Learn from Google at #SESNY 2013

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.

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

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, Google will give you more information about this topic.

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

Understand Updates To Your Account With Change History

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. 

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

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:

<!-- Enable automatic crash measurement (Android) -->

<bool name=”ga_reportUncaughtExceptions”>true</bool>

// 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)

Learn About the 7 Factors of Bid Optimization

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.

Google Analytics Premium expands to Japan

Good news, or 良いニュース as they say in Japanese. We’re excited to announce thatGoogle 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

Enhancing Google Analytics Access Controls

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!

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

Tuesday, March 12, 2013 | 10:44 AM

Labels: Advanced Topics, Analytics API

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 aGoogle 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.