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3 Key Google Analytics Features In-House Practitioners Should Be Using

Google Analytics team — Wed, 17 Apr 2013 20:16:00 +0000

Working as a practitioner in house at a technology company, one of my jobs is to teach my team members how to fish with Google Analytics. What should they be looking for in GA? Where do they start? What is meaningful? Are the campaigns being measured? Are the microsites tagged? These are the types of questions I get everyday, and very likely, you do too.

I've narrowed down my tips to 3 key things I try to get people comfortable with first (bite sized bits to get them hooked).

1. Event Tracking

Most of the things that people are interested in are actions on a page. Did a visitor click on button X? Did they complete form Y? Watch video Z? These are all questions we can answer with event tracking.

Because event tracking in Google Analytics is a blank slate in terms of setup and use, there is no one right answer for how to set it up and use. Given that most of my account was setup before I arrived in this position, I too have had to get used to a new architecture. The way I do this, and the way I explain it to my colleagues, is by investigating the event hierarchy. What are the categories, actions, and labels? How is data organized into these three tiers?

While there is no one 'best way' of organizing an event tagging hierarchy, and while it will vary site to site, I like to set mine up like this:

Category: location of event (Homepage, About Us page, Resources page, etc)
Action: action the user took (Video, Whitepaper download, Start Trial, etc)
Label: specifics about action (Video name, Whitepaper name, detail of linked clicked if there are multiple with same action (ex. Learn more - product A, learn more - product B, etc)

2. Advanced Segments

Advanced segments are a great way to filter data to be more specific to the question you are trying to ask. For example, you can create a segment for a region (North America = US + Canada), or you can create a segment for a set of pages (meaning visit applies to homepage and/or about us page). To evangelize and teach this, I've created a Google doc that I've shared with my team with step by step instructions and links to some pre-built regional segments.

As an account admin, it's great to share out globally the segments you make that may apply to multiple consumers. And you can easily share links to segments for users to apply to their own account.

Regional Segment example:

3. Shortcuts

Normally when an internal user asks for GA training and/or help pulling a report, it's for something they plan to look at on more than one occasion. Depending on how complex the report is, it may be useful to create a shortcut.

Ex. Your account has 5000 uniques pages tracked in the pages report. You are interested in 4 pages that all share the same sub-domain (they may be steps in flow - example: www.myshoppingsite.com/women, www.myshoppingsite.com/accessories, www.myshoppingsite.com/handbags, www.myshoppingsite.com/gucci).

You can filter the pages report (using advanced filters) to show only these 4 pages. Then you want to know how many visits to those pages had a checkout, so you apply a checkout segment onto the report. Then you also want to define that group one more step by only looking at North America traffic, so you apply a second advanced segment for North America. Then, just for kicks (or analysis) you want to know what the landing page was for this subset of purchasers, so you apply a secondary dimension for landing page.

Now that's a fairly complicated report that took several steps to build. Your may not want to go through all those steps the next time you need this report (nor as an admin/power user do you want to have to show them again), so you can create a shortcut for this report. The shortcut link is a new beta feature located on the top nav bar that allows you to save a report as is and provides a shortcut link on the left hand nav to get back to it quickly. Pretty handy.

As an admin or power user: Once your users have these three functions handled they will a) be able to pull a lot of their own data, freeing up your time, and b) feel more confident and excited about using Google Analytics to make data driven decisions. Win-win.

Posted by Krista Seiden, Product Marketing Manager, Google Enterprise

Real-Time Widgets Now Available in Dashboards

Google Analytics team — Tue, 16 Apr 2013 18:30:00 +0000

The Dashboards feature in Google-Analytics is a great way to arrange a set of related custom widgets into a report you look at frequently (for example, see dashboards for a variety of use cases in our Solutions Gallery). Today we’re expanding dashboards’ functionality with four new real-time widgets that you can and plug into any dashboard (new or existing) of your choosing!

1) Setting up a real-time widget

If you have set up a dashboard widget before, then this will be very familiar to you. Create a new dashboard or click on the +Add Widget menu option of an existing dashboard.

Pick any of the four widgets available in the Real-time: section and customize the widget. Below I have created a widget that shows me active visitors from Canada but split up by Traffic-Type:

Here is how I set up this widget:

I chose the ‘Counter’ real-time widget and gave it a custom name (“Canada Active Visitors”), then set up a ‘Group by’ on ‘Traffic type’. This is how I see the split between referral and organic in the widget. Finally, I set up a filter to only show visitors from Canada.

2. Combining with other dashboard widgets

By creating and combining widgets, you can perform many types of analysis at a glance.

Using filters you can compare different segments of your real-time traffic. For example you can create two separate widgets that have different filters for the Country dimension (say Country==USA for one and Country==Canada for another) and you can compare real-time USA traffic versus India traffic, side by side.

You can go further and set up real-time and non real-time widgets on the same dashboard.

Here is how one of my dashboards looks like (I use it to monitor and understand how traffic from Canada behaves after a blog post):

I have 5 widgets and widget #4 is a non real-time widget that gives me context on where I get visits from across the globe over the last month.

We hope you will enjoy creating new dashboards with these widgets and sparkling some real-time magic on existing dashboards!

Posted by the Google Analytics Real-Time team

Is the web getting faster?

Google Analytics team — Mon, 15 Apr 2013 19:28:00 +0000

At Google, we are passionate about speed and making the web faster. A faster web is better for both users and businesses - faster pages lead to better user experience and improved conversions.

The Site Speed reports in Google Analytics give every website owner detailed data on the speed of their web pages, as experienced by real users.

Last year, we published a study on the speed of websites around the world based on one week of aggregated Site Speed data from opted-in web publishers.

Over the last year, we have seen significant improvements in the core infrastructure that powers the Internet: the web browsers have gotten faster; there have been quite a few LTE/4G deployments making mobile networks a lot faster; and processing power on mobile devices continues to increase at a rapid pace.

To determine whether these improvements in technology are making the web faster, we present recent Site Speed data and compare it with the data from last year.

Here are the results.

While access from desktop is only a bit faster, it is still impressive given that the size of the web pages have increased by over 56% during this period. It’s great to see access from mobile is around 30% faster compared to last year. This is evident from the histograms below as well. For desktop, there is not a significant change in the bucket distributions, but for mobile we see a shift from slower buckets (i.e. higher page load time) to faster buckets.

Taking a look at change in the speed of web pages for a few specific countries, for most of them, there is a slight improvement in page load times on the desktop.

However, there is a significant improvement in page load times on mobile.

The following interactive world map compares the relative improvement in median page load times for desktop over the last year.

This map shows the same data for mobile (Countries without enough data for accurate measurement either this year or last year are shown as having 0% improvement). Speed improvements are greater for mobile in most of the world.

If you are a web site owner, you can analyze and speed up your web site using the PageSpeed products, and check the resulting improvements in Site Speed reports.

Posted by Arvind Jain and Mustafa M. Tikir, Google Analytics team

4 Improvements To Google Analytics Real-Time Reports

Google Analytics team — Thu, 28 Mar 2013 15:58:00 +0000

Real-time reports provide useful insights for businesses that help them understand how their systems are reacting, instantly, such as when you send out an email campaign or engage in marketing that has a temporal nature. It provides alerting / intelligence, giving insight into things that are new or different such as a sudden increase in site traffic. Real-time also lets you win social by capitalizing on trending topics. For example, if you noticed a blog post you published previously is suddenly gaining attention due to something happening in the news, you could highlight it on the front page of your site to draw additional attention and ‘pour fuel’ on the social fire.

Today, we're announcing 4 improvements to real-time reports. You can now:

Analyze Events in real-time
Breakdown real-time by Desktop/Tablet/Mobile traffic
Create shortcuts to your favorite real-time segments
Compare real-time filtered data against overall real-time data

Let’s go through the changes in more detail:

1. Realtime Events Report

With the real-time events report, you can now not only see the top events as they occur but also filter on particular event categories (and actions). Additionally, you can see whether particular segments of visitors trigger different events and debug your events deployment in real time.

To access this report, navigate to the real-time section of Google Analytics and click on the Events section. You should see a report similar to this:

Clicking on any of the Event Category will drill down and show all the Event Actions and Event Labels for that particular category.

If you are trying to see what events a particular segment of visitors generate, that is easy as well. Any filters you set up in any part of real-time are preserved in the Events report. For example, in the above screengrab we have set up a filter here to see what events are triggered from visitors coming via organic search.

2. Content Breakdown by Desktop/Tablet/Mobile

We live in an increasingly multi-screen world, and now you can see in real-time the type of device that visitors are using to visit your web site (desktop, tablet and mobile). This is available in the content report as shown below:

As with real-time reports, you can easily see your visitors filtered by the device type (by clicking on either of “Desktop” “Tablet” “Mobile”).

3. Shortcuts for your important real-time segments

We’ve heard from users that you like to look at certain segments of visitors in real-time, but dislike setting up the filters each time. Now, you can use the “Create Shortcut” feature to store your favorite segments.

Now all you need to do is open up Shortcuts from the left navigation menu and click to any of your shortcuts.

4. Comparison real-time to overall data

Finally, you can compare the pageviews of your segmented visitors to overall traffic as shown below. This is nifty if you want to see quick comparison trends. For example, many times, after a G+ post, I create a filter by device type of “Mobile” and can see that the mobile traffic picks up much faster and also contributes more to the initial increase in pageviews.

Stay tuned for continued improvements to real-time, a growing area of importance for your digital marketing.

Posted by the Google Analytics Real-Time team

Improving The Activity Stream In Social Reports

Google Analytics team — Tue, 26 Mar 2013 18:39:00 +0000

We’ve redesigned the Google Analytics Social Reports to make it easier to see the conversations and activity happening surrounding your content on our Social Data Hub Partners. We’re introducing 2 new reports that make it easier to consume this data:

Data Hub Activity
Trackbacks

The activity stream was previously available via drill down from the Network Referrals or Landing Pages reports. We have now made it a standalone report. By navigating to the Data Hub Activity report you’ll see a timeline of the number of activities that have occurred in the Social Data Hub and the raw activities in a list below. You can also filter this list by any specific networks you choose.

We’re also excited to announce that Trackbacks are now available in a standalone report. Trackbacks are all of your inbound links across the web, so you’ll be informed if anyone from a small to blog to the New York Times posts a link to your site. Additionally, we are providing context for the significance of each of these trackbacks by displaying the number of visits that were driven by each endorsing URL during the reporting period. You’ll see this number presented alongside the trackback.

Image via Google’s Analytics Advocate, Justin Cutroni

Give them a try and happy analyzing!

Posted by Linus Chou, Product Manager, Google Analytics

Expanding Universal Analytics into Public Beta

Google Analytics team — Fri, 22 Mar 2013 15:59:00 +0000

A typical consumer today uses multiple devices to surf the web and interact in many ways with your business. For most large businesses, already swimming in many sources of data, it’s an enormous challenge, but also an incredible opportunity.

Back in October, we announced the limited beta release of Universal Analytics as a way for businesses to understand the changing, multi-device customer journey. Today, we’re excited to welcome and invite all Google Analytics customers to try Universal Analytics.

The benefits of using Universal Analytics to businesses are:

Understanding how customers interact with your businesses across many devices and touch-points,
Insights into the performance of your mobile apps,
Improvements of lead generation and ROI by incorporating offline and online interactions so you can understand which channels drive the best results,
Improved latency on your site by reducing client-side demands.

Testimonials from the initial beta release

Our initial beta customers using Universal Analytics and are pleased with their results. Rojeh Avanesian, VP of Marketing at PriceGrabber.com reports:

"At PriceGrabber, we know it’s important to understand consumer shopping behavior so we can provide a more customized experience to our users. Google’s Universal Analytics will solve this problem for us and many sites that are facing this challenge and help us serve our users better by providing them with more relevant content and shopping results. We can use Google Analytics’ metrics to segment our users in a way that improves and simplifies the shopping experience for consumers. That’s what we strive for at PriceGrabber, to make shopping and saving money as easy as possible."

How to get started using Universal Analytics

If you’re new to Google Analytics, you can choose Universal Analytics when you setup your account. Already using Google Analytics? Create a new web property in your Google Analytics account to set up Universal Analytics and explore the new features.

Here’s what you’ll see when you create a new web property. Select the Universal Analytics column to get the new analytics.js code snippet you can implement on your website:

You can implement Universal Analytics with the new analytics.js JavaScript for websites, our iOS and Android SDKs for apps, and the new Measurement Protocol for all other platforms.

Find more details on how to set up using our help center or developer guide. (Migration guides for properties using ga.js coming soon. Until then, set up a new property in your account for Universal Analytics).

To tag in the most flexible way possible, you can also take advantage of the Universal Analytics template available in Google Tag Manager, which allows you to make additional changes and enable new features to your analytics setup without changing the hard-coded tags on your website. Learn more about how to implement Universal Analytics through Google Tag Manager.

For more information on Universal Analytics, visit our help center and developer guides.

Happy analyzing - in the new and innovative ways you can with Universal Analytics!

Posted by JiaJing Wang, Product Manager, Google Analytics

Learn from Google at #SESNY 2013

Google Analytics team — Wed, 20 Mar 2013 20:12:00 +0000

SES New York (#SESNY) is coming up next week, and just like last year, we hope you’ll join us again in the Big Apple.

During day 1 of SES New York, we’ll be on the expo floor with a custom-built Google classroom as well as part of the main conference sessions. Join us where you’ll learn about the newest need-to-know tools, features, and solutions from Google Analytics, AdWords, Mobile, GDN and DoubleClick Search to help you win in a constantly connected world. Following is a listing of our sessions to help you plan your SES experience.

Google Solutions for a Constantalny Connected World

Day 1 - Tuesday, March 26th all day in the Expo Hall

10:30-11:30am: Reaching customers whenever, wherever, across any device. Surojit Chatterjee, Lead Product Manager for Enhanced Campaigns

11:45-12:45pm: Understanding the Full Value of Mobile. Brendon Kraham, Director, Global Mobile Solutions

2:00-3:00pm: Engaging with your audience in a multi-screen world.

3:30-4:30pm: Using DoubleClick Search to manage campaigns across channels and devices. Anthony Chavez, Product Manager for DoubleClick Search

Don’t have a pass yet? Don’t worry. Expo Only passes are free and get you access to our Google classroom. You can also use the code NYGOOGLE which is redeemable for up to $700.00 off a full conference pass.

For attendees of the main conference, Google Analytics team members will be participating in the following sessions:

Landing Page Optimization

Day 1: Tuesday, March 26 from 11:45-12:45pm

Are your search landing pages working hard or hardly working? Landing pages can be the hardest-working part of your search campaigns, or they can kill your lead generation and sales conversion rates. Testing insignificant details such as button colour and headline size won't lead to real lift in your conversion rates. This session will show you what to test, how to analyse your landing pages, and how to get significant revenue lift quickly.

Moderator:

Andrew Goodman, SES Advisory Board; President, Page Zero Media

Speakers:

Justin Cutroni, Analytics Advocate, Google

Angie Schottmuller, Director of Interactive Strategic Planning & Optimization, Three Deep Marketing

The Dawn of Convergence Analytics

Day 1: Tuesday, March 26 from 3:30-4:30pm

The combination of "big data," access to cloud computing, powerful algorithms, and unprecedented visualization capabilities has created an emerging new class of analytics tools for the marketer. It's being called "Convergence Analytics". It's the marketing equivalent of "one ring to rule them all." Though still in its infancy as a discipline, there are many vendors in the market, and their goal is to pull together data sources from multiple touch points from the web and beyond. They're also using advanced data gathering and data regularization strategies to create a correlative dashboard-like experience for the marketer.

