Get social, mobile, and 40+ new data points with the Google Analytics API

Google Analytics Core Reporting APIs enable a powerful and flexible way to analyze, report on, and ultimately optimize such things as web and mobile experiences, conversions, and sales.

Today we’re adding over 40 new metrics and dimensions that can be queried through the Core Reporting API. This enables developers to create reports that are similar to what is available in the Google Analytics web interface for important areas such as social and mobile. See a full list of additions on the Core Reporting API changelog.

Here’s a rundown of what’s new and a few helpful questions the data can answer.

Social Data
Now you can get data for both on-site interactions with social buttons as well as off-site social data from social data hub partner networks.

• Which content receives the most comments on social networks?
• What are the conversion rates for visits from social networks?
• For social buttons embedded in my property, which ones get used most often and for which content?

Mobile Devices
For mobile visits to your site, get all the good stuff like like brand, model, and input type.

• What are the top 10 mobile devices used to visit my website?

Geo
We added a new dimension to indicate the Designated Market Area (DMA) where traffic came from.

• What are the Top 10 Campaigns for the San Francisco Bay Area DMA?

Page Path Rollups
Create your own drill down reports with these new dimensions that allow you to roll-up metrics to hierarchical levels of your property.

• What does the performance of my website look like across different sections?

App & Exception Tracking
If you’re using the Google Analytics SDK for iOS/Android v2 beta, you can now retrieve App View and Exception metrics.

• On a daily basis, how many views did my mobile app get?
• On a daily basis, how many exceptions occurred in my mobile app, and how many exceptions were fatal? 

User Timings
New ways to report on all things related to user timing data.

• What were the time measurements for all user defined timings?

Related Resources:

• Dimensions & Metrics Reference for a list of all available metrics & dimensions. 
• Google Analytics Query Explorer to help you build queries and try out the Core Reporting API.
• Core Reporting API changelog for a list of the 40+ new dimensions & metrics.

Posted by Pete Frisella and Nick Mihailovski, Google Analytics API team

Combining a User Problem with a Desire to Learn: the Story of Quicklytics

This article is part of our Developer Spotlight Series that promotes new tools and applications built using the Google Analytics Developer platform. To see other tools, check out our App Gallery. Interested in having us showcase your story? Let us know what you’re working on!

Eduardo Scoz is a software architect and self-proclaimed, “analytics addict.” In early 2010, he grew frustrated with his daily routine of checking in on his web analytics from several sites and personal blogs. Very quickly he found himself spending an overwhelming amount of time monitoring his key metrics from across his own content kingdom: he yearned for a way to keep an eye on his KPI’s without having it feel like a full-time job.

Eduardo was determined to find an iPhone application that gave him a high-level view of all of his sites in way that was easy to digest. After a few days of searching he realized that the only way for him to get exactly what he wanted was to build it himself. He had never built an iPhone application but his “learn by doing” mentality prevailed: after a few weeks of prototyping, he had come up with something he was proud of. He showed it to a few friends and gauging their reaction, he realized he might be onto something. He incorporated their feedback, finished building it out and decided to release it publicly. In February 2010, Quicklytics was born.

Quicklytics allows users to rapidly check the status of multiple websites in a matter of seconds and visually understand how their site is performing for both current and historical timeframes. It has full support for both iPhone and iPad as well as custom filtering that allows for quick deep dives into areas of interest. While its primary views focus on top-level metrics, Quicklytics also provides detailed reports with most of the data also available through Google Analytics.

“All apps were about either showing as much data as possible, or focusing on less-useful stuff, like browsers and screen sizes, which are only really necessary when you’re doing deep analysis, not when ‘checking the weather’,” says Eduardo.

As soon as Quicklytics hit the App Store, it spread like wildfire. In the 2 ½ years since it was released, Quicklytics has received over 40,000 downloads - most of which were paid. This has translated into a significant source of side revenue for Eduardo’s business that has allowed him to continue building new features for Quicklytics while looking for new projects to learn from.  Now, Eduardo finds great joy in using Quicklytics to measure the mobile app analytics on - you guessed it - Quicklytics.

Quicklytics leverages the Analytics Core Reporting APIs Objective-C library and OAuth 2.0 for user authentication. Although this was Eduardo’s first experience with the Analytics APIs and Objective-C, he was able to take full advantage of the Developer Forums for support: “In the few cases I found issues with the tool, Google developers were actually very helpful and fixed some issues from their side. It was a great experience.”

Armed with a clear user problem and a willingness to learn, Eduardo was able to turn one of his biggest pain points into a viable side business and a solution that is enjoyed by many. According to Eduardo, “It’s great to know that a lot of people find it as useful as I do.”

To learn more about Quicklytics, check out his App Store listing.

Posted by John Milinovich, Google Analytics API team

Automate Google Analytics Reporting using Google Apps Script

Many people have been asking for a simple way to put Google Analytics data into a Google Spreadsheet. Once the data is inside a Google Spreadsheet, users can easily manipulate Google Analytics data, create new visualizations, and build internal dashboards.

So today we released a new integration that dramatically reduces the work required to put Google Analytics data into any Apps Script supported product, such as Google Docs, Sites, or Spreadsheets.

Here’s an example of Google Analytics data accessed through Apps Script and displayed in a Google Spreadsheet.

Custom API Dashboards - No Code Required

We know that a popular use case of this integration will be to create dashboards that automatically update. To make this easy to do, we’ve added a script to the Spreadsheets script gallery that handles all this work - no code required. The script is called Google Analytics Report Automation (Magic).

