content publishers, the goal is to encourage engagement and frequent visitation.
• For online informational or support sites, helping users find the information they need at the right time is of primary importance.
• For branding, the main objective is to drive awareness, engagement and loyalty.

There are key actions on any website or mobile application that tie back to a business’ objectives. The actions can indicate an objective, like a purchase on an ecommerce site, has been fully met. These are “macro” conversions. Some of the actions on a site might also be behavioral indicators that a customer hasn’t fully reached your main objectives but is coming closer, like, in the ecommerce example, signing up to receive an email coupon or a new product notification. These are “micro” conversions. It’s important to measure both micro and macro conversions so that you are equipped with more behavioral data to understand what experiences help drive the right outcomes for your site.

2.2 Core Analysis Techniques - Segmentation and Context

Segmentation allows you to isolate and analyze subsets of your data. For example, you might segment your data by marketing channel so that you can see which channel is responsible for an increase in purchases. Drilling down to look at segments of your data helps you understand what caused a change to your aggregated data.

Examples:

• You can segment your data by date and time, to compare how users who visit your site on certain days of the week or certain hours of the day behave differently.
• You can segment your data by device to compare user performance on desktops, tablets and mobile phones.
• You can segment by marketing channel to compare the difference in performance for various marketing activities.
• You can segment by geography to determine which countries, regions or cities perform the best.
• And you can segment by customer characteristics, like repeat customers vs. first-time customers, to help you understand what drives users to become loyal customers.

2.3 Conversion and Conversion Attribution

A macro conversion occurs when someone completes an action that’s important to your business. For an ecommerce business, the most important macro conversion is usually a transaction. A micro conversion is also an important action, but it does not immediately contribute to your bottom line. It’s usually an indicator that a user is moving towards a macro conversion. It’s important to measure micro conversions because it helps you better understand where people are in on the journey to conversion.

Attribution is assigning credit for a conversion. In last-click attribution, all of the value associated with the conversion is assigned to the last marketing activity that generated the revenue. However, there are other attribution models that can help you better understand the value of each of your channels. For example, rather than assign all of the value to the last channel, you might want to assign all of the value to the first channel, the one that started the user on the customer journey. This is called first-click attribution. Or, you might assign a little bit of value to each of the assisting channels in the customer journey (linear attribution).

2.4 Measurement Plan

The measurement planning cycle consists of the following:

• Define your measurement plan.
1. Document your business objectives.
2. Identify the strategies and tactics to support the objectives.
3. Choose the metrics that will be the key performance indicators. (Revenue/ Clicks)
4. Decide how you’ll need to segment your data. (Marketing channel, new vs repeat)
5. Choose what your targets will be for your key performance indicators.

    Document Technical Infrastructure

• Create an implementation plan. After defining your business needs and documenting the technical environment of your business, create an implementation plan that is specific to the analytics tool that you’re using. For Google Analytics, this means defining the code snippets and specific product features that you’ll need in order to track the data defined in your measurement plan. 
• 
  
• Implement your plan.The next step is to have the web development team, or the mobile team, actually implement the tracking recommendations that you’ve made. Some website technologies will require additional planning, such as:
  • Query string parameters
  • Server redirects
  • Flash and AJAX events
  • Multiple domains and subdomains
  • Responsive web design
  The most common features used in an Analytics implementation plan for a website:
  • Implement the standard Analytics tracking snippet. This gives you the bulk of the data you need.
  • Determine how to track your KPIs. You can do this using goal tracking and the ecommerce module if you are an ecommerce business.
  • Use filters to normalize your data so that your reports are accurate and useful.
  • Use campaign tracking and AdWords linking to properly track marketing campaigns.
  • Use custom dashboards and custom reports to simplify the reporting process.
• Maintain and refine. The final step of the measurement planning cycle is to maintain and refine your plan. Your business requirements and your technical environment can change over time. Without a team to maintain your measurement plan, your data won’t keep pace with your reporting needs.