Moderator

Adam Singer, Product Marketing Manager, Google Analytics

Speakers:

Andrew Edwards, Managing Partner, Efectyv Marketing

Rand Schulman, Managing Partner, Efectyv Marketing

We hope to see you out at the event, you can find out more details on the conference site.

Can’t make it to NY? Be sure and follow Google Analytics on Twitter and Google+ for real-time updates on sessions throughout the day sharing interesting bits from the conference.

Posted by the Google Analytics team

Google Analytics Premium expands to Japan

Google Analytics team — Thu, 14 Mar 2013 23:59:00 +0000

Good news, or 良いニュース as they say in Japanese. We’re excited to announce that Google Analytics Premium is now available in Japan.

Google Analytics Premium offers all the power and ease you expect from the standard version of Google Analytics plus extras that make it great for large businesses. With more processing strength for granular insights, a dedicated services and support team, service guarantees and up to 1 billion hits per month, all for one flat fee. It provides access to more data, flexibility, and 24/7 support to help power the analytics of world-class brands including Travelocity, Gilt, TransUnion, Zillow, Papa Johns, & IGN.

Over the last year we’ve launched Google Analytics Premium in the US, UK, & Canada and our customers have been doing some really cool stuff, for example:

Glit was able to gain a more holistic view of their customers by using the increased number of custom variables to power their predictive modeling

They found that access to unsampled data helped them to remove uncertainty and enabled them to act on test and campaign results with confidence

Travelocity was able to provided greater access to data across their company, enabling agile, data driven decision making

For more details check out the full case studies from Gilt and Travelocity.

We’re excited to learn how the data driven marketers in Japan will use Google Analytics Premium to find insights that help them to grow their businesses online.

Partner network provides expert support & customizable service
We’re also pleased to have an international network of Google Analytics Authorized Resellers that have been quality checked to ensure they are best in class analytics consultants. They have highly trained analysis teams and can perform deep analysis projects to reveal valuable insights. Below is the select group of companies that we have partnered with in Japan:

  IMJ Ayudante
  e-Agency NRI Netcom
  Dentsu eMarketing One transcosmos
  Mitsue-Links

We plan to make Premium available to even more countries in 2013. If you would like to learn more about Google Analytics Premium and how it can help your business, contact the Google Analytics sales team or one of our Google Analytics Premium Authorized Resellers.

Posted by Clancy Childs, Google Analytics Premium Team

Get Useful Insights Easier: Automate Cohort Analysis with Analytics & Tableau

Google Analytics team — Tue, 12 Mar 2013 17:44:00 +0000

The following is a guest post by Shiraz Asif, Analytics Solutions Architect at E-Nor, a Google Analytics Certified Partner.

Cohort analysis provides marketers with visibility into the behavior of a “class” of visitors, typically segmented by an action on a specific date range. There are many applications and businesses that would benefit tremendously from cohort analysis, including the following sample use cases:

What traffic channel yields the most valuable customers (not just valuable one time conversions)
Customer life time volume based on their first bought item (or category)
Methods for gaining and retaining customers and which groups of customers to focus on
For content and media sites, understanding frequency, repeat visitors and content consumption after sign up or other key events
Repeat Purchase Probability

If you read E-Nor President and Principal consultant Feras Alhlou’s latest post on cohort analysis in a cross-platform environment, and read until the very end, you saw a note about a follow up post on how to automate cohort reporting from Google Analytics in Tableau. This is what I'll outline in today’s post. Why the emphasis on automation, you might ask? Without automation, we end up spending more time than necessary on exporting/copying/pasting/massaging data which can eat up resources better used analyzing and optimizing.

In addition to report automation, data visualization is also key. Google Analytics offers amazing visualization, including the recently announced dashboard enhancements, but at times you also want to view the data and trend it or merge with other sources. For this, its best to use tools available in the Google Analytics Application Gallery or a BI platform like Tableau.

With the introduction out of the way, following is a step-by-step guide to automated, cohort analysis with Google Analytics and Tableau:

1. Cohort Data Elements in Google Analytics

If you have your cohort data elements already captured in Google Analytics, then skip this step, otherwise, this post is on setting up cohort data in by Google’s Analytics Advocate Justin Cutroni is a must.

2. Tableau version 8 (Google Analytics connectors)

In order to automate reports, you need to have Tableau version 8, since this is the version that has a Google Analytics connector (works well, although still in beta).

3. Data Import from Google Analytics Into Tableau

From the Tableau home screen, select Connect to Data, and then pick the Google Analytics connector. After authenticating to Google Analytics, you'll be prompted to select your Account, Property and Profile, if you have access to more than one.
Set up the data import to get your Custom Variable key (e.g. CV1) and Date as dimensions, and Revenue as a Metric.

4. Tableau Cohort Analysis Configuration

Change the format from Google's 20130113 to a Tableau DATE format. Since the date was stored in a custom variable, it was stored as a string. So that Tableau can treat this as a date, we need to convert the string to a date format. This was done by creating a new Calculated field in Tableau. We called the field "Cohort Date". The formula below worked for our purposes but would require some tweaking for larger datasets.

Now that we have the date in the format we want, the next step is to subtract the cohort date from the transaction date. To do this, we created another calculated field called "Days since Signup". The formula for this field was simply:

DATEDIFF('day',[Cohort Date],[Date]).

Important: Tableau natively treated this as a "Measure" since it is a number. However since we're going to be graphing this on the X Axis, you should drag it to the Dimensions pane.

Drag the Revenue measure to the rows Rows tab. Now drag the Days since Signup to the Columns tab. You should see a long graph similar to:

Drag the Cohort date to the Filter pane, and select the cohort dates you'd like to visualize. For ease of use, I suggest, select only a few to begin with. Drag the Cohort to the color shelf to enable color coding of individual cohort dates.
Now let's make a couple of adjustments to make the visualization more useful. In the color shelf, click the down arrow next to Cohort Date, and change the default display from Continuous to Discrete. Then, in the same field, select Exact Date instead of Year.

Voila! Your final view should look like this:

There you have it. With a few steps, we’ve pulled data from Google Analytics via the API using Tableau, massaged the data and then created a very insightful visualization. With this work now done, the graphic can be easily updated/refreshed. This takes the manual and mundane work of setting up the graphic and automates it so we can spend more time analyzing the data and finding hidden insights for our clients.

Posted by Shiraz Asif, Analytics Solutions Architect at E-Nor, Google Analytics Certified Partner. Learn more about E-Nor on their website, Google+ or check out their Marketing Optimization blog.

Enhancing Google Analytics Access Controls

Google Analytics team — Mon, 11 Mar 2013 15:59:00 +0000

Today we’re excited to announce that enhanced user-access control lists are coming to Google Analytics. Google Analytics users have long been requesting more fine-grained control over access to various parts of their accounts. We listened, and we're delivering that control over the coming weeks.

Recap of the current access control system

Previously, user access was controlled with a role-based system. A user could be either a full account administrator, or a simple report viewer on your profiles.

How the new system will work

First, we’re expanding where permissions can be applied. We’ll allow permissions to be set not only at the Google Analytics account and profile levels, but also at the property level (learn more about these entities). Second, we’re enhancing the permissions any user can have. Instead of offering only two roles, (administrator & report viewer) we’re now allowing every user to have any combination of view, edit, and manage-users access. You can customize permissions for each user at the account, property, and profile level, providing a greater variety of access than was available with the previous role-based system.

For example, one user can have full access to an entire account, another user can have edit and view access to a single property, and a third user could have view only access to a set of profiles.

Properties inherit permissions set on their parent account, and profiles inherit permissions set on their parent properties. For example, a user with view access to an account, also has view access to all of that account's properties and profiles.

Migration

We will automatically migrate all accounts over the coming weeks. When your account is migrated, you will notice a new, richer user-management interface that looks like the following:

Click for large-sized preview

The migration will convert account admins to users with full (manage users, edit, and view) access to that entire account; report-viewing users in the current role-based system will remain as users with view access to relevant profiles.

Conclusion

By making these changes Google Analytics users will be able to better meet their access-control needs and have an even better analytics and reporting experience. Enjoy the new controls!

Posted by Tim Thelin & Matt Matyas, Google Analytics Team

Learn About the 7 Factors of Bid Optimization

Google Analytics team — Tue, 05 Mar 2013 18:24:00 +0000

A version of the following post originally appeared on from the DoubleClick Search blog.

At DoubleClick Search we know that search marketing has expanded dramatically in scale and complexity over the years, and today, large search campaigns may be difficult to manage using manual methods alone. As such, marketers are relying more and more on automated bid optimization platforms to run larger campaigns -- enabling them to scale up and streamline their operations at the same time.

In a recent blog post series on the DoubleClick Blog, we explored key factors to consider when evaluating a search bid optimization platform, including flexible expression of goals, fresh data, smart algorithms, fast operations, regular software updates, sufficient controls, and dedicated, consultative services. As a wrap up to our bid optimization series, we want to recap the importance of these factors with an infographic:

Click here to view the full infographic

Using the 7 factors as a guideline, you can choose the platform that’s best for your business, to help you save time, get the best results, and make better decisions in your digital marketing efforts.

Stay tuned to the DoubleClick Search blog to learn more about enhancements, updates, and launches around the Performance Bidding Suite. To learn more about the 7 factors to consider when choosing a bid optimization tool, download our white paper here.

Posted by Kim Doan, Product Marketing Manager, DoubleClick Search

5 Things You Should Be Doing With Google Mobile App Analytics Crash & Exception Measurement

Google Analytics team — Wed, 27 Feb 2013 18:44:00 +0000

When an app crashes, it disrupts the user experience, may cause data loss, and worst of all, might even cause users to uninstall the app altogether. As developers, we do our best to minimize crashes, but no app is ever perfectly stable.

A crash can actually represent a great opportunity to improve an app and one of the best things we can do as developers is to measure our crashes and exceptions.

The crashes and exceptions report in Google Mobile App Analytics.

Measuring crashes in your app can help you make better a product, make more money (if that’s your thing), and use your development resources more efficiently (especially if you are the only developer).

Google Mobile App Analytics offers easy-to-implement automated crash and exception measurement for Android and iOS as part of the V2 SDKs, as well as a host of reporting options to slice the data in context with all of the user engagement, goal completion, and in-app payments data you already know and love.

To help new developers get started, and to give existing developers some pointers, here are four things app developers should be doing today with Google Analytics crash and exception measurement:

1. Automate your crash measurement.

Want to measure app crashes but don’t want to deal with a complicated implementation? Fully automated crash measurement with Google Mobile App Analytics takes just one line of code to implement for Android or iOS:

true

// Enable automatic crash measurement (iOS).

[GAI sharedInstance].trackUncaughtExceptions = YES;

Implement automated crash measurement with just one line of code on Android or iOS.

Now each time your app crashes, the crash will be measured and sent to Google Analytics automatically. Try automated crash measurement now for Android or iOS.

2. Find out how stability is trending.

Are new releases increasing or reducing app crashes? Monitor the stability of your app from version to version by looking at crashes and exceptions by app version in the Crashes & Exceptions report.

If you are measuring the same app on two different platforms, like Android or iOS, you can break this view down further by selecting Platform as the secondary dimension.

View crashes and exceptions by app version number in the Crashes & Exceptions report. In this example, version 1.1.7 has crashed 7,285 times, while the latest version 2.0.0 has only crashed 91 times in the same period. Nice work dev team!

To graph crashes for two or more versions over time, you can create advanced segments for each version number, and apply them both to the Crashes and Exceptions report.

See crashes by app version over time using advanced segments and the crash and exception report In this example, a bug fix pushed around January 24 caused significant reduction in crashes across both versions, but crashes persist for v1.1.7 that might warrant some additional investigation.

3. Find out what crashes are costing you.

Do you know what app crashes are costing you? Find out what crashes cost in terms of both user engagement and dollars by using a custom segment.

By using a particular crash or exception as a custom segment, you can see how user engagement and in-app revenue may be impacted by a particular issue or set of issues.

Use custom segments to segment user experience and outcome data by crashes. This gives you some idea of what they might be costing you in users and in dollars.

To set this up, you’ll want to create two custom segments: one that contains all the sessions in which the exception(s) occurred, and another baseline segment that contains all other sessions unaffected by the exception(s).

Learn how to create advanced segments.

Once created, try applying both segments to your Goals or Ecommerce Overview reports to get a sense of how the exception(s) might affect user outcomes. Or, apply the segments to your Engagement overview report to see how the exception(s) might impact user engagement metrics.

4. Gain visibility into crashes at the device model level.

Do you know which device models are the most and least stable for your app? Developers can’t always test their app on all devices before launch. However, by using Custom Reports in Google Mobile App Analytics, you can monitor crashes and exception per device to find out where additional testing and bug fixes may be needed.

To see crashes and exceptions by device, create a custom report and use a dimension like Mobile Device Marketing Name, with Crashes and Exceptions as the metric.

Learn how to create a custom report.

See crashes by device by using a custom report. To get even more detail, add the Exception Description dimension as a secondary dimension. In this example, the high level view shows the Galaxy Note and Desire HD as device that might need additional testing before the next launch.

5. (Advanced) What about caught exceptions? You should measure those too.

While caught exceptions won’t crash your app, they still may be valuable events to measure, especially when they might have an impact on user experience and outcomes.

For instance, if your app normally catches a server timeout exception when requesting user data, it might be useful to measure that caught exception to know how often a user’s request is not being fulfilled.

A caught exception is measured in Google Analytics using a custom description. In this example, a number of failed connections may indicate a backend problem and could be causing a poor user experience. Reducing the number of these caught exceptions could be a goal for the dev team in the next release.

Get started measuring caught exceptions on Android or iOS.

As always, please keep in mind that you should never send personally identifiable data (PII) to Google Analytics. Raw exception descriptions may contain PII and we don’t recommend sending them to Google Analytics for that reason.

Also note that there’s a 100 character limit on exception descriptions, so if you send your own descriptions, be sure to keep them concise.

Lastly, here are some links to resources you might find helpful when implementing crash and exception measurement in your app:

Developer Guide: Measuring Crashes & Exceptions (Android)

Developer Guide: Measuring Crashes & Exceptions (iOS)

And for brand new users:

Getting Started: Google Mobile App Analytics (Android)

Getting Started: Google Mobile App Analytics (iOS)

Posted by Andrew Wales, Google Analytics Developer Relations

Integrated Platform Enables Mindshare to Deliver Better Insights, Faster for Clients

Google Analytics team — Tue, 26 Feb 2013 17:27:00 +0000

A version of the following post originally appeared on the DoubleClick Advertiser Blog.

Mindshare, a global media network and digital agency, recently deployed DoubleClick Digital Marketing (DDM) components to service several of their client accounts.

We caught up with Danny Huynh, managing director of digital and client leadership at Mindshare, to find out more about their experience with DDM.

What led you to look at the DoubleClick platform?