This script is a great template for starting your own project, and we’ve had many internal Google teams save hours of time using this tool. Here’s a video demoing how to build a dashboard using this script:

You can find this script by opening or creating a Google Spreadsheet, clicking Tools -> Script Gallery and searching for “analytics magic”.

Writing Your Own Script

Of course many developers will want to write their own code. With the new Analytics – Apps Script integration, you can request the total visitors, visits, and pageviews over time and put this data into a spreadsheet with just the following code:

// Get Data.
var results = Analytics.Data.Ga.get(
    tableId,
    startDate,
    endDate,
    'ga:visitors,ga:visits,ga:pageviews',
    {‘dimensions’: ‘ga:date’});

// Output to spreadsheet.
var sheet = SpreadsheetApp.getActiveSpreadsheet().insertSheet();
sheet.getRange(2, 1, results.getRows().length, headerNames.length)
    .setValues(results.getRows());

// Make Sandwich.

To get started now, read our Automated Access to Google Analytics Data in Google Spreadsheets tutorial. Also check out the Google Analytics Apps Script reference docs.

Solving Business Problems

Are you ready to start building solutions using Google Analytics and Google Apps Script?

We’d love to hear new ways you use this integration to help manipulate, visualize and present data to solve business problems. To encourage you to try out this integration, we are giving out Google Analytics developer t-shirts to the first 15 developers to build a solution using both APIs.

To be eligible, you must publish your solution to either the Chrome Web Store or the Spreadsheets Script Gallery and include a description of a business problem the script solves. We’ll then collect these scripts and highlight the solutions in an upcoming blog post. After you publish your script, fill out this form to share what you’ve built.

We’re looking forward to seeing what you can do with this integration.

Posted by Nick Mihailovski   profile Nick is a Senior Developer Programs Engineer working on the Google Analytics API. In his spare time he likes to travel around the world.

Introducing the Multi-Channel Funnels Reporting API

Measuring how marketing efforts influence conversions can be difficult, especially when your customers interact with multiple marketing channels over time before converting. Last fall, we launched Multi-Channel Funnels in Google Analytics, a new set of reports that help shed light on the full path users follow to conversion, rather than just the last click. One request we’ve had since the beginning was to make this data available via an API to allow developers to extend and automate use cases with the data. So today we’re releasing the new Google Analytics Multi-Channel Funnels Reporting API.

The API allows you to query for metrics like Assisted Conversions, First Interactions Conversions, and Last Interaction conversions, as well as Top Paths, Path Length and Time Lag, to incorporate conversion path data into your applications. Key use cases we’ve seen so far involve combining this conversion path data with other data sources, such as cost data, creating new visualizations, as well as using this data to automate processes such as bidding.

For example, Cardinal Path used the new Multi-Channel Funnels API, Analytics Canvas ETL (Extract, Transform, Load) and Tableau Software to help their client, C3 Presents, uncover how time and channels affected Lollapalooza ticket sales in an analysis dubbed “MCF DNA.” The outcome was a new visualization, similar to a DNA graph, that helped shed light on how channels appeared throughout the conversion funnel.

MCF DNA Visualization in Tableu Software

In another case, Mazeberry, an analytics company from France, helped their client 123Fleurs decrease customer acquisition costs by 20% by integrating data from the Multi-Channel Funnels API into a new reporting framework. Their application, Mazeberry Express, combines media cost and full conversion path data to provide new Cost Per Acquisition (CPA) and Return on Investment (ROI) metrics that provide a more complete understanding of how online channels are working together to influence conversions.

Mazeberry Express Screenshot - Focus on a Channel

Please note that this functionality only works with the new v3.0 API libraries, so you should upgrade now if you haven’t already (see our migration guide). We look forward to seeing how you make use of this new data source.


Posted by John Huang, Software Engineer

Upgrade now to the new Core Reporting API

Core Reporting API Migration Update
Back in December we launched the Core Reporting API to replace the Data Export API. We also announced that we would be shutting down the old Data Export API and that all applications should migrate to the new version.

The time has come for us to shut down the old version. So this is our last reminder to migrate to the new Core Reporting API.

Starting next week, we’ll begin redirecting a portion of Data Export API requests to the Core Reporting API as we prepare to shut down the Data Export API on July 10th. So you'll begin to see Data Feed requests return a Core Reporting API response, and requests for the Account Feed will produce an error.

If you do not migrate, your application will experience service outages.

For more information, visit:
Reminder: Migrate to the new Core Reporting API
Migration Guide: Moving from v2.3 APIs to v2.4 & v3.0



New Guides To Get You Started Fast
It’s important for the Google Analytics APIs to be open and accessible to all developers. It’s common practice for developers learning a new API to start off with the basics and incrementally build from this foundation.

So with that in mind, we wrote a new Hello Analytics API tutorial to give you that basic foundation. The tutorial includes sample code for Java, PHP, Python, and JavaScript. It also walks you through the basic steps of using the Google Analytics API, including registration, authorizing users, retrieving account and profile information, and querying for a report. Once complete you will have a working example that you can customize.

To make it even easier to build applications, we’ve also updated the developer guides for both the Core Reporting API and Management API. Examples for a variety of programming languages have been included, but more importantly the basic concepts have been highlighted.

So whether you’re just starting, updating, or migrating to the new version, you should check out the Hello Analytics API tutorial and Developer Guides before settling down to write that awesome application.


Posted by Pete Frisella, Nick Mihailovski, and Jeetendra Soneja, Analytics API team

Reminder: Migrate to the new Core Reporting API

At the end of 2011 we announced the Google Analytics Core Reporting API as a replacement for the Data Export API. We also announced a 6 month deprecation period for the Data Export API version 2.3, after which all v2.3 queries will return a v2.4 response. Well, it's almost been 6 months since the announcement was made. If you haven't already moved to our shiny new APIs, and we know there are quite a few of you out there who haven't, we urge you to get movin' or risk your application not working come June.