3.1 How Google Analytics Works

    

• Collection: You can use Analytics to collect user interaction data from websites, mobile apps, and digitally connected environments like kiosks or point of sale systems. For websites, Analytics uses JavaScript code to collect information. One package of information is referred to as a “hit” or an “interaction.” A “hit” is sent every time a user views a page tagged with Analytics.
  For mobile apps, you must add extra code to each "activity" you want to track. Note that since mobile devices are not always connected to the internet, data can not always be sent to the collection server in real time. To handle this situation, Analytics can store the “hits” and dispatch them to the server when the device reconnects to the internet. 
• Processing: Once the hits from a user have been collected on Google’s servers, the next step is data processing. This is the “transformation” step that turns your raw data to something useful. 
• Configuration: In this step, Analytics applies your configuration settings, such as filters, to the raw data. Once your data is processed, the data is stored in a database. Once the data has been processed and inserted into the database, it can’t be changed. 
• Reporting: Typically, you will use the web interface at www.google.com/analytics to access your data. However, it is also possibly to systematically retrieve data from your Analytics account using your own application code and the Core Reporting API.

3.2 Metrics and Dimensions

Reports in Analytics contain dimensions and metrics. Most commonly, you’ll see dimensions and metrics reported in a table, with the first column containing a list of the values for one particular dimension, and the rest of the columns displaying the corresponding metrics.

• Dimensions describe characteristics of your users, their sessions and actions.
• Metrics are the quantitative measurements of users,sessions and actions. Metrics are numerical data.

Add Event Tracking to AJAX/ Flash sites to avoid falsely inflated bounce rates

4.1 Google Account

An 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 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.

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. You should 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. Once you delete a view it’s gone forever. So having a backup view, like the unfiltered data view, is very useful.
• You should have a master view. This view should have all of the settings needed to transform your data into useful information.
• 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.

When you create a new view, Analytics does not automatically copy any of the historical data in the original view to the new view. You’ll only have data starting from the date you created the view.

4.2 Setting Up filters

You can use filters 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, 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.

For example, 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 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 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.

4.3 Goals

Setting up Goals in Analytics is one of the most important parts of implementation. Once you enable Goals, you get metrics like the number of conversions and the conversion rate. Goals are the way that we map the data in Analytics to the key performance indicators that you defined in your measurement plan. 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 Analytics. This is useful because the reports allow you to segment your conversion data.

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.

Goals are configured at the view level. That means you can create different Goals for each view. There are four types of Goals.

• A Destination Goal is a page on your website that users see when they complete an activity. For an account signup, 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.
• An Event 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.
• A Pages per Visit Goal is triggered when a user sees more or fewer pages than a threshold that you specify.
• A Duration Goal is triggered when a user’s visit exceeds or falls below a threshold that you set.

There is an important difference between Goal conversions and ecommerce transactions. 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 download 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.

As a best practice, you should only add a Goal value for nonecommerce Goals. The reason is that Goal value is cumulative. If you add a Goal value and you track transactions with the ecommerce tracking code, Analytics will add the value of the transaction to the value of the Goal.

4.4 Collecting Marketing Campaign Data

Link Tagging

Campaign Tags:

source : julynews

medium : email

campaign : nameofcampaign

term : optional

content : optional

5.1 Reports 

Annotations can be added to reports (days).

Analytics Reports:

Conversions Reports:

Goal Flow Report

Ecommerce Reports

Multi Channel Funnels Reports

Attribution Reports

GOOGLE ANALYTICS PLATFORM PRINCIPLES

1.2 Components

Collection

Let’s take a look at collection first. Collection is all about getting data into your Google Analytics account.

To collect data, you need to add Google Analytics code to your website, mobile app or other digital environment you want to measure. This tracking code provides a set of instructions to Google Analytics, telling it which user interactions it should pay attention to and which data it should collect. The way the data is collected depends on the environment you want to track.

For example, you’ll use the JavaScript tracking code to collect data from a website, but a Software Development Kit, called an SDK, to collect data from a mobile app.