We’re platform agnostic at Mindshare, and are constantly evaluating technology solutions to identify what works best for a particular client.

Early in 2012, one of our retail clients wanted to drive deeper consumer insights with Google Analytics, as well as, enhance the overall performance of their campaigns. The integrated DDM stack had just been unveiled, and it made sense for us to explore the synergies this platform could unlock.

You eventually deployed DoubleClick Search, DFA, Invite Media and Google Analytics for the client. What was the deciding factor in selecting these components?

An important factor was that these were tied into Google Analytics and the single cookie view across the DoubleClick stack. We knew these data would help us understand the complete consumer journey including all the various interactions and touch points -- whether that’s through a search ad, a display ad from a brand campaign, or part of a remarketing effort.

How was the deployment process for these new platforms?

Look, you need a platform that is accurate and reliable, but that only gets you so far; the brains and effort it take to set up the platform is really the key to success.

In the past, we’ve always had a sales team and a technical team to interact with. In this instance our everyday contacts were also super knowledgeable about all of the behind-the-scenes stuff. So when we do have a problem, they take care of it.

What kind of advice would you have for others about their use of tech platforms?

Be careful to not use technology to optimize your brand out of consideration. Today’s consumer journey is no longer a funnel. It’s complex, with different zigzags of consideration and reconsideration. As a marketer, you need to paint the full picture of how the digital channels interact and how each drives a sale -- whether an immediate conversion or not.

It can be tough to convince a client to do that if they’re a pure performance marketer, but if you’re a brand marketer, you need that strong foundation, have the plumbing in place, so that you can see how it all works within an integrated media plan.

At Mindshare, we call that approach “adaptive marketing” where we use fast-moving data to refine our approach over time to make sure we’re driving the results our clients want.

Read more about Mindshare’s experience with DoubleClick Digital Marketing here.

Posted by Scott Brown, DoubleClick Team

Understand Updates To Your Account With Change History

Google Analytics team — Thu, 21 Feb 2013 15:28:00 +0000

Have you ever wanted to learn more about changes made to your Google Analytics account, wanted to refresh your memory as to when a particular profile setting was changed, or wondered who on the team made a goal change? Now, all of that is possible with the launch of Change History.

What it does

Change History presents a summary of many important changes to your account over the last 180 days. Users will find records of changes made to users, accounts, properties, profiles, goals, and filters. This feature is available only to Analytics account administrators.

How to find it

We are rolling out Change History to our customers over the coming weeks. Once it’s enabled on your account, you’ll be able to see it by clicking the “Admin” button in the upper right corner of the Analytics interface, selecting the appropriate account, and clicking the tab labeled “Change History.” In this new section, you will see a list of changes on your account, when the change took place, and who made the change.

Conclusion

The new Change History helps you better understand how your accounts evolve over time, improves account collaboration, and provides an additional tool for optimal configuration.

Be sure and view our help center article for additional information.

Posted by Scott Ellsworth and Matt Matyas, Google Analytics team

Are you a Datavore? Insights on the use of online customer data in decision-making

Google Analytics team — Wed, 20 Feb 2013 19:19:00 +0000

The following is a guest post contributed by Hasan Bakhshi and Juan Mateos-Garcia who work at Nesta, an independent charity based in London. Nesta’s mission is to help people and organizations bring great ideas to life by providing investments and grants, and mobilizing research, networks and skills.

We surveyed 500 of the UK's companies that were actively online and promoting their products. We asked about the collection, analysis and use of online customer data in their decision-making, and the impact this has on their bottom line. Our research suggests that a startlingly high number of businesses in the UK's Internet Economy would benefit from reading Michael Loban's post on data resolutions for 2013. Here, we revisit some of his insights backed up by our data to illustrate how big the online data gap is for many UK companies, and what they must do to bridge it.

Insight 1: 'Address your data phobia'.
We identified a cohort of companies in our sample with apparently no fear of data. We call them 'datavores'. When making decisions about how to grow their sales, they rely on data and analysis over experience and intuition. They collect data comprehensively, analyze it thoroughly and use it to make decisions. But they are in the minority: only 18% of the companies we surveyed compared with 43% who make decisions on the basis of experience and intuition. These ‘gut-driven’ companies would stand to reap significant commercial benefits from their online data if they could get over their data phobia. We find that datavores are four times more likely to report substantial benefits from their online customer data.

Insight 2: Get on with social network marketing.
Only 40% of businesses in our sample report that online data is important for designing and evaluating their social media strategy. Lacking the right data to make decisions, perhaps we shouldn’t therefore be surprised to learn that more than half of the businesses we surveyed were hesitant to dip their toes in social network marketing, despite the fact that nearly 1/2 of the UK population* uses social media actively. (* cited from UK Office for Communications)

Insight 3: Tools are great, but great analysts are awesome.
Our survey suggests that fully harnessing the potential of online data requires up-skilling the workforce. Over three-quarters of businesses who have trained their staff to improve their data capabilities in the past two years report significant benefits from online data (compared with only 20% of those who haven’t provided training). Another of our findings leads us to add a coda to Michael’s resolution, however - while it is true that great analysts are awesome, it appears that great analysts who are empowered to act on the basis of their insights are even better. Datavores are much more likely to delegate decision-making than other firms. The implication is that making most of your data is not always painless. It may require re-organizing the business, changing its culture and rethinking the role of managers. 'No pain, no gain', as they say about most New Year's Resolutions.

Insight 4: ACTION!
This brings us neatly to perhaps our most important finding: in order to benefit from online data UK businesses need to put their data to work. They need to use it to improve their website to be sure, but they also need to feed it into decision-making process in other parts of their business – such as in product development and business strategy. In fact, the ‘use of data to make decisions’ turns out to be the main factor discriminating between the datavores and the other companies we surveyed. And that, controlling for other relevant business characteristics, ‘using data’ is what really makes a difference on the impact of data on performance. To put it in stark terms, if you don’t use your data, you may as well not have collected and analyzed it.

We present the findings above (and many more) in greater detail in our Rise of the Datavores report. This is the first milestone in our program of research and action looking at the potential of online data for innovation and growth.

In the coming months, we will be matching our survey responses to financial data to measure in quantitative terms the connection between data-driven decision-making and sales growth, profitability and productivity. To get a really robust handle on the direction of causality in this relationship we are looking to run a controlled experiment to measure the impact of an Online Analytics intervention on a randomly selected group of UK businesses. In related research we plan to quantify the extent of business demand for data-savvy talent and assess the adequacy of the UK's education system in supplying it.

Last, but not least, we will be looking through practical work to identify datavores in the public and third sectors, and work to encourage the transfer of good data practices across different parts of the UK’s economy and society.

In all these areas we will be looking for data experts to work with, to probe whether we are asking the right questions, to refine and help implement our research plans. Drop us a line if you want to hear more!

hasan.bakhshi@nesta.org.uk
juan.mateos-garcia@nesta.org.uk

Posted by Hasan Bakhshi and Juan Mateos-Garcia at Nesta

Upgrade To The Next Generation Mobile App Analytics Platform

Google Analytics team — Thu, 14 Feb 2013 17:32:00 +0000

Spring is coming right around the corner and it's never a bad idea to get a head start on your spring cleaning! The same goes for your mobile app analytics solution. Have you switched over to our new Mobile App Analytics Platform (v2) yet? The benefits are numerous:

A more powerful mobile SDK

We are providing a new mobile app analytics solution, solving the problem that there is currently no single repository to understand end-to-end value of mobile app users. This is supported by a more powerful mobile SDK (v2.0) that is easy to implement.

“One stop shop” for app measurement

Understanding app performance holistically through acquisition, engagement and outcome is critical to improve mobile app results, optimize user engagement and increase revenue generated. Our new reports show the entire lifecycle.

Improve ROI and engagement

App developers and brands can make better, more comprehensive data-driven decisions for mobile investments with better reports. For example, marketers can optimize their mobile programs to improve ROI and app developers can improve in-app engagement.

Though we call it “Version 2,” the truth is that we didn’t just “upgrade” our original platform. We decided to rebuild the whole thing from scratch. Our team at Google Analytics has reimagined mobile app analytics and have created a brand new experience tailored specifically for mobile app developers, providing reports on the data you care most about, in the language you understand. In addition, we also completely rebuilt our Android and iOS SDKs to be even more lightweight, efficient, and faster.

We’re continuing to build and add features to this platform all the time. So if you haven’t migrated yet, now is the perfect time to do it and find out exactly what you're missing out on.

To make it easy to migrate, we’ve put together a Migration Guide for Android and iOS to help you make the move.

And if you’re new to Mobile App Analytics, check out the Getting Started Guide for Android or iOS to get your app up and running with Google Analytics in minutes.

Posted by Calvin Lee, Google Analytics Team

Multi-Currency E-Commerce Support In Google Analytics

Google Analytics team — Wed, 13 Feb 2013 18:48:00 +0000

We’ve listened to your feedback and have heard the requests loud and clear: E-Commerce should support multiple currencies. We’re pleased to announce the launch of this feature which will be rolling out to all users over the next few weeks.

Multi-Currency support for eCommerce provides Google Analytics users with the ability to track transaction metrics (total revenue, tax, and shipping & handling) in multiple local currencies within a single web property. And Google Analytics will convert them into the one currency based on your profile setting. This provides key benefits for e-commerce brands looking to conduct analysis across an international customer base and helps make some previously complex reporting easier.

New metrics supported in multi-currency

Multi-Currency tracking code implementation:

Multi-currency is supported by web tracking and Android SDK (iOS SDK support is coming soon).

The ‘currency code’ is a global setting that can be set via tracker ‘_set()’. It only need to be set once, unless the page is sending multiple transactions in separate currencies.

Here are a few other things we think you’ll want to know:

How the conversion rate is decided?
The conversion rate is pulled from currency server which is serving Google Billing. The value is the daily exchange rate of the day before hit date. See a technical overview for additional information.

Which currency does GA support?

We support currencies which are available in GA profile currency dropdown menu. Right now, 31 currencies altogether are supported.

Currency dropdown menu

Which currency code shall I use?

A full version of currency codes shared across Google products is available on the Google Developers site.

Can I retro-process my history transaction data?

Only from the day you started using multi-currency support, you can get both local and global value.

Several companies have already started using Multi-Currency in Google Analytics and are seeing great results. One of our Certified Partners, Blast Analytics & Marketing, helped their client AllPosters.com implement this feature. David Tjen, Director of Analytics at AllPosters.com reports:

"Google Analytics' new multi-currency feature increases sales metric accuracy for AllPosters.com. As an international brand, the AllPosters family of sites supports 20 currencies across 25 countries. Previously, manual adjustments were required before we could read sales metrics in Google Analytics when we had transactions with large currency conversion ratios to the US dollar, such as the Mexican Peso and Japanese Yen. The simple code update solves the issue by automatically converting all transactions to the primary currency on each site, providing sales metrics that allow us to make faster decisions with our web analytics data.”

To get started today, view our help center page with detailed instructions on how to begin.

Posted by Wayne Xu, Google Analytics team

Verify Your Measurement Setup With Tag Assistant

Google Analytics team — Mon, 11 Feb 2013 17:42:00 +0000

Google Analytics is, at its core, a simple and powerful tool. But once you start to customize the code to take advantage of all the flexibility available you may find yourself needing some help troubleshooting a nagging issue.

A new Chrome Extension created by engineers here at Google hopes to make troubleshooting tag installs much easier. Tag Assistant aims to highlight errors, warnings, and provide useful suggestions for Google's most widely adopted tags including Google Analytics, Google Tag Manager, Adwords Conversion Tracking, the new Remarketing Tag, Trusted Stores and Floodlight.

After installing the extension, Tag Assistant will alert you if tags are found on any page you are currently browsing. For each tag we will tell you if it appears to be working or if we notice any problems with your implementation. Tag Assistant will even make recommendations on how to improve your installation if we notice any optimizations. For example, if you have 2 or more tags implemented separately we might suggest that you migrate to use Google Tag Manager instead.

How does it work? Tag Assistant looks for errors in two different ways. First, we check the source code to look for common errors like forgetting to include a closing tag. We also review the HTTP headers to ensure that we are getting expected responses.

Since launching in October of 2012 we have collected a lot of your feedback and have added dozens of new checks. Over the course of the year we will be adding more checks that will make the Tag Assistant more accurate and helpful.

We encourage you to try it out for yourself by installing it via the Chrome Web Store. If you have feedback on new checks to add or if you have questions about the tool, join our Google+ community where our team and users can help you out.

Posted by Geoff Pitchford, Google Tag Assistant PM

Google Tag Manager: Implementation webinar video, cheat-sheet, and Q&A

Google Analytics team — Fri, 08 Feb 2013 18:47:00 +0000

Last Tuesday, we held a webinar on the technical implementation of Google Tag Manager, a free tool that makes it easy for marketers to add and update website tags, freeing up webmaster time while providing users with more reliable data and insights. This technical session includes a more in-depth look than our introductory webinar, illustrating how the product operates in a live environment and showing how flexible Google Tag Manager is for enterprise systems.

Watch the webinar video here for:

Step-by-step implementation process + live product demo
Advanced use cases, including the Data Layer API
Best practices and common pitfalls

And don’t forget to download our handy implementation Cheat-Sheet, which outlines each of the steps involved in migrating onto Google Tag Manager.

Click here to download the Implementation Cheat-Sheet: http://goo.gl/5GJyA

And as usual, we like to provide a recap of some of the top questions we received during the webinar. Please note that this webinar is intended for technical audiences, so some of the Q&A below gets into the nitty-gritty technical details. If you’re less experienced technically, we invite you to check out our forum or reach out to one of our certified partners for implementation assistance.

Questions and Answers

Where can I find more detailed information about all of this stuff?
In addition to the walkthrough we provide in the webinar and our Cheat-Sheet, you can find a detailed description of the implementation process in the Google Developer docs, and helpful articles about how to use the Google Tag Manager user interface in our Help Center, including some notes about what to think about before you begin implementing. And as noted above, if you still have questions, check out our forum or reach out to one of our certified partners for implementation assistance.

Where can I place the GTM snippet? Can I put it in ? Does placing it in the footer have any adverse effects? Can I place the data layer in ?
The recommended location for the GTM snippet is just after the opening tag. The only exception to this would be in the case where you want to declare page-level metadata by declaring the data layer immediately above the GTM snippet.

The GTM snippet can be deployed later in the page, like the footer, but doing so increases the time before the snippet loads. This can cause incremental amounts of data loss, since the user could navigate away before all your tags finish loading.

We do not recommend placing the GTM snippet in head, because the GTM snippet contains an for the

What should I do about collecting macros and tagging events if I don’t have access to my client’s site or if IT is too busy?How do I do cross-domain tracking in Google Analytics using Google Tag Manager? Do you have any account and container setup best practices? What if I’m an agency? What if I have separate sites for mobile and desktop?user permissions controlsAre all tags with document.write off limits? Are there any workarounds?How do Google Analytics events differ from Google Tag Manager events?Google Analytics eventsGoPro
Posted by the Google Tag Manager team

Win moments that matter in 2013 with Learn with Google webinars

Google Analytics team — Mon, 04 Feb 2013 17:59:00 +0000

A version of the following post originally appeared on the Inside AdWords Blog.