The good news is that we published a new, easy to follow migration guide to help you make the transition and ensure your application continues to work after we shut down the Data Export API sometime in June.

If you are building a new application, we highly recommend using the Core Reporting API v3.0. For existing applications, we also recommend moving to v3.0 but it may be easier for you to migrate to v2.4 as an intermediary step, since it is backwards compatible with the Data Export API v2.3.

The great news is that if you make the move to v3.0, you'll be able to take advantage of any new features, and the compact JSON format that reduces response size by 10x!

To get started, check out the Migration Guide: Moving from v2.3 APIs to v2.4 & v3.0.

Additional details and support:

• Core Reporting API Forum - for any questions you may have
• Core Reporting API Changelog - for details on the change

Posted by Pete Frisella, Google Analytics API team

New Google Analytics Easy Dashboard Library

Many developers save time by using the Google Analytics API to automate Analytics reporting tasks. For example, you can use the API to create a dashboard to report data across multiple profiles. The Google Analytics App Gallery includes many 3rd party solutions that do this.

What if you want to build something quickly that’s custom-tailored  to your business? You would typically have to spend time learning the API, figuring out how to handle authorization, then deciding how to integrate this data with a visualization library. You could build a custom solution, but it took a lot of effort – until now, thanks to the Google Analytics Easy Dashboard Library.

Four months ago we started a project with a team of University of California Irvine students to simplify all of these steps. As part of this project, together we built the Google Analytics Easy Dashboard Library. This library makes it easy to use the Google Analytics API by distilling the process into three easy steps:

1. Register with Google APIs Console.
2. Copy and paste the JavaScript code.
3. Configure this code to query your data and choose a chart type to visualize it.

So now you can create custom Google Analytics dashboards very quickly, with minimal code.

Here’s a quick example. Say you want to create a line chart plotting visitors and visits for the last 30 days. Besides including the library, the only code required is:

<div id=”chart1”></div>
<script>
var chart1 = new gadash.Chart({
  'type': 'LineChart',
  'divContainer': 'chart1',
  'last-n-days':30,
  'query': {
    'ids': TABLE_ID,
    'metrics': 'ga:visitors,ga:visits,ga:pageviews',
    'dimensions': 'ga:date',
    'sort': 'ga:date'
  },
  'chartOptions': {
    hAxis: {title:'Date'},
    vAxis: {title:'Visits'},
  }
}).render();
</script>

Using the code above will create this chart.



It’s that easy! To find out more about using the Easy Dashboard Library, read our Getting Started guide.

While the current library is very useful, we think we can add more features and make it even easier to use. To reach this goal, we’ve started working with another group of UC Irvine students, this time for three academic quarters. This new project’s main goal will be to further simplify the library. We want the students we’re working with to engage with you and implement your feature requests, if possible. If you use this library, we'd love to hear how you think it can be improved. Feel free to send any feedback to through our new GA-easy-dash-feedback google group.

We hope this library saves you time and helps you get more out of Google Analytics.

Posted by,
Jeetendra Soneja and Nick Mihailovski, Google Analytics API Team

More ways to measure your website's performance with User Timings

As part of our mission to make the web faster, Google Analytics provides Site Speed reports to analyze your site’s page load times. To help you measure and diagnose the speed of your pages in a finer grain, we’re happy to extend the collection of Site Speed reports in Google Analytics with User Timings.

With User Timings, you can track and visualize user defined custom timings about websites. The report shows the execution speed or load time of any discrete hit, event, or user interaction that you want to track. This can include measuring how quickly specific images and/or resources load, how long it takes for your site to respond to specific button clicks, timings for AJAX actions before and after onLoad event, etc. User timings will not alter your pageview count, hence,  makes it the preferred method for tracking a variety of timings for actions in your site.

To collect User Timings data, you'll need to add JavaScript timing code to the interactions you want to track using the new _trackTiming API included in ga.js (version 5.2.6+) for reporting custom timings. This API allows you to track timings of visitor actions that don't correspond directly to pageviews (like Event Tracking).  User timings are defined using a set of Categories, Variables, and optional Labels for better organization. You can create various categories and track several timings for each of these categories. Please refer to the developers guide for more details about the _trackTiming API.

Here are some sample use cases for User Timings

• To track timings for AJAX actions before and after onLoad event. 
• A site can have their own definition of “User Perceived Load Time”, which can be recorded and tracked with user timings.  As an example, news websites can record time for showing the above fold content as their main metric instead of onLoad time. 
• Detailed performance measurement and optimization of sub components on a page, such as time to load all images, CSS or Javascript, download PDF files and time it takes to upload a file.

Want to check out User Timings Report in your account?
Go to the content section and click the User Timings report under Content section. There are three tabs within the User Timings report for you to review: Explorer, Performance, & Map Overlay. Each provides a slightly different view of user timings reported.

The Explorer tab on the User Timings report shows the following metrics by Timing Category, Timing Variable, or Timing Label (all of which you define in your timing code).

• Avg. User Timing—the average amount of time (in seconds) it takes for the timed code to execute
• User Timing Sample—the number of samples taken

The Explorer tab also provides controls that you can use to change the tabular data. For example, you can choose a secondary dimension—such as browser— to get an idea of how speed changes by browser.

To learn more about which timings are most common for user timings, switch to the Performance tab. This tab shows timing buckets, providing you with more insight into how speed can vary for user reported timings for selected category, variable and label combinations. You may switch to Performance tab at any point of navigation in the Explorer tab, such as after drilling down on a specific category and variable, to visualize distribution of user reported timings.  The bucket boundaries for histograms in Performance Tab are chosen to be flexible so that users can analyze data at low values ranging from 10 milliseconds granularity to 1 minute granularity with addition of sub-bucketing for further analysis.