Each time the tracking code is triggered by a user’s behavior, like when the user loads a page on a website or a screen in a mobile app, Google Analytics records that activity. First, the tracking code collects information about each activity, like the title of the page viewed. Then this data is packaged up in what we call a “hit”. Once the hit has been created it is sent to Google’s servers for the next step -- data processing.

A hit is transferred to the server via an image request (contains all data).

Collection from a Website

Adding the Google Analytics JavaScript code to your website

You simply add the standard code snippet before the closing </head> tag in the HTML of every web page you want to track. This snippet generates a pageview hit each time a page is loaded. It’s essential that you place the Google Analytics tracking code on every page of your site. If you don’t, you won’t get a complete picture of all the interactions that happen within a given website session.

When a user views a page on your site, the web browser begins to render the HTML on the page. It starts at the top of the page and moves towards the bottom. When it gets to your Google Analytics tracking code, the browser automatically triggers the JavaScript. Adding the code snippet to the top of the page, before the closing </head> tag, ensures that the Google Analytics code runs, even if a user navigates away from a page before it fully loads.

Functions of the web tracking code

The Google Analytics tracking code executes JavaScript asynchronously, meaning that the JavaScript runs in the background while the browser performs other tasks. This is very important -- it means that the Google Analytics tracking code will continue to collect data while the browser renders the rest of the web page.

As the tracking code executes, Google Analytics creates anonymous, unique identifiers to distinguish between users. There are different ways an identifier can be created. By default, the Google Analytics JavaScript uses a first-party cookie, but you can also create and use your own identifier.

When a page loads, the JavaScript collects information from the website itself, like the URL of the current page. The JavaScript also collects information from the browser, such as the user’s language preference, the browser name, and the device and operating system being used to access the site. All of this information is packaged up and sent to Google’s servers as a pageview hit. This process repeats each time a page is loaded in the browser.

 From an Android Device

Using the Google Analytics mobile SDKs

Instead of using JavaScript to collect data like you do on a website, youll use an SDK, or Software Development Kit, to collect data from your mobile app. There are different SDKs for different operating systems, including Android and iOS.

SDKs collect data about your app, like what users look at, the device operating system, and how often a user opens the app. This data gets packaged as hits, and sent to your Google Analytics account. This is similar to how the JavaScript code sends hits from a website.

Dispatching

Data from mobile apps is not sent to Analytics right away. When a user navigates through an app, the Google Analytics SDK stores the hits locally on the device and then sends them to your Google Analytics account later in a batch process called dispatching.

Mobile data collection uses dispatching for two reasons:

• First, mobile devices can lose network connectivity, and when a device isn’t connected to the web, the SDK can’t send any data hits to Google Analytics.
• Second, sending data to Google Analytics in real time can reduce a device’s battery life.

For these reasons, the SDKs automatically dispatch hits every 30 minutes for Android devices and every two minutes for iOS devices, but you can customize this time frame in your tracking code to control the impact on the battery life.

Differentiating users on mobile

Another important function of the mobile SDK is differentiating users. When an app launches for the first time the Google Analytics SDK generates an anonymous unique identifier for the device, similar to the way the website tracking code does. Each unique identifier is also counted in Google Analytics as a unique user.

If the app gets updated to a new version, the identifier on the device remains the same. However, if the app gets uninstalled, the Google Analytics SDK deletes the identifier. If the app is then reinstalled, a new anonymous identifier is created on the device. The result is that the user will be identified as a new user, not a returning  user, but no other data in your Google Analytics reports will be impacted.

Collecting and sending data with the Measurement Protocol

The Measurement Protocol lets you send data to Google Analytics from any web-connected device. Recall that the Google Analytics JavaScript and mobile SDKs automatically build hits to send data to Google Analytics. However, when you want to collect data from a different device, you must manually build the data collection hits. The Measurement Protocol defines how to construct the hits and how to send them to Google Analytics.