What was your business’ New Year’s resolution, and how do you plan to keep it? At Google, ours is to help make the web work for you. Our new series of Learn with Google webinars will teach you how to use digital to build brand awareness and give you the tools you need to drive sales. By tapping into technology that works together across your business needs, you can resolve to win moments that matter in 2013.

Check out our upcoming live webinars:

Build Awareness

02/12 [Multiscreen] Brand Building in a Multiscreen World

02/20 [YouTube] How to Build your Business with YouTube Video Ads

03/05 [Social] How to Use Google+ and Make Social Work for You

03/12 [Mobile] Understanding Mobile Ads Across Marketing Objectives

03/27 [Wildfire by Google] The Call for Converged Media

Drive Sales

02/07 [Search] Your Shelf Space on Google: Get Started with Google Shopping

02/26 [YouTube] From Awareness to Sales: Making the Most of Video Remarketing

02/27 [Search] What's New and Next in AdWords

03/06 [Display] Biggest Loser: Digital Ad Spend Edition

03/13 [Mobile] The Full Value of Mobile

03/20 [Display] Getting Started with Dynamic Remarketing

Visit our webinar site to register for any of the sessions and to access past webinars on-demand. You can also stay up-to-date on the schedule by adding our Learn with Google Webinar calendar to your own Google calendar to automatically see upcoming webinars.

During our last series of webinars, attendees had the chance to win a Nexus 7. Our lucky winner was Donella Cohen, who is happily enjoying her new tablet. Check out our upcoming webinars for another chance to win!

Learn with Google is a program to help businesses succeed through winning moments that matter, enabling better decisions and constantly innovating. We hope that you’ll use these best practices and how-to’s to maximize the impact of digital and grow your business. We’re looking forward to seeing you at an upcoming session!

Posted by Erin Molnar, Learn With Google

Optimize Your Website with SiteApps and GA

Google Analytics team — Fri, 01 Feb 2013 17:30:00 +0000

Google Analytics excels at collecting an incredible amount of information about how visitors interact with the web and mobile properties of its users. This data provides marketers and analysts who know what they’re looking for with with an incredibly powerful platform to understand what’s working and what’s not. To those who aren’t sure what they’re looking for though, all of this information can be overwhelming and make it easy to take no action at all.

SiteApps enables businesses to get instantaneous, free recommendations on how to optimize their website based on their Google Analytics data. SiteApps’ technology runs hundreds of automated analyses on its customers’ web data to identify opportunities for improvement. Based on these tailored recommendations, SiteApps then enables businesses to install apps from their marketplace to help solve these problems.

One of SiteApps’ customers is a family-owned home furnishings designer that was having difficulty maintaining their eCommerce presence while still focusing on the day-to-day operations of their brick and mortar retail store. Within minutes of signing up for SiteApps, they were able to identify dozens of opportunities for site optimization. By installing the apps that were recommended to them, they were able to create a compelling web presence that increased their conversion rate by 108% and led to 65% more time spent on site by its visitors. This led to a substantial increase in revenue for the business simply by unlocking the power of their web analytics data.

“Our business is completely based on data. It’s incredibly important to us that customers know - or learn - just how valuable their data is,” says Phillip Klien, co-founder of SiteApps. “We consider Google Analytics the foundation for our platform and use the results to help customers make the most of the data their website produces.”

SiteApps is free to try and takes a matter of minutes to set-up. Give it a try today to see what you can uncover from your web analytics.

Posted by the Google Analytics team

Dashboards, Advanced Segments, And Custom Reports For Your Business Needs

Google Analytics team — Mon, 28 Jan 2013 16:58:00 +0000

We’ve heard you loud and clear that getting started on Google Analytics can be challenging. It’s such a robust tool with a variety of reports, filters, and customizations that for a new user it can be overwhelming to figure out where to look first for the data and insights that will enable you to make better decisions. For more advanced users it can be time consuming to build out different variations of reports and dashboards to get the clearest snapshot of your performance. That is why we’ve created the Google Analytics Solution Gallery.

The Google Analytics Solution Gallery hosts the top Dashboards, Advanced Segments and Custom Reports which you can quickly and easily import into your own account to see how your website is performing on key metrics. It helps you to filter through the noise to see the metrics that matter for your type of business: Ecommerce, Brand, Content Publishers. If you're not familiar with Dashboards, Advanced Segments and Custom Reports, check out these links to our help center for detailed descriptions on how they work and the insights they can help provide.

Solution examples
Here are a few examples of the solutions that you can download into your account to see how the analysis works with your data.

Social sharing report - Content is king, but only if you know what it's up to. Learn what content from your website visitors are sharing and how they're sharing it.

Publisher dashboard - Bloggers can use this dashboard to see where readers come from and what they do on your site.

Engaged traffic advanced segment - Measure traffic from high-value visitors who view at least three pages AND spend more than three minutes on your site. Why do these people love your site? Find out!

How do I add these to my account?
We’ve designed it so it’s easy to get started. Simply go to the Google Analytics Solution Gallery, pick from the drop drown menu the solutions that will be most helpful for your business. Select from Publisher, Ecommerce, Social, Mobile, Brand, etc.. . Hit “Download” for the solution you want to see in your account. If you are not already logged into Google Analytics we’ll ask you to sign in. Then you’ll be asked if you want to accept this solution into your account and what Web Profile do you want to apply it to. After you select that it will be in your account and your own data will populate the report.

We’re planning on expanding on this list of top solutions throughout the year so be sure to check back and see what we’ve added. A big thank you to Justin Cutroni & Avinash Kaushik for supplying many of the solutions currently available.

Posted by Ian Myszenski, Google Analytics team

Digital Analytics Association Awards Are Back

Google Analytics team — Fri, 25 Jan 2013 21:45:00 +0000

It’s that time of year again - award season. No, not Hollywood awards, Digital Analytics awards!

The Digital Analytics Association has announced its list of nominees for the DAA Awards of Excellence. These awards celebrate the outstanding contribution to our profession of individuals, agencies, vendors and practitioners.

This year we’re honored to be nominated for two awards.

Google Tag Manager has been nominated for New Technology of the Year. Launched in October 2012, Google Tag Manager has helped many companies simplify the tag management process.

Google, as an organization, has been nominated in the category Agency/Vendor of the year.

We’re incredibly humbled by these nominations - thank you. Our goal is to provide all businesses with the ability to improve their performance using data. We’re excited to be part of this community and we look forward to an even more amazing future.

In addition, a few Googlers have been nominated for individual awards:

Eduardo Cereto Carvalho and Krista Seiden have been nominated for Digital Analytics Rising Star.

Our Analytics Advocate, Justin Cutroni and our Digital Marketing Evangelist, Avinash Kaushik, who travel the world sharing Analytics love have each been nominated as Most Influential Industry Contributor (individual).

If you’re a DAA member make sure you vote by February 6. Winners will be announced at the 2013 DAA Gala in San Francisco on April 16. Tickets are available now.

Posted by the Google Analytics Team

lastminute.com finds that traditional conversion tracking significantly undervalues non-brand search

Google Analytics team — Fri, 25 Jan 2013 16:54:00 +0000

The following post originally appeared on the Inside AdWords Blog.

Understanding the true impact of advertising

Advertisers have a fundamental need to understand the effectiveness of their advertising. Unfortunately, determining the true impact of advertising on consumer behavior is deceptively difficult. This difficulty in measurement is especially applicable to advertising on non-brand (i.e. generic) search terms, where ROI may be driven indirectly over multiple interactions that include downstream brand search activities. Advertising effectiveness is often estimated using standard tracking processes that rely upon ‘Last Click’ attribution. However, ‘Last Click’ based tracking can significantly underestimate the true value of non-brand search advertising. This fact was recently demonstrated by lastminute.com, a leading travel brand, using a randomized experiment - the most rigorous method of measurement.

Experimental Approach

lastminute.com recently conducted an online geo-experiment to measure the effectiveness of their non-brand search advertising on Google AdWords. The study included offline and online conversions. The analysis used a mathematical model to account for seasonality and city-level differences in sales. Cities were randomly assigned to either a test or a control group. The test group received non-brand search advertising during the 12 week test period, while the control group did not receive such advertising during the same period. The benefit of this approach is that it allows statements to be made regarding the causal relationship between non-brand search advertising and the volume of conversions - the real impact of the marketing spend.

Download the full lastminute.com case study here.

Findings

The results of the experiment indicate that the overall effectiveness of the non-brand search advertising is 43% greater¹ than the estimate generated by lastminute.com’s standard online tracking system.

The true impact of the non-brand search advertising is significantly larger than the ‘Last Click’ estimate because it accounts for

upper funnel changes in user behavior that are not visible to a ‘Last Click’ tracking system, and
the impact of non-brand search on sales from online and offline channels.

This improved understanding of the true value of non-brand search advertising has given lastminute.com the opportunity to revise their marketing strategy and make better budgeting decisions.

How can you benefit?

As proven by this study, ‘Last Click’ measurement can significantly understate the true effectiveness of search advertising. Advertisers should look to assess the performance of non-brand terms using additional metrics beyond ‘Last Click’ conversions. For example, advertisers should review the new first click conversions and assist metrics available in AdWords and Google Analytics. Ideally, advertisers will design and carry out experiments of their own to understand how non-brand search works to drive sales.

Read more about AdWords Search Funnels
Read more about Google Analytics Multi-Channel Funnels

-- Anish Acharya, Industry Analyst, Google; Stefan F. Schnabl, Product Manager, Google; Gabriel Hughes, Head of Attribution, Google; and Jon Vaver, Senior Quantitative Analyst, Google contributed to this report.

¹ This result has a 95% Bayesian confidence interval of [1.17, 1.66].

Posted by Sara Jablon Moked, Google Analytics Team

Increasing Your Analytics Productivity With UI Improvements

Google Analytics team — Thu, 24 Jan 2013 20:51:00 +0000

We’re always working on making Analytics easier for you to use. Since launching the latest version of Google Analytics (v5), we’ve been collecting qualitative and quantitative feedback from our users in order to improve the experience. Below is a summary of the latest updates. Some you may already be using, but all will be available shortly if you’re not seeing them yet.

Make your dashboards better with new widgets and layout options

Use maps, devices and bar chart widgets in order to create a perfectly tailored dashboard for your audience. Get creative with these and produce, share and export custom dashboards that look exactly how you want with the metrics that matter to you. We have also introduced improvements to customize the layout of your dashboards to better suit individual needs. In addition dashboards now support advanced segments!

Get to your most frequently used reports quicker

You’ll notice we’ve made the sidebar of Google Analytics even more user-friendly, including quick access to your all-important shortcuts:

If you’re not already creating Shortcuts, read more about them and get started today. We have also enabled shortcuts for real-time reports, which allows you to set up a specific region to see its traffic in real-time, for example.

Navigate to recently used reports and profiles quicker with Recent History

Ever browse around Analytics and want to go back to a previous report? Instead of digging for the report, we’ve made it even simpler when you use Recent History.

Improving search functionality

Better Search allows you to search across all reports, shortcuts and dashboards all at once to find what you need.

Keyboard shortcuts

In case you've never seen them, Google Analytics does have some keyboard shortcuts. Be sure you’re using them to move around faster. Here are a few useful ones:

Search: s , / (Access to the quick search list)

Account List: Shift + a (access to the quick account list)

Set date range: d + t (set the date range to today)

On screen guide: Shift + ? (view the complete list of shortcuts)

Easier YoY Date Comparison

The new quick selection option lets you select previous year to prefill date range improving your productivity to conduct year over year analysis.

Export to Excel & Google Docs

Exporting keeps getting better, and now includes native Excel XSLX support and Google Docs:

We hope you find these improvements useful and always feel free to let us know how we can make Analytics even more usable for you to get the information you need to take action faster.

Posted by Nikhil Roy, Google Analytics Team

Multi-armed Bandit Experiments

Google Analytics team — Wed, 23 Jan 2013 17:40:00 +0000

This article describes the statistical engine behind Google Analytics Content Experiments. Google Analytics uses a multi-armed bandit approach to managing online experiments. A multi-armed bandit is a type of experiment where:

The goal is to find the best or most profitable action
The randomization distribution can be updated as the experiment progresses

The name "multi-armed bandit" describes a hypothetical experiment where you face several slot machines ("one-armed bandits") with potentially different expected payouts. You want to find the slot machine with the best payout rate, but you also want to maximize your winnings. The fundamental tension is between "exploiting" arms that have performed well in the past and "exploring" new or seemingly inferior arms in case they might perform even better. There are highly developed mathematical models for managing the bandit problem, which we use in Google Analytics content experiments.

This document starts with some general background on the use of multi-armed bandits in Analytics. Then it presents two examples of simulated experiments run using our multi-armed bandit algorithm. It then address some frequently asked questions, and concludes with an appendix describing technical computational and theoretical details.

Background

How bandits work

Twice per day, we take a fresh look at your experiment to see how each of the variations has performed, and we adjust the fraction of traffic that each variation will receive going forward. A variation that appears to be doing well gets more traffic, and a variation that is clearly underperforming gets less. The adjustments we make are based on a statistical formula (see the appendix if you want details) that considers sample size and performance metrics together, so we can be confident that we’re adjusting for real performance differences and not just random chance. As the experiment progresses, we learn more and more about the relative payoffs, and so do a better job in choosing good variations.

Benefits

Experiments based on multi-armed bandits are typically much more efficient than "classical" A-B experiments based on statistical-hypothesis testing. They’re just as statistically valid, and in many circumstances they can produce answers far more quickly. They’re more efficient because they move traffic towards winning variations gradually, instead of forcing you to wait for a "final answer" at the end of an experiment. They’re faster because samples that would have gone to obviously inferior variations can be assigned to potential winners. The extra data collected on the high-performing variations can help separate the "good" arms from the "best" ones more quickly.
Basically, bandits make experiments more efficient, so you can try more of them. You can also allocate a larger fraction of your traffic to your experiments, because traffic will be automatically steered to better performing pages.

Examples

A simple A/B test

Suppose you’ve got a conversion rate of 4% on your site. You experiment with a new version of the site that actually generates conversions 5% of the time. You don’t know the true conversion rates of course, which is why you’re experimenting, but let’s suppose you’d like your experiment to be able to detect a 5% conversion rate as statistically significant with 95% probability. A standard power calculation ¹ tells you that you need 22,330 observations (11,165 in each arm) to have a 95% chance of detecting a .04 to .05 shift in conversion rates. Suppose you get 100 visits per day to the experiment, so the experiment will take 223 days to complete. In a standard experiment you wait 223 days, run the hypothesis test, and get your answer.

Now let’s manage the 100 visits each day through the multi-armed bandit. On the first day about 50 visits are assigned to each arm, and we look at the results. We use Bayes' theorem to compute the probability that the variation is better than the original². One minus this number is the probability that the original is better. Let’s suppose the original got really lucky on the first day, and it appears to have a 70% chance of being superior. Then we assign it 70% of the traffic on the second day, and the variation gets 30%. At the end of the second day we accumulate all the traffic we’ve seen so far (over both days), and recompute the probability that each arm is best. That gives us the serving weights for day 3. We repeat this process until a set of stopping rules has been satisfied (we’ll say more about stopping rules below).