The Map Overlay tab provides a view of your site speed experienced by users in different geographical regions (cities, countries, continents).

-  Satish Kambala & Mustafa M. Tikir, Google Analytics team

Measuring app engagement across device & platforms

There are more ways now to consume your favorite television shows, movies, and on demand content than ever before. People are turning to their smartphones, tablets, and Internet connected TV’s to watch what they want, when they want it. For broadcasters, agencies, and advertisers the question is how are users engaging with this media and how can it be monetized?

This measurement opportunity is what drove TV App Agency to be founded in 2011. The London-based software company designed a software application that works across a variety of viewing devices to help deliver on-demand media. They turned to Google Analytics as the platform to help them measure and analyze their data.

Why turn to Google Analytics?
TV App Agency opted to use Google Analytics’ server side APIs, which were more easily compatible with the on-demand media environment than JavaScript APIs. They were able to use their own in-house knowledge from previous mobile development to come up with a tagging strategy that highlighted exactly the data that mattered most to their business model. Learn more by reading the full case study.



“We are now able to track which adverts are being played and get an idea of which functions in apps are being used. Plus, the real-time reports show when people are actually using these apps.”
                          Bruno Pereira, co-founder of TV App Agency

Future Analytics goals

TV App Agency is working on expanding their Analytics to track more events, and understand more about viewer engagement from Analytics robust reports. By integrating Google Analytics they are able to offer richer data and analysis than other connected TV app developers, which gives them an incredible advantage in this exciting new space.

- The Google Analytics team

Sharing Personalized Dashboards using the Analytics API

Web agencies often rely on Excel and Word to create analytics reports for clients. It’s a manual process that involves a lot of copy and pasting. Yet an agency’s main value-add isn’t report creation, but analyzing data and providing key findings and recommendations to clients. And while Google Analytics provides the tools to slice and dice the data, many web agencies also want to present clients with personalized reports, complete with the agency’s logo. And they want to be able to deliver and share reports without requiring users to log in, especially in large organizations where report distribution can become an onerous administrative process.

DashThis addresses these challenges with dashboards that combine simple automatic reporting with accessibility. Agencies spend less time creating reports and more time analyzing. Using the Google Analytics API, DashThis imports the client’s data and updates a set of dashboards with Key Performance Indicators (KPIs) for a specific job function or industry. The agency can also request a set of custom dashboards that meet exact specifications and requirements.

Alerts and warnings can be set to notify managers of changes in KPIs via email. All this is accomplished securely and without requiring the user to log in. There is also a white-label option for additional branding requirements demanded by agencies.

According to Kari Harju, CEO of SalesLion, an SEO and conversion agency in Helsinki, Finland. “Customers do not always understand how to read the results from web analytics products and see a tangible return on their investments. It's hard to show them without time consuming meetings. SalesLion opted for a custom dashboard to meet the needs of their clients. As a result of using DashThis, our clients now have a simple and easy way to understand what’s going on with their web properties as it relates to their KPIs”. SalesLion eliminated most of the reporting work, leaving more time to analyze, highlight key findings, and make actionable recommendations to clients.


DashThis was built by Trimali Technologies and uses the Google Analytics API. Stéphane Guérin, CEO describes DashThis’ experience with the Google Analytics API and the response from customers, “The API is really simple to use but extremely powerful. It allows developers to add even more value on a great tool such as Google Analytics. We’ve been able to develop strong business relationships with agencies and we’re proud to have made a tool that is useful for professionals. By opening the platform, Google Analytics allows smaller companies like ours to flourish in a rich eco-system.”


DashThis can be found in the Google Analytics App Gallery and on the DashThis website.
If you’re interested in developing solutions for the Google Analytics platform, visit Google Analytics Developers.


Posted by Pete Frisella, Google Analytics API Team

PBS saves time with automated reports

For most companies using Google Analytics, reporting on website traffic and performance for a few web properties is a straightforward task. However, if your company manages hundreds of web properties, delivering useful and timely reports can become a significant challenge. For many, the only apparent solution is to manually export analytics data for each web property, then combine and compare that data to answer relevant business questions. It’s a slow and costly process and you spend most of your time creating reports instead of carrying out meaningful analysis.

The Public Broadcasting Service (PBS) faced precisely this challenge when it made the decision to use GA Data Grabber by AutomateAnalytics.com. GA Data Grabber works within Excel and uses the Google Analytics API. Users create or choose reports and GA Data Grabber automatically retrieves the Google Analytics data from any number of websites. And with multi-login capabilities, users can seamlessly combine data between Google Analytics profiles that reside under different Google Accounts.

Designed for non-technical users, GA Data Grabber generates great-looking visualizations and can automatically highlight important changes in key metrics over a date range. It’s also possible to use Excel’s visualization and data processing features. For example, formulas can be added to calculate Key Performance Indicators (KPIs) based on any set of metrics.




Amy Sample, Director, Web Analytics, Public Broadcasting Service explains the challenges that PBS faced and how GA Data Grabber was able to help. “The PBS.org and PBSKIDS.org web sites are made up of hundreds of individual companion sites to broadcast programs.  From a business perspective, there is a need to evaluate performance of individual program sites relative to each other.” As is common for many large organizations, PBS has separate Google Analytics accounts for each program site. “While multiple accounts works well to evaluate the site content and performance, it makes it difficult to look at all of the sites side-by-side without a lot of manual effort.  Our previous attempts to create this type of report were time-consuming and often subject to data input errors.”