For instance, let’s say we want to collect data from a kiosk. Here’s a sample hit that will track when a user views a screen on the kiosk:

http://www.google-analytics.com/collect?v=1&tid=UA-XXXX-Y&cid=555&sr=800x600&t=pageview&dh=mydemo.com&dp=/home&dt=homepage

Notice there is a parameter in the hit that contains the screen resolution. This particular parameter will become the dimension Screen Resolution during processing. The  value in the parameter will end up in the Screen Resolutions report.

Like the JavaScript and mobile SDKs which include a tracking ID with each hit, you must also add a tracking ID to every hit you send to Google Analytics. This will ensure that the data appears in your specific Analytics account and property. All of the parameters that you can include in the hit are explained in the Analytics Developer documentation.

Processing & Configuration

During data processing, Google Analytics transforms the raw data from collection using the settings in your Google Analytics account. These settings, also known as the configuration, help you align the data more closely with your measurement plan and business objectives.  

For example, you could set up something called a Filter that tells Google Analytics to remove any data from your own employees. During processing Google Analytics would then filter out all of the hits from your employees, so that this data wouldn’t be used for your report calculations.

You can also configure Google Analytics to import data directly into your reports from other Google products, like Google AdWords, Google AdSense and Google Webmaster Tools. You can even configure Google Analytics to import data from non-Google sources, like your own internal data. It’s during the processing stage that Google Analytics then merges all of these data sources to create the reports you eventually see in your account.

It’s important to note that once your data has been processed, it can not be changed. For example, if you set a filter to exclude data from your employees, that data will be permanently removed from your reports and can’t be recovered at a later date.

• 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.

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.

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.

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 called Content 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 

Reporting

After Google Analytics has finished processing, you can access and analyze your data using the reporting interface, which includes easy-to-use reporting tools and data visualizations. It’s also possible to systematically access your data using the Google Analytics Core Reporting API. Using the API you can build your own reporting tools or extract your data directly into third-party reporting tools.

Dimensions in Google Analytics

A dimension describes characteristics of your data. For example, a dimension of a session is the traffic source that brought the user to your site. And a dimension of an interaction a user takes on your site could be the name of the page they viewed.

Metrics in Google Analytics

Metrics are the quantitative measurements of your data. They count how often things happen, like the total number of users on a website or app. Metrics can also be averages, like the average number of pages users see during a session on your website.

Combining dimensions and metrics in reports

Dimensions and metrics are used in combination with one another. The values of dimensions and metrics and the relationships between those values is what creates meaning in your reports.

Most commonly, you’ll see dimensions and metrics reported in a table, with the first column containing the values for one particular dimension, and the rest of the columns displaying the corresponding metrics.

However, not every metric can be combined with every dimension. Each dimension and metric has a scope that aligns with a level of the analytics data hierarchy -- user-, session-, or hit-level. In most cases, it only makes sense to combine dimensions and metrics in your reports that belong to the same scope.

For example, the count of Visits is a session-based metric so it can only be used with session-level dimensions like traffic Source or geographic location. It would not be logical to combine the count of visits metric with a hit-level dimension like Page Title.

Here’s another example: the metric Time on Page is a hit-level metric. It measures how long users spend on a page of your site. It is not possible to use this metric with a session-level dimension like traffic Source or geographic location. In this case you would need to use a session-based time metric, like Average Visit Duration

Using the Google Analytics reporting APIs

For example, you can use the APIs to integrate your own business data with Google Analytics and build custom dashboards.

To use the reporting APIs, you have to build your own application. This application needs to be able to write and send a query to the reporting API. The API uses the query to retrieve data from the aggregate tables, and then sends a response back to your application containing the data that was requested.

Each query sent to the API must contain specific information, including the ID of the view that you would like to retrieve data from, the start and end dates for the report, and the dimensions and metrics you want. Within the query you can also specify how to filter, segment and order the data just like you can with tools in the online reporting interface.

You can think of the data that gets returned from the API as a table with a header and a list of rows. The header describes the name and data type of each column -- these are either the dimension or metric names.

1.3 Data Model

Users

Sessions

Interactions