Figure 1 shows a simulation of what can happen with this setup. In it, you can see the serving weights for the original (the black line) and the variation (the red dotted line), essentially alternating back and forth until the variation eventually crosses the line of 95% confidence. (The two percentages must add to 100%, so when one goes up the other goes down). The experiment finished in 66 days, so it saved you 157 days of testing.

Figure 1. A simulation of the optimal arm probabilities for a simple two-armed experiment. These weights give the fraction of the traffic allocated to each arm on each day.

Of course this is just one example. We re-ran the simulation 500 times to see how well the bandit fares in repeated sampling. The distribution of results is shown in Figure 2. On average the test ended 175 days sooner than the classical test based on the power calculation. The average savings was 97.5 conversions.

Figure 2. The distributions of the amount of time saved and the number of conversions saved vs. a classical experiment planned by a power calculation. Assumes an original with 4% CvR and a variation with 5% CvR.

But what about statistical validity? If we’re using less data, doesn’t that mean we’re increasing the error rate? Not really. Out of the 500 experiments shown above, the bandit found the correct arm in 482 of them. That’s 96.4%, which is about the same error rate as the classical test. There were a few experiments where the bandit actually took longer than the power analysis suggested, but only in about 1% of the cases (5 out of 500).

We also ran the opposite experiment, where the original had a 5% success rate and the the variation had 4%. The results were essentially symmetric. Again the bandit found the correct arm 482 times out of 500. The average time saved relative to the classical experiment was 171.8 days, and the average number of conversions saved was 98.7.

Stopping the experiment

By default, we force the bandit to run for at least two weeks. After that, we keep track of two metrics.
The first is the probability that each variation beats the original. If we’re 95% sure that a variation beats the original then Google Analytics declares that a winner has been found. Both the two-week minimum duration and the 95% confidence level can be adjusted by the user.

The second metric that we monitor is is the "potential value remaining in the experiment", which is particularly useful when there are multiple arms. At any point in the experiment there is a "champion" arm believed to be the best. If the experiment ended "now", the champion is the arm you would choose. The "value remaining" in an experiment is the amount of increased conversion rate you could get by switching away from the champion. The whole point of experimenting is to search for this value. If you’re 100% sure that the champion is the best arm, then there is no value remaining in the experiment, and thus no point in experimenting. But if you’re only 70% sure that an arm is optimal, then there is a 30% chance that another arm is better, and we can use Bayes’ rule to work out the distribution of how much better it is. (See the appendix for computational details).

Google Analytics ends the experiment when there’s at least a 95% probability that the value remaining in the experiment is less than 1% of the champion’s conversion rate. That’s a 1% improvement, not a one percentage point improvement. So if the best arm has a conversion rate of 4%, then we end the experiment if the value remaining in the experiment is less than .04 percentage points of CvR.

Ending an experiment based on the potential value remaining is nice because it handles ties well. For example, in an experiment with many arms, it can happen that two or more arms perform about the same, so it does not matter which is chosen. You wouldn’t want to run the experiment until you found the optimal arm (because there are two optimal arms). You just want to run the experiment until you’re sure that switching arms won’t help you very much.

More complex experiments

The multi-armed bandit’s edge over classical experiments increases as the experiments get more complicated. You probably have more than one idea for how to improve your web page, so you probably have more than one variation that you’d like to test. Let’s assume you have 5 variations plus the original. You’re going to do a calculation where you compare the original to the largest variation, so we need to do some sort of adjustment to account for multiple comparisons. The Bonferroni correction is an easy (if somewhat conservative) adjustment, which can be implemented by dividing the significance level of the hypothesis test by the number of arms. Thus we do the standard power calculation with a significance level of .05 / (6 - 1), and find that we need 15,307 observations in each arm of the experiment. With 6 arms that’s a total of 91,842 observations. At 100 visits per day the experiment would have to run for 919 days (over two and a half years). In real life it usually wouldn’t make sense to run an experiment for that long, but we can still do the thought experiment as a simulation.

Now let’s run the 6-arm experiment through the bandit simulator. Again, we will assume an original arm with a 4% conversion rate, and an optimal arm with a 5% conversion rate. The other 4 arms include one suboptimal arm that beats the original with conversion rate of 4.5%, and three inferior arms with rates of 3%, 2%, and 3.5%. Figure 3 shows the distribution of results. The average experiment duration is 88 days (vs. 919 days for the classical experiment), and the average number of saved conversions is 1,173. There is a long tail to the distribution of experiment durations (they don’t always end quickly), but even in the worst cases, running the experiment as a bandit saved over 800 conversions relative to the classical experiment.

Figure 3. Savings from a six-armed experiment, relative to a Bonferroni adjusted power calculation for a classical experiment. The left panel shows the number of days required to end the experiment, with the vertical line showing the time required by the classical power calculation. The right panel shows the number of conversions that were saved by the bandit.

The cost savings are partially attributable to ending the experiment more quickly, and partly attributable to the experiment being less wasteful while it is running. Figure 4 shows the history of the serving weights for all the arms in the first of our 500 simulation runs. There is some early confusion as the bandit sorts out which arms perform well and which do not, but the very poorly performing arms are heavily downweighted very quickly. In this case, the original arm has a "lucky run" to begin the experiment, so it survives longer than some other competing arms. But after about 50 days, things have settled down into a two-horse race between the original and the ultimate winner. Once the other arms are effectively eliminated, the original and the ultimate winner split the 100 observations per day between them. Notice how the bandit is allocating observations efficiently from an economic standpoint (they’re flowing to the arms most likely to give a good return), as well as from a statistical standpoint (they’re flowing to the arms that we most want to learn about).

Figure 4. History of the serving weights for one of the 6-armed experiments.

Figure 5 shows the daily cost of running the multi-armed bandit relative to an "oracle" strategy of always playing arm 2, the optimal arm. (Of course this is unfair because in real life we don’t know which arm is optimal, but it is a useful baseline.) On average, each observation allocated to the original costs us .01 of a conversion, because the conversion rate for the original is .01 less than arm 2. Likewise, each observation allocated to arm 5 (for example) costs us .03 conversions because its conversion rate is .03 less than arm 2. If we multiply the number of observations assigned to each arm by the arm’s cost, and then sum across arms, we get the cost of running the experiment for that day. In the classical experiment, each arm is allocated 100 / 6 visits per day (on average, depending on how partial observations are allocated). It works out that the classical experiment costs us 1.333 conversions each day it is run. The red line in Figure 5 shows the cost to run the bandit each day. As time moves on, the experiment becomes less wasteful and less wasteful as inferior arms are given less weight.

Figure 5. Cost per day of running the bandit experiment. The constant cost per day of running the classical experiment is shown by the horizontal dashed line.

¹The R function power.prop.test performed all the power calculations in this article.
²See the appendix if you really want the details of the calculation. You can skip them if you don’t.

Posted by Steven L. Scott, PhD, Sr. Economic Analyst, Google

Google Tag Manager: Technical Implementation Deep Dive Webinar

Google Analytics team — Tue, 22 Jan 2013 18:55:00 +0000

Just three months ago we launched Google Tag Manager to make it easier for marketers (or anyone in the organization) to add and update website tags, such as conversion tracking, site analytics, remarketing, and more. The tool provides an easy-to-use interface with templates for tags from Google and templates for other vendor’s tags, as well as customizable options for all your tagging needs. This minimizes site-coding requirements and simplifies the often error-prone tagging process.

In November, we held an introductory webinar (watch the recording here, plus read Q&A), and next week we’re holding a second webinar going beyond the basics and diving into the technical details and best practices for how to implement Google Tag Manager. This webinar will be hosted by Rob Murray, our Engineering Manager, and Dean Glasenberg, Sales Lead.

Webinar: Google Tag Manager — Technical Implementation
Date: Tuesday, January 29, 2013
Time: 10 am PST / 1pm EST / 6pm GMT
Register here: http://goo.gl/17OFd
Recommended Audience: IT or webmaster team members

During the webinar we’ll go through a step-by-step process for implementation, and we’ll cover some more advanced topics (i.e. deploying more complex tags). We’ll introduce the role of a Data Layer and use it in conjunction with Events to show how you can set up a site to gather detailed usage metrics, for example, to help you understand why users are dropping off at a specific page. We’ll also show you how common browser Developer Tools, as well as the Google Tag Manager Debug mode, can be used to help verify that your tags are working correctly (and fix them if they’re not).

Hope to see to see you on Tuesday!

Posted by Dean Glasenberg, Global Sales Lead, Google Tag Manager

Kapitall Uses Content Experiments To Drive A 44% Conversion Increase

Google Analytics team — Wed, 16 Jan 2013 18:55:00 +0000

Video game entrepreneur Gaspard de Dreuzy and financial technologist Serge Kreiker had a thought: why not use the gaming experience to break the traditional online investing mold? Their idea took hold and Wall Street firm Kapitall, Inc. was born in 2008. Based in New York, Kapitall now has 15 full-time employees providing a unique online investing platform and brokerage.

Kapitall has used Google Analytics Certified Partner Empirical Path since 2011 for analytics services on its JavaScript website. The complex implementation required custom JavaScript to allow for Google Analytics tracking within the trading interface as well as on landing pages. Empirical Path implemented Google Analytics tracking directly within the Kapitall interface so that decision makers could understand pivotal actions, such as how often brokerage accounts were being funded or where in the sign-up process potential investors were dropping out.

Challenge: Refining the landing page for maximum response

Kapitall wanted to do more than simply capture data however; they also wanted to test the content of their landing page and then optimize it by targeting visitors with messages and options that would lead to conversions. Why was creating a truly effective landing page seen to be so critical? Kapitall’s gaming-style interface enlists traders to sign up for brokerage accounts and use the site to trade stocks or create practice portfolios. Every incremental sign-up is key to the company’s success.

Approach: Split testing to identify a winning landing page

Kapitall understood that there was little point in making one-off ad hoc responses to analytics insights, or doing before-and-after comparisons that would inevitably be confounded by differences in the before and after audiences. Empirical Path recommended taking their analytics efforts to the next level with a closed-loop solution to eliminate complications and identify the best page version.

The team proposed automated experiments to compare different versions of the landing page to see which performed best among a random sample of visitors. To accomplish this, Empirical Path first set Google Analytics’ Event Tracking and Custom Variables on brokerage accounts to distinguish current customers from traders. The team then designed Content Experiments in Google Analytics to understand which version of the landing page drove the greatest number of sign-ups.

Results: A new landing page with proven success

The outcomes from the test were illuminating, clearly identifying that the Angry Birds landing page was most effective. The winning version showed a dramatic increase in sign-ups of 44 percent and a 98 percent probability that this version would continue to beat the original. “Kapitall was impressed by how quickly Content Experiments was able to zero in on the Angry Birds version,” says Jim Snyder, principal at Empirical Path Consulting. “Having the ability to quickly surface the best performing version directly resulted in attracting more investors at a faster rate, and that was a huge value-add to Kapitall.” Thanks to the split testing approach, Kapitall possesses valuable insights into the perfect blend of messaging and creative elements to optimize the page. With the strongest version now implemented, Kapitall is able to realize the true power of its online real estate.

View the entire case study as a PDF here.

Posted by the Google Analytics Team

10 Google Analytics Resolutions for 2013

Google Analytics team — Fri, 11 Jan 2013 18:33:00 +0000

The following is a guest post from Michael Loban, CMO of InfoTrust a Google Analytics Certified Partner and Google Analytics Premium Reseller based in Cincinnati, OH.

New Year’s is the ideal time for making resolutions (that we keep until the second week of January). To avoid this cliché, I decided to actually wait until the second week of January to put together my resolutions/ideas/tactics for taking Google Analytics in 2013 to the next level.

1. Address your data phobia. Maybe it is a little bit extreme to say that a lot of digital marketers have an extreme fear of web analytics data, but it is safe to assume that data is what often causes migraines. Staring at pie charts, graphs and percentages until you know what you are looking for is the wrong way to start. The remedy for data phobia is simple – ignore the data you do not need to make a marketing decision. And always remember to align your Google Analytics configuration with your business goals.

2. Assign a monetary value to your goals even if you do not sell anything on your website. Each submitted form, played video, downloaded PDF is worth something. Otherwise, why did you put it on a site? It is not enough to say that you need to decrease your bounce rate by 5%. Equate the decrease in your bounce rate by 5% to the amount of money that you can make when those visitors submit completed “contact us” forms or other micro conversions. For example, work with the email marketing team to determine the value of each new email subscriber. If you get more people to join your email list then you will be able to sell more products via email marketing.

3. Not all marketing strategies are created equal. In order to turn a prospect into a customer you might have to engage in remarketing, email marketing and social media marketing. Use attribution modeling inside Google Analytics to examine how each marketing tactic contributes to a sale/conversion. Here is a blog post from the Google Analytics team on how to get started with attribution modeling. In 2012, Attribution Modeling was only available for Google Analytics Premium customers; in 2013 it will be available across all Google Analytics.

4. For some reason, social media measurement is something companies are still unsure about. It is difficult and complex, but you have to start somewhere. Why not start with Google Analytics Social Reports? This will help you track visitors that social media channels brought to your website, measure the value of those channels by tracking conversions and examine how your content is being shared across social networks. It feels good to say that last month, 10 people from Facebook who came to your website became your customers. Learn more about Social Reports.

5. Tools are great, but great analysts are awesome. The true value of analytics is fully unlocked when you get to work on your data and turn it into something actionable. Make sure that you have a team (even if it is a team of 1) that knows how to analyze data to help you reach your marketing goals.

6. If you begin to analyze your data, and realize that you do not have enough context to make a decision, get more data. Now, you can do a cost data import and Google Analytics will display how non-Google paid search campaigns perform and compare to your other marketing campaigns. Here is a how-to article from Google Analytics on Measuring Performance of Paid Campaigns.

7. While we are on the subject of trying new things, Remarketing is something that I have promised myself to do more of. Remarketing is now available right inside Google Analytics. Here is a PDF document and a Webinar about Remarketing with Google Analytics.

8. Mobile optimization is the most exciting digital opportunity for marketers in the coming year, according to new Econsultancy report. Since this is the case, mobile analytics will become more important than ever. Segmenting and understanding your mobile visitors will help you create a winning mobile experience that will lead to conversions and sales. In October, Google Analytics announced a public beta launch for mobile app analytics.

9. Begin measuring your analytics ROI. Time that you spend on collecting, reporting and analyzing data is not free – there is an opportunity cost. In order to prove the value of analytics inside your organization, begin measuring your Return on Analytics. When you accurately collect data, and properly analyze it, you are able to make accurate marketing decisions. Measure the impact of your analytics.

10. ACTION! This is a common phrase on any movie set. This should become a common phrase for everyone who uses Google Analytics. Turn your data into actionable marketing reports and smart dashboards that will help with the analysis. When you see true data analysis, you will want to scream ACTION! This means that the data and data analysis are so clear and crisp that you know exactly what needs to be done to reach your marketing objectives. Don’t settle for anything less. DATA, ANALYSIS, ACTION!

Happy New Year!

Must-Have Analytics Customizations for Any Business

Google Analytics team — Thu, 10 Jan 2013 18:58:00 +0000

The following is a guest post by Mike Pantoliano, a web marketing consultant at Distilled in Seattle.