“Using Google Analytics, combined with GA Data Grabber, we were able to create a benchmark report for our program sites. The monthly report pulls a standard set of KPIs from each of the program accounts and ranks the programs by traffic. The report is used as a management tool by both the PBS.org and PBSKIDS.org teams to monitor monthly performance of programs. The teams have also used it to identify opportunities for programs that are no longer being broadcast but still getting significant online traffic.  Our program producers use the report to benchmark their performance against other sites of similar content or size and determine ways to improve audience engagement. As a result of using GA Data Grabber to pull the data, we can produce this report quickly and accurately on monthly basis.”

GA Data Grabber
Mikael Thuneberg, Founder & CEO of AutomateAnalytics.com has been using the Google Analytics API since its launch. “I’ve been very happy with the API. Having developed for several other APIs, I can say that the Google Analytics API is by far the easiest to develop for. It’s logically structured and flexible, the documentation is excellent, and it’s easy to get help through the forum. I’ll certainly continue developing for the Google Analytics API. I’ve expanded to other APIs as well, but Google Analytics is still by far the most important one for my business.”

GA Data Grabber can be found through the Google Analytics App Gallery and can be downloaded from the GA Data Grabber website.

If you’re interested in developing solutions for the Google Analytics platform, visit Google Analytics Developer Program.

Posted by Pete Frisella, Google Analytics API Team

The power of visualization with the Google Analytics API and Google Earth

Does your organization have several websites, each serving a particular geographic region? If so you know how challenging it is to analyze the data across these regions in a meaningful way.

Visualizations can help, but they can be difficult to design. Newland communities, a developer of residential and urban home communities, manages numerous web properties for each community and is no stranger to these challenges. To address them, Newland used the query tool from ShufflePoint. The tool enabled the combination of data from Google Analytics and Google Earth, allowing Newland to visualize the data in new ways.

ShufflePoint implemented a pilot project after discussing the idea with Chief Ingredient and their client Newland Communities. Their goal: deal with some of the problems associated with clarifying large amounts of data in a visually appealing manner. The outcome of the project was an integration of Google Analytics data with Google Earth.

Using the Google Analytics API, the ShufflePoint query tool extracts metrics by location from Google Analytics for multiple Newland Communities web properties and creates static and time-animated geographic representations (using KML) viewable in Google Earth. The mashup provides advanced visual reporting on location based campaigns, showing their effect on pageviews, and highlighting any anomalies requiring further investigation. Additionally, the visualization is a great fit for promotional videos, or digital signage needs.



“ShufflePoint uses almost every feature and capability of the Google Analytics API. The API has all of the characteristics that a developer could hope for, including great performance, correct semantics, OAuth for authentication, and good community support. The Google Earth based application has given ShufflePoint recognition for doing innovative and challenging things with Google Analytics. This has been beneficial for promoting ShufflePoint’s offerings.” Chris Harrington, CTO

The ShufflePoint application can be found through the Google Analytics App Gallery and from the ShufflePoint website.

If you’re interested in developing solutions for the Google Analytics platform, visit Google Analytics Developer Program.

Posted by Pete Frisella, Google Analytics API Team

Introducing the Google Analytics Core Reporting API

Today we are announcing the new Google Analytics Core Reporting API as a replacement for the Data Export API. This is the second phase in a larger project we started a couple months back to upgrade our APIs to new infrastructure.

The Core Reporting API has two versions.

Version 3.0 is a brand new API, with a 10x reduction in output size and support for many new client libraries, like PHP, Ruby, Python, JavaScript and Java. All new features will only be added to this version.

Version 2.4 is backward compatible with the legacy Data Export Version 2.3.

If you are building a new application or maintaining an existing one, we highly recommend migrating to version 3.0.

One of the biggest changes in switching to the Core Reporting API is that you now need to register your applications via the Google APIs Console and use a project ID to access the API.

With this change, we are also announcing the deprecation of the Data Export API version 2.3. This API will continue to work for 6 months, after which all v2.3 XML requests will return a v2.4 response. Also, we plan to terminate the Data Export API Account Feed. All configuration data should be retrieved through the Google Analytics Management API.

See our Data Export API changelog for all the details of the change and read our developer documentation for more details about each API.

If you have any questions feel free to reach out in our Data Export API Google group.

Thanks,
Jeetendra Soneja and Nick Mihailovski, Google Analytics API Team

Simplifying eCommerce reporting across international sites with multiple currencies

It’s quite common for organizations to have complex reporting requirements that combine multiple data sources. In these cases, an efficient way to get at Google Analytics data is to use the API. But for those without the development resources needed to access the API there are many Google Analytics Apps available that can offer a solution. The following mini case study is about Analytics Canvas, a 3rd party Google Analytics App. Analytics Canvas helped Fairmont Raffles Hotels International take a complex reporting task and make it easy.

Like most global organizations, Fairmont Raffles Hotels International has regionally operated business units. Each unit typically maintains its own reporting profile in Google Analytics. But with individual business unit profiles, how do you analyze the data across all the business units? And, how do you combine external business data with each of these Google Analytics profiles?

For Fairmont Raffles Hotels International, the answers lie in the Analytics Canvas tool from nModal. Analytics Canvas is an easy to use, drag-n-drop interface for creating data queries and transformations needed for advanced reports and dashboards. Analytics Canvas can extract data from multiple Google Analytics accounts, combine it with additional data sources, and then segment and filter the data.

Barbara Pezzi, Director Analytics & Search Optimization, Fairmont Raffles Hotels International, uses Analytics Canvas to automate her complex reporting tasks. “We trade in over 10 different currencies on our websites. We needed to have multiple international hotels’ eCommerce data converted to a common currency in various reports, such as ‘All Traffic Sources’ and ‘Campaigns’, to analyze the overall production of each traffic channel and marketing initiative. Traditionally we would have to export data from multiple profiles and do currency conversion by hand in spreadsheets. With almost 100 hotels, the large number of profiles made it a very time consuming and potentially error prone task to get a multi-hotel, single currency report.” With Analytics Canvas, Fairmont automated this task in minutes with a single currency report for all its hotels.

Analytics Canvas was built by nModal Solutions Inc using the Google Analytics API. Here is what James Standen, the founder of nModal, has to say “The Google Analytics API is very easy to use and the full access to data makes it possible to make a very powerful query interface. So far we’ve had a great response to Analytics Canvas. People are very pleased to have greater access to their web traffic data, and we’re thrilled to hear about the interesting new applications and integrations our customers are able to do using our tool.”

The Analytics Canvas application can be found through the Google Analytics App Gallery and can be downloaded from the Analytics Canvas website.

If you’re interested in developing applications for the Google Analytics platform visit Google Analytics for Developers.

Posted by Pete Frisella, Google Analytics API team

BCIT increases visitor satisfaction with 4Q Suite and the Google Analytics API

One of the great aspects of being part of the Developer Relations team for Google Analytics is that I get to work with a lot of awesome partners that build cool and successful apps using the Google Analytics API. We've decided to share these successes as a series of mini case studies highlighting a variety of Google Analytics Apps. And to start off with we have iPerceptions’ 4Q Suite.

Objective: Improve the visitor experience

British Columbia Institute of Technology wanted their website to be both functional and satisfying. But, behavioral data alone wasn’t telling them what key audiences thought about the site. BCIT knew what visitors were doing on the site but wanted to learn more about why they behave the way they do. The overall objective for BCIT was to gain a better understanding of which content and processes were most effective for various audiences.

The Solution: 4Q Suite and the Google Analytics API

To meet this objective, BCIT chose 4Q Suite, an online survey tool built by iPerceptions. 4Q uses the Google Analytics API to link 4Q Suite survey responses with the corresponding Google Analytics session data. An analyst can then use this data to answer questions related to visitor intention and satisfaction. 4Q tracks six “Voice of Customer” events within Google Analytics. These events are related to survey completion, task completion, purpose of visit, and overall satisfaction. With 4Q survey data available in Google Analytics, marketers can better prioritize site enhancements, monitor the effectiveness of ad campaigns and marketing events more closely, and quickly identify changes in conversion. The Google Analytics API also makes it possible for 4Q to export GA data into the 4Q Suite dashboard, enabling analysis of the integrated dataset and open-ended feedback. And, users can view or receive automated alerts of significant changes based on the combination of 4Q Suite and Google Analytics data.

Result: Increased visitor satisfaction

Alan Etkin, Project and Web Analytics Manager at British Columbia Institute of Technology uses Google Analytics and 4Q Suite to segment site visitors by key audiences (students, prospective students, and faculty & staff), and see the differences in task completion and satisfaction. When BCIT redesigned their site with a strategic focus on prospective students, they saw a 15% increase in satisfaction among these visitors. Their behavioral analytics data also showed a 279% increase in a key conversion event for prospective students. From a strategic standpoint, 4Q Suite has given BCIT a clearer understanding of key audiences and has helped them report their results to the leadership team with easy to understand metrics. This, in turn, has helped them secure additional resources and the support to move forward with new projects.

About 4Q Suite and Google Analytics

4Q Suite was built by iPerceptions. According to Claude Guay, President & CEO, “The response has been tremendously positive. Many of our clients insist that the integration between 4Q Suite and Google Analytics is the most valuable feature that iPerceptions has to offer because it connects what visitors are doing on their website with why they are doing it and how satisfied they are. 4Q Suite has rounded out our Voice of Customer analytics offering. Now companies of all sizes can hear what their website visitors are saying, connect the what with the why, and react to the issues that affect satisfaction and conversion. In the space of a few weeks since launching, hundreds of 4Q Suite customers have already enabled Google Analytics integration.”

4Q Suite can be found through the Google Analytics App Gallery or directly from the 4Q Suite website.

If you’re interested in developing applications for the Google Analytics platform visit Google Analytics for Developers.

Posted by Pete Frisella, Google Analytics API team

Introducing two new versions of the Management API

Today we are releasing two new versions of our Management API into public beta; a brand new version 3.0 and a backwards compatible version 2.4. While the data the API exposes is the same, both versions migrate the Management API from the existing Google Data Protocol to Google’s new API infrastructure. This impacts the way you request and handle data from the API.

With this change, we are also announcing the deprecation of the legacy version 2.3 of the Management API. It will continue to work for 2 months, after which all v2.3 requests will return a v2.4 response.

The biggest changes in switching to the new versions are that developers need to register their applications via the Google APIs Console and use a developer token. Also the URL endpoints have changed, which influence how you request OAuth authorization tokens.

Here’s a rundown of what’s new:

Version 3.0
Is the latest major version of our API and is not backwards compatible. Features include:

• A faster response over version 2.3


• An improved quota policy


• Integration with the Google APIs console to manage API access and request more quota


• OAuth 2.0 is now the recommended way to Authorize users


• The URL to make requests is now at https://www.googleapis.com/analytics/v3/management/...