Out of the box, Google Analytics is really powerful. It's amazing how much awesome data we have at our fingertips by just implementing a couple of lines of code across our entire site. Having worked in an agency setting for a number of years now, I'm fortunate to have overseen hundreds of various web sites' Google Analytics implementations. And while no business' analytics needs are the same, I've found there are a few must-have customizations that can be applied across almost any GA implementation.

While the following tips will help you get more out of Google Analytics, there's no replacing a solid understanding of how Google Analytics operates by default. I consider this post a successor to Daniel Waisberg's 5 Ways To Ensure Google Analytics Is Running Perfectly and Simply Business's Small Business Guide to Google Analytics. Once you've got a good hold on how things work, give some of the following a shot in your accounts.

Build a Branded RegEx

Regular expressions can be scary, but in many cases this will only have to be done once. Once you have one built it can be applied to advanced segments or multi-channel funnel channel groupings to get a really enlightening look at how visitors coming from non-branded keywords are interacting with your site. If you're actively trying to grow your traffic from search, the biggest gains can be had from visitors that do not yet know your brand.

Even if you're not a RegEx pro, your Google Analytics keyword report will allow you to tinker until you get it just right. Once you have some of the basics down, you can begin to build your branded RegEx:

Head to your keywords reports and click advanced

Begin to build your RegEx. Simply typing in your brand name would be a good start.

Watch out for brand-name-less keywords that are technically still branded. For instance, Distilled's conference brand would still appear in our keyword reports with our original RegEx.

Make adjustments as necessary to your RegEx using pipes ("|") to indicate an OR, and other RegEx operators like "?", "*", and parentheses.

Now you can apply to your advanced segments and compare behavior and conversion data.

Or create custom channels in your multi-channel funnel reports.

And speaking of MCF channel groupings…

Create Custom MCF Channel Groupings

The world of digital measurement is increasingly becoming aware of the fact that the customer journey is far too complicated to work solely off of last touch attribution. Many marketing channels like social, display advertising, and organic search (especially non-branded) inherently act as "exposers". Looking only at last touch attribution isn't fair to these channels that are bringing potential customers in the door. This is the problem that the multi-channel funnels reports and the (soon to be released for everyone) attribution modeling tool are built to solve.

But those reports are only as good as the input channel segmentation. By default, Google Analytics offers a solid basic channel grouping from which to work. Right off the bat I like to create a copy of the default channels, and customize for the site I'm analyzing.

Now I can create custom channels. This will vary greatly between websites, but the following are some channel ideas that might be useful:

The aforementioned non-branded and branded channels
Separate out partner sites or special relationships from the default "referral" channel into their own group.
An affiliate channel
Separated social network channel. Perhaps separate channels for just the channels you're active on (Pinterest, Google+, Twitter, etc.). Or maybe pulling out any networks that are used in the "closer" role (say, if you're mainly using Twitter to post coupon codes).
A channel for a subset of your visitors that were exposed to a specific portion of your site before anything else. Check out Josh Braaten's How to Prove the Value of Content Marketing with Multi-Channel Funnels for a great example of this.

Once you've built your custom channels, take a look at the assisted conversion reports. Watch for channels with high assisted/last interaction conversion ratios. Those are your exposers! They've been acting mostly in the assist role and might deserve a bit more credit than they've been getting with standard last touch attribution.

Build a Custom Dashboard or Two (or 10!)

Dashboards are a great way to create an at-a-glance snapshot of what matters. It's here where we'll be able to make sure everything is operating as normally, all in one view. Any general marketing dashboard worth its weight in pixels will include a 10,000 feet look at acquisition, behavior, and outcomes. How is traffic? How is time on site/bounce rate/etc.? How are conversions/revenue?

Larger organizations may have stakeholders in various parts of your business that would love a 10,000 foot view of the metrics that matter to them. Got a team that runs the blog? Build a dashboard that offers a view of dimensions like top landing pages and entrance keywords, as well as metrics like bounce rate and comments (set up as a goal). Need a view for just the C-level folks? Build one with revenue, overall site traffic, and time on site.

The previously mentioned Simply Business "Small Business Guide to Google Analytics" includes an example dashboard that can be copied into your account and modified as needed.

Setup Custom Intelligence Data Alerts

Even with daily checkups on your site's health, sometimes major problems can go unnoticed until it's too late. Enter custom data alerts. This handy feature lets you define triggers that will alert you via email or text message should a given threshold be passed. It's really easy to setup alerts for site wide drops in traffic, conversions, revenue, etc.

And we can take it a step further and apply our triggers to a subset of our site's traffic, for example:

drop in traffic from search
increase in bounce rate from direct
drop in conversions from example.com
drop in impressions from ppc

And even more advanced:

One client I've worked with was sending events whenever an error was triggered in their checkout process. With custom data alerts, it's then totally possible to get an alert whenever there's an increase in checkout error events.

Both Luna Metrics and Justin Cutroni wrote some great posts on data alerts if you'd like more ideas:

Wrapping Up

These are just some of the most common enhancements I make to GA's out-of-the-box setup. Even after the above, there's so much more that can be tweaked as necessary to make for the perfect analysis reports, the possibilities are endless. I didn't even touch on filters, custom reports, and advanced segments! And now with even more features like cost analysis, dimension widening, and Universal Analytics being rolled out the possibilities will be even more endless-er.

What are your go-to Google Analytics customizations?

Insights for 2013: Understanding Your Customers & The Full Value of Digital

Google Analytics team — Wed, 09 Jan 2013 18:16:00 +0000

We’re just a week into 2013, and we’re undeniably living in the new multi-screen age. Our day-to-day interactions with technology—and our expectations—have increased dramatically. We’re no longer content to wait until later to buy shoes or schedule travel or find a hot spot to eat. Technology lets us act now, and we expect reliable results. In fact, we’ve become so dependent on being connected all the time that 43% of U.S. adults would be willing to give up beer for a month if it meant they could keep accessing the Internet on their smartphones, and 36% said they’d be willing to give up chocolate.*

What does this mean for you? Many businesses have fallen behind consumer behavior—in a world where people look first to mobile devices and real-time streams, the digital journey has grown more complex, and it’s become more challenging to gain a clear picture of these interactions. As a marketer or analyst, your success depends on adapting to this new reality. We’re working to provide tools that let you connect the dots, so you can regain visibility into your customers’ preferences and behaviors and take advantage of the full value of digital.

Holistic Measurement: Capture the full customer experience
Your customers are active all the time and everywhere, so you should be as well. To truly win moments that matter across all screens and situations, you must acknowledge, and measure, all relevant touch-points. For example, are you running related marketing efforts across email, display, and search ads? Do you see customers performing some tasks more frequently on smartphones while other tasks are more common on laptop? Rather than evaluating these programs and behaviors separately, your measurement strategy should focus on connecting the dots between these consumer moments. Consider your overall business goals and then measure the role that each touch-point plays in achieving those goals, keeping in mind the complementary effects of multiple channels and devices.

Active measurement: Plan ahead and optimize throughout
Harnessing the power of location, intent and social connections is possible today, and smart marketers are always on, always optimized—using customer signals to create winning experiences for their brand. Active measurement is about quickly acting on the insights you uncover as you go, but more importantly, it’s about good measurement planning. This proactive piece is often overlooked, even by the savviest marketers and analysts. To effectively measure, you must define expectations about how your customer will interact with your brand before you look at the numbers. Then, question any surprising results. Is it normal or strange for your business to have a 3% conversion rate? Is it good or bad if event tracking shows that 3,000 people view a video on your homepage in a single day? Are your TV spots driving viewers to your website in Real-Time, or do you see more traffic at other times? Are you bringing together data sources and devices holistically? Active measurement means examining real human behavior and influences instead of reporting the same old data on clicks or bounce-rates.

Media-Agnostic Measurement: Give credit where it’s due
As technology usage has expanded, patterns of research and influence have become more diverse (with many consumers consulting 10 or more sources before a purchase)*—but also more identifiable. In the new digital age, it’s no longer realistic or smart to judge campaigns solely by the final interaction using a last-click model, or to think only about single-device or single-session interactions. Customers move fluidly across channels and devices. To truly understand the value of your digital investments, you have to overhaul your conversion goals to capture all the large and small behaviors that lead to business success. Once you have visibility into the customer journey, you can begin assigning credit with attribution models to determine your best channel and investment mix. Next, optimize your programs and run controlled experiments to see how you could improve your results even further. It’s an active, ongoing process.

So, make a New Year’s resolution to take a more proactive approach: consider and give credit to all the interactions in the customer journey, and act on your measurement insights. Throughout 2013 we’ll be sharing practical advice to help you dive in and join your customers in the always-on world. Good luck!

Posted by Paul Muret, Director of Engineering, Google Analytics

*Source for statistics in this post: http://www.thinkwithgoogle.com/insights/facts/marketing-objective/

Video: Remarketing Webinar and Q&A

Google Analytics team — Wed, 19 Dec 2012 17:54:00 +0000

Last Wednesday we held a webinar on Remarketing with Google Analytics. We launched this feature earlier this year to help you reconnect with your site visitors in relevant ways. Remarketing with Google Analytics lets you show ads to website visitors who have shown an interest in your site as they browse other sites on the Google Display Network (GDN). So you can reach the right audience with the right message at the right time.

Watch the webinar video here to learn more about:

The overall benefits of Remarketing with Google Analytics
See a live demo of the product
Understand how to set this up for your business
And see some key examples of what’s possible

Read on for responses to some of the top questions we received during the webinar:

Any quick tips for getting started?
Yes, our help center includes a great guide with everything you need to know to get started.

Is there a limit on the number of lists that you can create in your Google Analytics account?
No! We want to encourage you to create as many lists as you need to run an effective remarketing campaign.

How should I set “membership duration” for my lists?
The default membership duration is 30 days, but we recommend choosing a duration related to the length of time you expect your ad to be relevant to the user. Learn more about membership duration in this article in the AdWords Help Center.

How can remarketing lists in Google Analytics be edited or deleted?
It’s easy to edit existing lists by clicking on the name of the list in the main table. Visitors who have already been added to the list will be removed from the list when the list duration for those visitors expires.

Both AdWords and Analytics save lists for historical campaign reporting purposes, so it’s not currently possible to delete lists -- but often you can simply edit your old lists so they continue to be useful. That said, we are looking into ways to provide better controls for managing lists that are no longer in use such as providing ways to hide or archive old or unused lists.

Can you use Google Tag Manager with Remarketing with Google Analytics?
Yes! Google Tag Manager fully supports Remarketing with Google Analytics. When you are setting up your “Google Analytics” tag templates in the Google Tag Manager User Interface, you can choose to enable the “Add Display Advertiser Support” check box-- this will make all the tagging changes necessary to use Remarketing with Google Analytics.

Can you share lists between Google Analytics profiles? What about across different AdWords accounts?
When you create a remarketing list in Google Analytics, you must choose to base it off of a single, specific Profile (a Google Analytics Profile determines which data from your site appears in the reports; it may, for example, include filters to eliminate traffic from internal users). If you want to create a list that’s based off of two profiles, you must create that list twice -- once for each Profile. Similarly for AdWords accounts, if you want to share a list with more than one account, you must create the list once for each account you want to share it with.

Do you have examples of remarketing lists I might consider creating with Google Analytics?
Yes, you can find some examples in the webinar video and in on our product fact sheet, and we’re working on providing more examples and tips. Stay tuned!

We hope you found this webinar useful -- and that you go start creating your first remarketing lists using Google Analytics now.

Posted by Jesse Savage, Product Manager, Google Analytics

Extract Insights Across Datasets with SumAll

Google Analytics team — Tue, 18 Dec 2012 18:30:00 +0000

Businesses collect and rely on data that exists in silos across the web - from site analytics to inventory numbers, social media to sales data, there’s more important data available today than most are able to aggregate and analyze themselves.

SumAll is a connected data platform that enables business operators from companies of all sizes to visualize their mission-critical data through one centralized location. Users of SumAll can extract insights across datasets by combining and analyzing the metrics that matter most to them. “Put simply, our vision is to democratize information by making it beautiful, affordable and accessible to all. In doing so, the visibility and insights that SumAll brings enables business operators to turn data into dollars,” says Catherine Gluckstein, President of SumAll.

One of SumAll’s customers was having a very difficult time making sense of his eCommerce, Google Analytics and social media data. He knew there was a story to be told about how each was influencing the other, but being a small business owner, he lacked the resources to dive too far into them himself. He decided to give SumAll a try and within a few minutes and even fewer clicks, was able to integrate all of his key data and view it in one uniform dashboard without having to work with his developers.

For the first time, he was able to see what was happening across his business and understand the relationship between his social media posts, web traffic and transactions. This made him more comfortable continuing to invest his limited resources in social media because, for the first time, he could see that it was working.

SumAll integrates with all major components of the eCommerce ecosystem including payment processors, social platforms, shopping carts, online marketplaces and, of course, Google Analytics. “It only took us about 6 weeks to complete our integration with Google Analytics, from concept to go live,” according to Catherine. “After our customer completes the authentication and authorization process, we ingest their data into SumAll and normalize it to make it available to all SumAll applications across web, mobile and email.”

SumAll is free to try and is incredibly intuitive and straightforward to set-up. Sign-Up today to break down the silos around your data and empower your business’ data-driven decisions today.

Posted by John Milinovich

John is a Developer Program Manager working to build the ecosystem around the Google Analytics APIs. In his spare time he likes to explore San Francisco and cheer loudly during UCLA games.

Google Analytics in Real Life: What would your customer experience look like?

Google Analytics team — Thu, 13 Dec 2012 14:33:00 +0000

With the holiday shopping season in full swing, it’s important to ensure your website and digital marketing are running on all cylinders. Your potential customers should be able to find what they need on the digital shelf as easily as in real life. Sadly, many sites leave visitors frustrated - losing potential customers. However, the advantage of your online storefront is that you can understand where you’re losing customers and work to improve your shopping experience.

For the holiday season, our team at Google Analytics thought it would be helpful (and fun) to demonstrate how missteps on the digital shelf play out in real life.

What’s distracting your customers?
Have you accidently placed obstacles directly in the path of your customers buying what they really want on your site? Watch Nick's journey to finding what he wants. Play Video
Improvement Tip:
Always make sure your landing pages meet your users' expectations. Be sure your ad text leads visitors to a page that matches what was featured in the ad. Here is a helpful article on ways to improve the performance of your landing pages.

How can it be so challenging to find your favorite type of milk?

Are you making it difficult for users to browse or search your site by the way you categorize your products? Watch as Oli struggles to find his breakfast essentials. Play Video

Improvement Tip:
A search box can be a goldmine of information because each time visitors search your site, they tell you in their own words what they are looking for. Here is an article on insights available from your Google Analytics Site Search reports to learn what your visitors want so you can improve your website to better meet those needs.

When do visitors check out from your online buying process?
We shared this last year, but it’s too much fun not to share again. Great example of the importance of having a simple easy to use checkout process on your website. Watch for the humor, stay for the insights. Play Video
Improvement Tip:
Are there some product pages that consistently send higher traffic through your shopping cart than others? See if there are differences between the page designs that might be driving the difference in traffic volume. Do the better performing pages offer more information about their products, more customer reviews, explain shipping options or provide more options for visualizing the products before adding them to the shopping cart? The Google Analytics goal flow visualization can help to identify these better performing pages to repeat their success.

Ready to learn more about how to improve your online customer experiences? Check out these Google Analytics resources:
- Article: Improve the performance of your landing pages
- 5 questions to ask of your Site Search data
- Understand the path or missteps visitors take to completing your goals with flow analysis

We hope this helps you to find more way to use Google Analytics to make your customers' lives easier, and generate more happy and loyal customers for you - now that’s a holiday present worth giving.

Posted by Clancy Childs, Google Analytics Product Manager
& Jon Day, Google Product Marketing Manager

Pull Analytics Data Into Tableau With New Connector

Google Analytics team — Wed, 12 Dec 2012 17:19:00 +0000

The following is a guest post from Ellie Fields. Ellie is the Director of Product Marketing at Tableau Software, responsible for new product launch, industry solutions and Tableau's community. Her data geek credentials come from time served in technology and finance companies. She works with people from all over the world who are trying to tell stories with data, from journalists to hospitals to high tech companies.

Over at Tableau Software we’re big Google Analytics users. That’s why we got so excited for Tableau’s new Google Analytics connector, which uses the GA API to pull data right into Tableau.

If you don’t know Tableau, consider checking it out today. It’s a useful, new way of working with data: you simply drag & drop to create sophisticated analyses. Anyone can create powerful dashboards without needing to know programming or be a specialist. Tableau was spun out of Stanford about 8 years ago, and it’s the technology developed there—a visual interface into data—that makes Tableau different.

So what can you do with GA data and Tableau? Well, for one thing you can connect directly to GA to get your data. You can create custom dashboards. And you can extend GA data with new calculations. Here’s a video of how we used the Tableau GA connector along with some Excel data to understand our own website better:

You might have noticed one special feature in there: the ability to blend Google Analytics data with other data, like data in Excel or a database. This opens up a whole new world of insight. Your website is a great asset, and of course it doesn’t work alone. Mashing up web data with offline data, demographics and more tells you more about what’s working and what’s not.

This is just a preview of the Google Analytics connector. It’ll be in beta very soon. If you’re a customer you can join the v8 beta when it comes out. If you’re not yet using Tableau, try it out (for free).

Posted by Ellie Fields, Director of Product Marketing at Tableau Software

Segment Your GA Data by Demographics with UserReport

Google Analytics team — Tue, 11 Dec 2012 17:30:00 +0000

One of the most complex challenges that marketers face is managing the effective segmentation of their user base. Each of their target audiences has a different set of preferences and the process of creating campaigns based on intuition just isn't effective.

UserReport is an on-site survey tool that integrates with Google Analytics and tackles this problem head-on. The product providing the ability to use demographic information and traditional research data to optimize acquisition, content and conversions when working with websites.

UserReport helps its users collect information about their website’s visitors with a free online survey tool that measures usability and key demographics of the site’s users. The product integrates harmoniously with Google Analytics to turn the survey data they collect into actionable insights by merging it with the behavioral data already stored in Google Analytics.

SAXO.com is one of the largest online book stores in Denmark and utilizes UserReport to identify their highest value demographic segments, create more targeted advertising material and to better understand which online advertising networks they should use for targeting specific groups of customers. By using UserReport, SAXO.com was able to uncover some surprising insights about their customers, including:

Men and women have about the same conversion rate, but the average basket size for women is almost $20 higher than it is for men. This made SAXO.com feel more comfortable in supporting a higher CPM/CPC to advertise to niche female audiences.
SAXO.com’s older book buyers have a higher conversion rate than their younger counterparts but the younger buyers’ average basket size is about $40 more than the older users’. A closer investigation revealed that most of these young customers were students purchasing books for classes. This led SAXO.com to focus on targeting the university student market to bring more young buyers into the mix.

The findings made by SAXO.com through integrating their Google Analytics data with their UserReport survey data has enabled them to create online campaigns focused on bundling unique, focused products and target them at the right customers on the right channels to drive conversions.

UserReport is free to use and takes minutes to set up. Give it a try to see what you can uncover about your own online audience!

Gilt embraces insights from Analytics at an enterprise level

Google Analytics team — Fri, 07 Dec 2012 17:39:00 +0000

A little over a year ago we launched Google Analytics Premium to help better meet the needs of our enterprise users. In that time we’ve been happily surprised by the warm reception and how companies have been using Google Analytics Premium to look at data in a new way. Below is a case study from Gilt, on how Google Analytics Premium has spread the love of data across their company, they leveraged the increased number of custom variables to power their predictive modeling, and used unsampled data to remove uncertainty from test results.

Gilt Groupe is an innovative online shopping destination offering its members special access to the most inspiring merchandise and experiences available. Gilt provides instant insider access to top designer brands at up to 60% off retail. Products span fashion, decor, artisanal ingredients, travel experiences, and unique activities in a growing list of cities. The bottom line for Gilt is that Google Analytics Premium has provided the ability to make better, faster data-driven decisions at every single level of the organization. Read the full case study.

“Google Analytics Premium has given everyone at Gilt quick, easy access to insights about our business. It has enabled true ‘self-service’ data across the company.”

- Ana Kravitz, Web Analytics Senior Manager

Gilt Groupe

Google Analytics Premium provides enterprise level analytics with access to more data, flexibility and 24/7 support. The benefits of Premium are guaranteed SLA’s on data collection, reporting and processing times. Premium accounts also get an increase in the number of hits per month, an additional 50 custom variables, and access to unsampled data. Premium accounts also gain access to customer support including an implementation review, quality assurance, training, and a dedicated account manager.

Google Analytics Premium is currently available in the United States, United Kingdom and Canada. Looking towards 2013 Google Analytics Premium will continue to expand our product and services to meet the variety of Analytics enterprise customer needs. We’ll soon be popping up in 7 more countries: Japan, Brazil, France, Germany, Netherlands, Italy and Spain.

If you would like to learn more about Google Analytics Premium and how it can help your business, contact the Google Analytics sales team or one of our Google Analytics Premium Authorized Resellers.

Clancy Childs
Google Analytics Premium Team

Tagging just got easier: Built-in templates for popular tags in Google Tag Manager

Google Analytics team — Thu, 06 Dec 2012 22:03:00 +0000

One of our favorite features of Google Tag Manager is the ability to add new tags to your site using a tag template instead of copying-and-pasting code — and we’ve just made tagging even easier with several new built-in tag templates. Just add a few key details to the template, and Google Tag Manager will automatically generate the correct code.

We’ve teamed up with a variety of companies to provide our first wave of Tag Vendor templates, including:

comScore Unified Digital Measurement tag
Turn Conversion Tracking and Data Collection tags
Media6Degrees Universal tag
Bizo Business Data Insight Tag
Neustar AdAdvisor tag

This is just the first wave of supported tags, and you can look forward to many more coming soon. If you have specific requests, we’d love to hear them in our Google Tag Manager Forum in the Feature Requests section.

If you’re a tag vendor, and you’d like to get your tag supported in Google Tag Manager through the Tag Vendor Program, follow the instructions here to get started. And thanks to all of our partners for your support and involvement with Google Tag Manager!

Posted by Laura Holmes, Product Manager, Google Tag Manager

Analytics reporting with Google Apps Script at the UK Cabinet Office

Google Analytics team — Thu, 06 Dec 2012 17:47:00 +0000

Guest author Ashraf Chohan works at the Government Digital Service (GDS), part of the UK Cabinet Office. Originally posted on the Google Apps Developer Blog by Arun Nagarajan.

Recently, when we were preparing the launch of GOV.UK, my team was tasked with creating a series of high-level metrics reports which could be quickly compiled and presented to managers without technical or analytical backgrounds. These reports would be sent daily to ministers and senior civil servants of several government departments, with the data customised for each department.

We decided to use Adobe InDesign to manage the visual appearance of the reports. InDesign’s data-merge functionality, which can automatically import external data into the layout, made it easy to create custom departmental reports. The challenge was to automate the data collection using the Google Analytics API, then organize the data in an appropriate format for InDesign’s importer.

In a previous post on this blog, Nick Mihailovski introduced a tool which allows automation of Google Analytics Reporting using Google Apps Script. This seemed an ideal solution because the team only had basic developer knowledge, much of the data we needed was not accessible from the Google Analytics UI, and some of the data required specific formatting prior to being exported.

We started by building the core reports in a Google spreadsheet that pulls in all of the required raw data. Because we wanted to create daily reports, the start and end dates for our queries referenced a cell which defaulted to yesterday’s date [=(TODAY())-1].

These queries were dynamically fed into the Google Analytics API through Apps Script:

// All variables read from each of the “query” cells  
var optArgs = {
    'dimensions': dimensions,              
    'sort': sort
    'segment': segment
    'filters': filters,         
    'start-index': '1',
    'max-results': '250'                    
  };

  // Make a request to the API.
  var results = Analytics.Data.Ga.get(
      tableId,                  // Table id (format ga:xxxxxx).
      startDate,               // Start-date (format yyyy-MM-dd).
      endDate,                 // End-date (format yyyy-MM-dd).
      endDate,                 // Comma seperated list of metrics.
      optArgs);

Next, we created additional worksheets that referenced the raw data so that we could apply the first stage of formatting. This is where storing the data in a spreadsheet really helps, as data formatting is not really possible in the Google Analytics UI.

For example, the final report had a 47-character limit for page titles, so we restricted the cells in the spreadsheet to 44 characters and automatically truncated long URLs by appending “...”.

Once the initial formatting was complete, we used formulas to copy the data into a summary sheet specially laid out so it could be exported as a CSV file that merges seamlessly into InDesign.

Below is an example of how a report looks on publication. Nearly everything on the page was extracted from the API tool, including the department name and the day number. Because most of the data was automated, it required minimal effort on our part to assemble these reports each morning.

We discovered that an added bonus of pulling data into a Google spreadsheet was that it also allowed us to publish the data to a Google site. This helped us display data to stakeholders without adding lots of users to our Google Analytics account.

The tools let us present Google Analytics data in deeper, more creative ways. That’s really important as we share information with more and more non-technical people, whether they’re inside GDS or beyond.

Posted by John Milinovich, Google Analytics team

Webinar Dec 12: Remarketing with Google Analytics

Google Analytics team — Wed, 05 Dec 2012 21:05:00 +0000

This summer, we launched Remarketing with Google Analytics, a new way to take advantage of the data you have in Google Analytics and use it to improve your campaign performance. Remarketing lets you reconnect with site visitors and show them ads that are tailored to their interests across the Google Display Network. This makes ads on the web more relevant for consumers while helping to improve your advertising ROI.

Join us next Wednesday, December 12, for an introductory webinar hosted by Product Manager Jesse Savage and Global Solutions Lead Rachel Witalec. This webinar is part of our recent Learn with Google series on Remarketing. We’ll cover the benefits of remarketing for your business, unique advantages of Remarketing with Google Analytics, sample use cases, and the key steps for getting started.

Webinar: Remarketing with Google Analytics
Date: December 12, 2013
Time: 10 am PST / 1pm EST / 6pm GMT
Register here: http://goo.gl/B49ma

Hope to see you at the webinar!

Posted by Sara Jablon Moked, Product Marketing Manager

5 Ways To Ensure Google Analytics Is Running Perfectly

Google Analytics team — Thu, 29 Nov 2012 17:29:00 +0000

The following is a guest post contributed by Daniel Waisberg, Owner of Conversion Journey, a Google Analytics Certified Partner, and Founder of Online Behavior, a Marketing Measurement and Optimization portal.

Abraham Lincoln once said: "If I had eight hours to chop down a tree, I'd spend six hours sharpening my axe." The same is true for measurement: it is of extreme importance to spend the necessary time thinking through which data should be collected and whether the collection works as planned (once implemented). Very often, the implementation model and quality assurance do not receive the proper attention.

I recently wrote a short eBook named Google Analytics Implementation Best Practices that covers some of the most important configurations you should setup. But in this post I will go through some techniques that will help you to make sure your Google Analytics implementation is working as you expect.

1. Create a "Raw Data" profile

The best way to check configuration errors is to have a profile that does not use any filters, this way you will be able to quickly learn if you have a misplaced or problematic filter. Here is a quick guide explaining how to create profiles.

Once you create this profile, I do recommend you create the same goals you have in your main profile, this will make the data more relevant in case you need to use it. For example, if you find out that your main profile have a filter that affected your past data, you might want to use the Raw Data profile for a while. To copy and paste a goal between profiles you can use the Chrome extension GA Copy and Paste.

2. Use Real Time Reports

In October 2012 Real Time reports started supporting profile filters. This means that "the data you see in real time is profile specific and obeys the filtering you set up for that profile. And this means any user with access to a profile can view the associated real time reports." This enables many interesting analysis opportunities like seeing real traffic for only small pre-defined segments.

In order to use Real Time to check your Google Analytics implementation, you should first create a new profile (see link above). Then, add a filter that includes the IP address of your company; learn how to do it in this help article, but make sure to change the filter from "exclude" to "include". Now you will be able to look at the Real Time reports of this profile and see what you are doing in real time, which makes code checks much easier and faster.

3. Keep Track Of Configuration Changes

One of the common configuration problems is a lack of communication, especially for large companies. From a few people to a few dozen people will have Admin access to Google Analytics, which means they can change the settings of any profile. This can lead to unwanted or misunderstood changes in the account.

By "changes" I mean goal refinements, filter improvements, new features, and so forth. Every change may impact data in several ways, and for this reason it is essential to have a system in place to keep track of code and profile changes. In order to facilitate/centralize the collection and sharing of the changes made to a Google Analytics account, I propose two different methods: using a Google Docs form & taking advantage of the Annotations feature. Please note that each company should find the optimal mix between these methods.

Using a Google Docs Form

The big advantage of Google Docs is that it can be shared with as many people as needed and everyone has access to the most updated version of the document. I recommend creating a Google Form (learn how) that will output its data into a spreadsheet. The form should be created so that all interested parties can be aware of all changes. These will then be aggregated for historical knowledge that can be used by the whole team (and future teams members). See one sample form that can be used by Analytics teams in this article.

Google Analytics Annotations

This feature allows website managers, marketers and developers to provide context directly from inside the graphs on the interface, allowing for richer analyses. Here are some important occasions when you should use this feature:

Offline marketing campaigns (e.g. radio, TV, billboards.)
Major changes to the website (e.g. design, structure, content.)
Changes to tracking (e.g. changing the tracking code, adding events.)
Changes to goals or filters.

While annotations can (and should) be used for technical changes in the website, it is important to keep them at a high level. You shouldn't add detailed information about your changes or annotate relatively minor changes; otherwise the annotations will become too crowded to convey meaningful information to readers.

4. Know What Your Site Sends To Google Analytics

The Google Analytics team built a Chrome extension that is intended to help you debug your implementation. Here is what you will be able to do using the extension and a screenshot of how you will see the data:

This extension loads the debug version of the Google Analytics Javascript for all sites you browse using Google Chrome. It prints useful information to the Javascript console. These messages include error messages and warnings which can tell you when your analytics tracking code is set up incorrectly. In addition, it provides a detailed breakdown of each tracking beacon sent to Google Analytics.

Important tip: this extension can also be used for competitive analysis. If you use it while browsing your competitors' websites you will learn how they are tracking their customers.