• The API response is more compact using JSON


• New Google API client libraries, which support many more languages


• Support for the Google Discovery API

All future development of the API will be done to this version so we also added some exciting new data only in version 3, including:

• Event goals are fully represented


• An internal web property id which can be used to deep link into the GA user interface


• Profile configurations for the default page and site search query parameters

Version 2.4
This is a minor version upgrade and we tried hard to make it backwards compatible with the existing Version 2.3. New changes include:

• A faster response over version 2.3


• An improved quota policy


• Integration with the Google API console to manage API access and request more quota


• Continued support for existing authorization mechanisms; OAuth 2.0 now supported


• The URL to make requests is now at https://www.googleapis.com/analytics/v2.4/management/...


• Supports XML response only


• The Google Data JavaScript client library will not work with this version

The XML output from this version is the same as version 2.3 so the existing Google Data client libraries will continue to work.

If You’re a Developer, What You Need To Do
Take a deep breath and get excited ;)

While we typically don’t share our roadmap, to alleviate any concerns, we wanted to give you some insight on where we’re going and how this release fits into the bigger picture. Today’s release adds two new versions to the Management API, v2.4 and v3.0. We also have a Data Export API that provides access to report data, which is still on version 2.3. In the future, we plan to do a similar upgrade to the Data Export API Data Feed by releasing two new versions for it, and deprecating version 2.3. At that time, we also plan to completely deprecate the Data Export API Account Feed.

The future of our APIs is to access all configuration data through the Management API and all processed report data through the Data Export API.

So for now, if you are already using the Management API, we recommend you migrate to the latest and greatest version 3.0.

If you are still using the Account Feed in the Data Export API, we highly recommend you test out the new Management API and start planning your migration. But, you can probably wait to do a full migration until all our APIs are on version 3.0.

If you have any questions feel free to reach out in our Management API developer group.


Posted by Jeetendra Soneja and Nick Mihailovski, Google Analytics API Team

New Ecommerce Tracking and Validation in the Analytics SDK for iOS

Back in May, we announced Version 1.2 of the Google Analytics SDK for Android.

Today we’re happy to announce that Google Analytics SDK for iOS version 1.2 has been released.  This new version supports Ecommerce tracking as well as the new debug and dry run modes, just like its Android counterpart.

Ecommerce Tracking
With Ecommerce mobile tracking, you can capture transaction and product information, send the data to Google Analytics, and then analyze which products performed best. Of course, because this is all within Google Analytics, you can also tie transaction data back to app usage behavior. See the Google Analytics SDK for iOS developer docs to learn how to implement this feature.

Debug and Validation
In addition to Ecommerce, we’ve added new debug and dry run modes to make it easier to validate your Google Analytics implementation.

Debug Mode:

[[GANTracker sharedTracker] setDebug:YES];

With debug mode, all data requests to Google Analytics are sent to the debug console.

Dry Run:

[[GANTracker sharedTracker] setDryRun:YES];

The dry run mode sends all tracking data locally so that you don’t corrupt your production data.  Just be sure to turn it off before releasing your app, otherwise you won’t collect any usage data.


Posted by Jim Cotugno, Google Analytics Mobile Tracking Team

New Ecommerce tracking and validation in the Analytics SDK for Android

Increasingly, mobile applications allow you to purchase products and virtual goods. For that reason, it’s important to track these mobile transactions in order to understand which products perform well.

So today we’ve added Ecommerce tracking functionality to the Google Analytics SDK for Android.

Ecommerce Tracking
With Ecommerce mobile tracking, you can capture transaction and product information, send the data to Google Analytics, and then analyze which products performed best. Of course, because this is all within Google Analytics, you can also tie transaction data back to app usage behavior. For example, you can now compare the referral that generated an app download by the revenue it generated. See the Google Analytics SDK for Android developer docs to learn how to implement this feature.

Debug and Validation
In addition to Ecommerce, we’ve added new debug and dry run modes to make it easier to validate your Google Analytics implementation.

Debug Mode:

tracker.setDebug(true);

With debug mode, all data requests to Google Analytics are sent to the Android log, allowing you to validate that your app is sending data properly. You can view the Android log using the adb logcat command.

Dry Run:

tracker.setDryRun(true);

The dry run mode sends all tracking data locally so that you don’t corrupt your production data.

See Us At Google IO
We’ll be demoing all this new functionality this year Google IO, so stop by the Optimizing Android Apps With Google Analytics session on May 11, 12:30PM – 01:30PM / Room 9.

Posted by Jim Cotugno, Google Analytics Tracking Team

Client Side Authorization in the API

This post is inspired by Henrik who posted a comment on last week’s Mastering Unique Visitor blog post. His question was, “Most API users would not create web apps but rather easily access their data without account authentication. Is this possible? Could you describe it?”

Great question Henrik! While you do have to authenticate to the Google Analytics Data Export API in order to access data, your application doesn’t have to re-authenticate every time. In this post we show you an easy way to re-use tokens and give you a Python class to simplify the process.

(Note: we recommend you follow the actual sample code to see how all the pieces described below work together.)


Background
Just like you have to log in to view Google Analytics data, you also have to grant an application access to your Analytics data when you use the the API. This is called authorization and is done through the Google Accounts API.

Different Authorization Protocols
When you log into the Analytics interface, your login credentials are protected from being exposed. In the same way, you use the Google Accounts API to protect credentials by requesting an authorization token in place of actual user names and passwords. That authorization token is used in the header of each Google Analytics API request.

There are a two authorization routines that applications can choose in order to obtain this token:

Client Login - a legacy, Google proprietary, authorization routine. The user of your application must supply their user name and password in your application. Your application communicates to Analytics on the user’s behalf and retrieves an authentication token. This token only lasts for 14 days. The issue with this method is that if the token is stolen, others can use it and the user has no way to revoke it.