5. [E-commerce sites] Compare Google Analytics to Database

The most important feature on Google Analytics for Ecommerce websites is the Ecommerce Tracking. It allows the marketer and website owner to understand what and who is driving online sales. But it is essential that the numbers on Google Analytics approximately match the database of the company, otherwise they won't be trusted.

In order to make sure the numbers match, ask from your Database administrator to retrieve the daily Ecommerce revenue for a month, and extract the same information from Google Analytics. Plot the numbers on your preferred spreadsheet tool and check if the numbers and the trends match. If they do not match, here is a quick list of things to check:

When 2 or more of the same item are purchased, does Google Analytics trigger _addItem more than once? (it should)
How does Google Analytics record transactions that use promotional coupons and how the database reports it?
Be careful with apostrophes! If you use apostrophes in your product names you should be careful not to pass them to Google Analytics on the _addItem, they can break your code.

Closing Thoughts

As we saw above, there are several tools that can help you understand why the data you are getting might not be what you expected. But if you still can't find a solution to your issue, try asking a question at the User Forum. I also highly recommend you read this code website article: Troubleshooting the Tracking Code.

Happy analyzing!

Posted by Daniel Waisberg, Google Analytics Certified Partner

Getting The Most Out Of Google Analytics For Lead Generation

Google Analytics team — Wed, 28 Nov 2012 19:32:00 +0000

The following is a guest post from Jeff Sauer, Vice President at Three Deep Marketing, a Google Analytics Certified Partner. Jeff recently started a website dedicated to advancing digital marketing knowledge called Jeffalytics.

Lead generators know that the combination of Google AdWords + Google Analytics is a winning combination for generating an inflow of high quality leads. They are like peanut butter and jelly, Forrest Gump and Jennay, Mel Gibson and Danny Glover.

What many users may not realize is that there are many features that they can unlock in Google Analytics to make their lead generation campaigns perform better while becoming more transparent and accountable. What follows is a series of tips, trips and hacks that you can use to make your lead generation campaigns work even better. I have broken this down into three sections: Configuration, Integration, Analysis.

Configuring Analytics for Lead Generation Websites

Set Up Goals in Google Analytics

Yes, this is a very elementary step in your Google Analytics evolution. You surely configured goals on your site years ago, right? Well, let's make sure you didn't miss anything:

Navigate to the URL of your 'thank you' page shown after a lead is generated. Make note of the URL of this page.
Make your best guess as to the value of each lead that you generate (note: you can have multiple lead values, and multiple goals).
Configure your goals in Google Analytics, assigning the proper goal value for each lead you generate.
Unlock a new world of reports in Google Analytics and see the real value of your lead generation efforts.

Bonus tip: There's absolutely nothing wrong with measuring micro conversions on your lead generation site. Have a PDF that someone can download freely? Set a goal and assign it a modest value (even if it's $5, the impact can be huge). Have a 2 minute video? Give it a value as well, even if it's just a dollar or two. Both PDF downloads and video plays can be tracked using GA event tracking - and you can configure goals around events.

Track Visitors Across Domains

Many lead generation sites use third party forms and services to capture leads, whether as part of an affiliate program or a third party CRM site. While this acts as an excellent conduit to lead delivery, it can often result in missing data in Google Analytics reports. Depending on the services used, there is still a way to retain this data in Google Analytics by tracking your visitors across domains. Here's how this is done:

On your primary website, add the _gaq.push(['_setDomainName', 'PRIMARY DOMAIN']); and _gaq.push(['_setAllowLinker', true]); methods.
When linking to your external domain, add an onclick element as follows: onclick="_gaq.push(['_link', 'THE LINK']); where THE LINK is your external page
Add the GA Tracking Code to your third party hosted page, being sure to use the _gaq.push(['_setDomainName', 'PRIMARY DOMAIN']); and _gaq.push(['_setAllowLinker', true]); methods on this page as well. It is important to make sure you are setting your primary domain here as well.
Configure your goals to match the thank you page URL on the third party domain (or on your own site if you can redirect visitors back to your domain)

By linking visits across domains, your reports will accurately attribute visitors and goals to their proper source and medium instead of treating them as direct visitors.

Integrate with Google AdWords Both Ways

Most of us know to share data between AdWords and Analytics and enable the Google AdWords report in Analytics, but many times this is not done properly. In addition, not enough marketers seem to take advantage of Google Analytics' ability to push conversion data back into AdWords. You really have nothing to lose when you integrate these two Google products both ways, but you have many insights to gain. Start off by making sure you configure these integrations properly:

Share Google AdWords data with Google Analytics. This may seem easy, but is often incomplete when implemented. Make sure that you 1) Turn on Auto Tagging in AdWords, 2) Enable Data Sharing and 3) Apply Cost Data into Google Analytics
Configure your goals in Google Analytics as outlined above
As soon as data starts to collect for these goals, you will see the option in AdWords to import your goals from Google Analytics
Enjoy consistent conversion data between both products and ensure that leads are being properly attributed

Using your goals in Google Analytics for your Google AdWords campaigns can come in handy when you don't have the ability to add a traditional JavaScript based conversion code onto your thank you page. In addition, importing goals from Google Analytics allows you to track some of the advanced conversions mentioned below in Google AdWords. The result? Better analysis capabilities, more advanced conversion rate optimization strategy and more credit for the leads you generate!

Integrating Analytics into Lead Generation Efforts

Phone Call Tracking

One thing that marketers may not realize is that for many industries, the majority of leads will come in through the phone instead of through a web form. Google AdWords understands this and now offers a robust system for tracking phone leads generated by AdWords. But how do you properly track and attribute phone calls generated from your site to a particular traffic source? You integrate Google Analytics with your call tracking provider.

This sounds complicated, but it really is not too bad. In fact, many phone tracking vendors offer a Google Analytics integration option as part of their service. For example, this works well with products like Marchex Voicestar and Mongoose Metrics among others.

Here are the basics of how this process works:

Sign up with a phone call tracking service, create tracking numbers and appropriate campaigns
Place tracking phone numbers on your website
Specify a post-back URL to be visited when a successful phone call occurs
Your phone tracking system will send a visit to the post back URL, complete with all Google Analytics cookie values for the visitor who saw that exact tracking number on your lead generation site

Please note that if you drive a lot of traffic to your website, it can take a lot of phone numbers and extensions to fully attribute phone calls to users. As such, you may want to start implementing this method for a small segment of your traffic and then building up to all visitors when this data proves useful.

Also note that even if you don't link calls back to Google Analytics, phone call tracking is still an imperative part of any lead generation campaign, because it's common for 30-70% of the leads you generate to come from the phone in certain industries.

Offline Marketing

Believe it or not, in many industries leads are still generated offline. Examples include trade shows, neighborhood canvassing (going door to door promoting a product or service), print and television advertising. These are activities that companies have been doing for years, but the problem that they run into when using these mediums to drive traffic to their website is that they don't register the traffic source properly in Google Analytics. The result: many direct visitors without proper attribution.

How do we fix this? By following this simple process:

Create a vanity URL that is unique to your campaign (can be a sub folder or new domain)
Create a tracking URL for your website using the Google Analytics URL Builder
301 redirect your vanity URL to the tracking URL (this preserves your campaign attributes)
Learn about how each traffic source performed by viewing your favorite reports in Google Analytics and paying attention to the source/medium/campaign

Now you can put your offline and online leads on a level playing field and compare the effectiveness of both side by side.

CRM Integration

For companies that are generating several leads a day, a Customer Relationship Management (CRM) system becomes imperative for keeping up with the leads coming in the door. Unfortunately, most CRM implementations are not integrated fully with the website and useful data is not shared between the two systems. This can create friction between sales and marketing, while making it nearly impossible to close the loop on what lead generation efforts are working the best.

Fortunately, people smarter than myself have found a way to solve this problem, and this solution for CRM integration by Justin Cutroni has become my gold standard for how to pull information out of Google Analytics cookies and attach to the lead record you enter into your CRM system.

While Justin's post goes into great detail, the basic premise is this:

A visitor comes to your website and has source/medium/campaign/keyword information assigned to them in their Google Analytics cookie
This information is accessible to your website by pulling cookie values out of Google Analytics using JavaScript
Once this information is pulled out, you enter the values into hidden form fields underneath where your lead enters their contact information
The vital information (source/medium/campaign/keyword term) is passed into your CRM system alongside the lead record
Your sales team can now have deeper understanding of what type of traffic generates the best leads, all the way down to a keyword level
You can use this information to refine your marketing efforts and campaigns to focus on your top performers

Sharing information between your website and your CRM system is an imperative step for making your marketing data actionable to the rest of the business. Without integrating, decisions are made based on faith and HIPPOs, instead of actionable data. As a note, with the advent of Universal Analytics this is likely to get even easier.

Analyze the Results and Make Your Site Even Better

How you analyze your site is a very personal thing, and your mileage may vary, so there isn't a magic bullet to ongoing success with your lead generation programs.

With that said, there are several reports that can be extremely useful in Google Analytics for lead generation campaigns. I would start by paying attention to the following:

Use an advanced segment of paid search traffic and then navigate to the Conversions > Goals report. Compare the goal values you created recently with a similar time period in the past. Are your results improving?
Navigate to the Multi Channel Funnels report and either use standard or custom channels. What is the most common first click channel? Are you giving it enough credit in your reporting?

Compare direct traffic before and after implementing the integrations suggested above. Do you start to see more activity with proper attribution? Are you more confident analyzing with less of a grey area?
Have you been receiving all of the credit you deserve for leads you generate over the phone?
When a salesperson tells you that the leads you generate "suck" are you able to match their lead close rate to the source/medium/keyword that generated the lead?
Instead of presenting raw lead numbers in a vacuum are you starting to factor in appointments issued, quotes given and sales made? Can you calculate the true cost of sale from keyword to purchase?

When configured properly, you can use Google Analytics and residual data from GA to perform some in depth closed loop analysis on how your lead generation campaigns are performing. Savvy lead generation experts have figured out how to deliver maximum value to their clients and constituents using the capabilities built into Google Analytics. Now it's your turn.

There you have it, the three pillars to getting the most out of Google Analytics for your lead generation website. Have any cool integrations yourself? Let's talk in the comments below.

Posted by Jeff Sauer, Google Analytics Certified Partner

Google Tag Manager: Video and Q&A

Google Analytics team — Tue, 27 Nov 2012 22:09:00 +0000

Have you ever struggled with implementing new marketing and measurement tools on your website? For many people, deploying data collection “tags” (like conversion tracking, remarketing, audience reporting and analytics) can take weeks or months. Worse, the tag implementation is often incorrect, meaning you’re missing out on valuable information about your site and its users.

It doesn’t need to be difficult. We recently held a webinar to introduce users to Google Tag Manager, a free tool that helps marketers and IT departments manage their marketing and measurement tags quickly and easily. Watch the video here to learn more about:

Overall benefits and features of using Google Tag Manager
A quick demonstration of how to deploy a new tracking tag
Tips for getting your company started with Google Tag Manager

In addition to this webinar, we’ll be hosting a technical webinar in January to help new users through the nuts and bolts of installing Google Tag Manager (with lots of concrete examples). Stay tuned -- we’ll share registration information in a future blog post, or you can check back on the Learn with Google webinar site.

Read on for responses to some of the top questions we received during the webinar.

Questions and Answers

Where can I find out more about the core concepts described in the webinar?
To learn more about the Google Tag Manager management interface, please visit our Help Center -- you may want to start with our Before you Begin article. There you can find more information about key concepts like Tags, Rules, and Macros. For developers interested in how to implement Google Tag Manager, please visit our developer documentation. Or if you’d like help with implementation, you can contact one of our Partners. You can also ask questions (and find responses to questions from others) on the Google Tag Manager product forum.

What happens to historical data if we move to Google Tag Manager?
All of your historical data should be preserved when you move to Google Tag Manager. Google Tag Manager only changes the way that tags are deployed and managed on your site, it does not change the way data is collected.

How would you migrate a tag?
Follow these steps to migrate tags -- whether it’s a single tag or all the tags on your site. If you’re just getting started, take a look at our Before you Begin article.

Create a Google Tag Manager Account and a Container associated with that account.
Install that Container code snippet on every page of your website (so that it appears immediately after the opening tag). The container should be empty.
Map your site - thinking about what data you want to collect, what events you want to track, and which tags you want to use to track that data. You should think about where your current tags are implemented, but now is a great time to rethink your overall data collection goals and start fresh.
(Optional) If you would like to make use of the Data Layer functionality, create a data layer on the pages where you wish to pass information or fire tags
Create Tags, Rules and Macros within the Google Tag Manager interface according to the map you just created. Make sure to apply the correct Rules to your Tags to make sure they fire in the right place.
Test the changes you’ve made in Google Tag Manager using debug and preview mode.
Then push a version of your site live that has removed the hard-coded tags from within the page. At this time, also Publish your changes using the Publishing feature of Google Tag Manager, which pushes the changes live to the site.

For more precise details on these steps, read our developer documents about migration.

Can you add tags to events or buttons?
Definitely! In order to use Google Tag Manager to fire tags on events and buttons, follow these steps (for more detail, read our developer document on event handlers):

On your page, proactively add the dataLayer.push({ ‘event’: ‘myEventName’}) to the event handlers for all events and buttons you might want to track.
Create a new rule where “event equals myEventName”.
Associate this rule with any tag you’d like to fire when the specified event happens.

Can hard-coded tags and tag manager co-exist? Do I have to remove my other tracking tags?
We strongly recommend that you completely migrate all your tags, so you can take advantage of the benefits of managing and updating those tags within Google Tag Manager. However, if a full migration seems too hard, you can use Google Tag Manager in parallel with hard-coded tags. Some of our users use Google Tag Manager to only manage adding new tags.

If you choose to do a partial migration to Google Tag Manager, you need to be very careful to make sure you don’t accidentally start double-counting your tags. If you decided to deploy a tag via Google Tag Manager, make sure that you don’t have a version of the same tag firing on the same page.

Can you build your own custom tag templates? And how do I become a recognized Tag Vendor within Google Tag Manager?
Custom Tag templates within Google Tag Manager allow you to copy/paste any HTML or Image tags directly into Google Tag Manager and fire it based on your predefined rules and macros. To turn it into a template, use the {{macro_name}} syntax to populate the tag code with dynamic values. We will also do a syntax check to ensure that when you copy your 3rd party tag, it will fire as intended.

If you’re interested in having your tag added to the list of predefined templates, apply to become a Tag Vendor within Google Tag Manager by completing this interest form.

How does this work with Google Analytics? How do you do things like track pageview and track event within Google Analytics?
Google Tag Manager is a convenient way to correctly deploy Google Analytics across your site. To use Google Analytics within Google Tag Manager, simply create a Tag with the Google Analytics tag template. You can select the “Track Type” as either a pageview, an event, or a transaction.

Make sure you have some version of the Google Analytics tag firing across all pages on your site. A good way to do this would be to have a basic tag firing on all pages, but blocking on pages where your more customized tags are firing (like the thank you page where you’d be firing a specialized transaction tag type).

Can the Google Tag Manager snippet be placed in ? How about in my footer?
The recommended best practice is to have the Google Tag Manager snippet at the top of the to maximize data collection, but some clients may find it easier to implement the Container snippet elsewhere in the in the page, like the footer.

Do not place the Google Tag Manager snippet in (for the IT folks: this is because there is an iframe in the