OAuth For Installed Apps - is Google-supported open standard. Client apps must send users to a web page where the user logs in and grants account access to the application. The web page presents the user with a verification code which they must input back into the application. The application can exchange this verification code for a long-lived token. The long-lived token lasts forever and can be revoked by the user through the Google Accounts interface. This routine is more secure because a user can revoke the token and prevent an application from accessing their data at any time.

Obviously OAuth For Installed Apps is a better way to generate authorization tokens.

Token Management
Because authorization tokens are valid for multiple requests, applications should reuse them until they become invalid. For a client-side app, this usually means saving the token to disk. Here’s the logic to handle this in python:

def GetAuthToken(self):
  if self.auth_token:
    return self.auth_token


  self.auth_token =
    self.auth_routine_util.LoadAuthToken(self.token_obj_name)


  if not self.auth_token:
    self.auth_token = self.RequestAuthToken()


  self.auth_routine_util.SaveAuthToken(self.auth_token)
  return self.auth_token

Here the code tries to load a token from disk. If it exists, the token is set in the client object that makes requests to Google Analytics. If the token does not exist, the code tries to obtain it from the Google Accounts API, then save it to disk...all pretty easy.

Retrieving a New OAuth Token

To get a new OAuth token, the code uses helper methods in the Python Client Library. The my_client parameter is a reference to the gdata.analytics.client.AnalyticsClient object provided by the Google Data Python Client Library. It handles all the logic to make requests to the Google Analytics API.

First, the my_client object is used retrieve the URL to the OAuth authorization end point. Next it retrieves a request_token from the Google Accounts API and use that token to generate a URL for the user to go to.

The URL is printed to the screen and the code tries to pop open a browser and take the user directly there. The user will be prompted to login and grant access to the application. Once complete, the web page gives the user a verification code.

The code prompts the user for the verification code, and once inputted, the application uses it to get a long-lived access token. This access token will be valid until the user revokes the token.

Here’s the sample code:

def GetAuthToken(self, my_client):
  url = ('%s?xoauth_displayname=%s' %
      (gdata.gauth.REQUEST_TOKEN_URL, self.my_client.source))


  request_token = self.my_client.GetOAuthToken(
      OAuthRoutine.OAUTH_SCOPES,
      next='oob',
      consumer_key='anonymous',
      consumer_secret='anonymous',
      url=url)


  verify_url = request_token.generate_authorization_url(
      google_apps_domain='default')


  print 'Please log in and/or grant access at: %s\n' % verify_url
  webbrowser.open(str(verify_url))


  request_token.verifier =
      raw_input('Please enter the verification code '
                'on the success page: ')


  try:
    return self.my_client.GetAccessToken(request_token)


  except gdata.client.RequestError, err:
    raise AuthError(msg='Error upgrading token: %s' % err)

Once we have the long-lived token, it’s returned and the GetAuthToken method above saves it to disk.

Making Google Analytics API Requests

To use the token retrieved above, we simply set it in the gdata.analytics.client.AnalyticsClient object:

try:
  my_client.auth_token = my_auth.GetAuthToken()


except auth.AuthError, error:
  print error.msg
  sys.exit(1)

At this point an application can use the client object to retrieve data from the Google Analytics API. Because a user can revoke a token from the Google Accounts API and the code loads the token from disk, there can be a case where the token on the disk is invalid. In this case, the Google Analytics API returns a 401 status code and the client library raises a gdata.client.Unauthorized exception. If this happens, we need to catch the exception, delete the saved token and prompt the user to get a new token.

try:
  feed = my_client.GetDataFeed(data_query)


except gdata.client.Unauthorized, error:
  print '%s\nDeleting token file.' % error
  my_auth.DeleteTokenFile()
  sys.exit(1)

So there you have it, a quick overview of client side authorization with the Google Analytics API. As we mentioned above, you can view and use the full sample code.

Submit your own questions
We’re thrilled to get feedback from our developers. You guys rock! If you have a question about our API, like Henrik, please leave it in our comments. We’ll go through them and try to highlight the code solutions.


Posted by Nick Mihailovski, Google Analytics API Team

Deep Dive Articles For The Data Export API

On the Google Analytics API Team, we’re fascinated with what people create using the Data Export API. You guys come up with some really amazing stuff! Lately, we’ve also been paying a lot of attention to how people use it. We looked at whether the API has stumbling points (and where they are), what common features every developer wants in their GA applications, and what tricky areas need deeper explanations than we can give by replying to posts in our discussion group.

As a result of identifying these areas, we’ve written a few in-depth articles. Each article is meant as a “Deep Dive” into a specific topic, and is paired with open-source, sample reference code.

In no particular order, the articles are as follows:

Visualizing Google Analytics Data with Google Chart Tools
This article describes how you can use JavaScript to pull data from the Export API to dynamically create and embed chart images in a web page. To do this, it shows you how to use the Data Export API and Google Chart Tools to create visualizations of your Google Analytics Data.

Outputting Data from the Data Export API to CSV Format
If you use Google Analytics, chances are that your data eventually makes its way into a spreadsheet. This article shows you how to automate all the manual work by printing data from the Data Export API in CSV, the most ubiquitous file format for table data.

Filling in Missing Values In Date Requests
If you want to request data displayed over a time series, you will find that there might be missing dates in your series requests. When requesting multiple dimensions, the Data Export API only returns entries for dates that have collected data. This can lead to missing dates in a time series, but this article describes how to fill in these missing dates.


We think this article format makes for a perfect jumping off point. Download the code, follow along in the article, and when you’re done absorbing the material, treat the code as a starting point and hack away to see what you can come up with!

And if you’ve got some more ideas for areas you’d like us to expound upon, let us know!

Posted by Alexander Lucas, Google Analytics API Team