Tag Archives: Google Analytics Blog

Full Credit Measurement: Attribution with Google Analytics

As we’ve discussed in many previous posts, the customer journey is evolving — most consumers will interact with many different marketing channels before a sale or conversion. And marketers are recognizing this shift in consumer behavior. Instead of “last click” measurement, a strategy that only gives credit to the final interaction, they’re turning to full credit measurement. To help you make sense of the full customer journey, we’ve been focused on bringing you the very best full credit measurement tools in Google Analytics.

Nearly two years ago, we announced our first Google Analytics attribution product, Multi-Channel Funnels. With its ability to measure customers’ different paths to conversion, it quickly became one of our most popular reports for advertisers and publishers alike. We’ve seen great results from our users, including online travel agency On the Beach, who used data from the Multi-Channel Funnels reports and AdWords Search Funnels to explore and adjust their strategy for generic keywords. These attribution adjustments helped On the Beach to drive a 25% uplift in ROI — see the full case study here.

Beyond Multi-Channel Funnels, we also wanted to provide our users with an advanced platform for testing entirely new, more robust attribution strategies, including the ability to test alternative models or understand how metrics, such as site engagement, could impact their existing investments. So last year we released our Attribution Modeling feature — the Model Comparison Tool.

After several months of testing on a public whitelist, we’re now in the process of rolling out the Attribution Model Comparison Tool to make it generally available to Google Analytics users without whitelist.  To reflect the importance of attribution, we also created a new “Attribution” section under the “Conversions” reports, so the tool will be found there.

Be sure to check out a previously recorded webinar with Product Manager Bill Kee for a complete walkthrough of the Attribution Model Comparison Tool, or view our multi-part attribution webinar series covering our entire selection of full-credit measurement tools.

Posted by Sara Jablon Moked, Product Marketing Manager, Google Analytics


Written by: Adam Singer at http://analytics.blogspot.jp/2013/06/full-credit-measurement-attribution.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed:+blogspot/tRaA+(Google+Analytics+Blog)

Universal Analytics Business Applications

The following is a guest post by the Analytics Team at Loves Data, a Google Analytics Certified Partner.

Universal Analytics introduces a new set of Google Analytics features allowing businesses to gain a deeper and more strategic understanding of what’s capturing the attention of customers as they move from online to offline. So how can Universal Analytics help businesses turn customer data into sales? We at Loves Data designed a simple experiment to find out.
Who drinks coffee? Who drinks tea? How much? How often? When? The answer to these questions reveal the role our espresso coffee machine and tea kettle play in productivity – and any need to order more tea or coffee! Take a look at our video to see what we learned.
Our experiment at Loves Data also measured how often and how much time team members spent standing in front of a display screen in the office viewing our website analytics.

Montage: Loves Data’s Universal Analytics office experiment will benefit businesses:

Experiment creates a new path to customers
Our team designed an experiment to dive into Universal Analytics by creating interactive scenarios inside our office. We integrated sensors and RFID readers to capture data about coffee and tea making behaviour in our office. We also measured each time the fridge was opened, when one of our team updated a support ticket, client hours were logged, code was committed, administrative tasks, and viewing of our Google Analytics dashboard display.
New Business Opportunities
Measuring users across platforms opens up new business opportunities. The RFID keys we’ve used in our experiments can be used to measure loyalty card usage. We can use Universal Analytics to enable retailers with bricks and mortar stores to measure customer behaviour and to improve and integrate online and offline sales and marketing.
Here are a few Universal Analytics opportunities we have identified at Loves Data for our clients:
  • Integrated measurement and analysis of in-store POS systems along with desktop and mobile e-commerce platforms
  • Measuring offline macro and micro conversions through physical buttons or integration with CRMs
  • Measuring physical interaction for example at display booths at conventions or artworks at major exhibitions through to online engagement on associated websites
Our office experiment provided ourselves and our clients with a range of valuable insights and showed that with Universal Analytics we can measure just about anything!
Posted by the Analytics Team at Loves Data, a Google Analytics Certified Partner. Learn more about Loves Data on their website, Google+ or check out their digital analytics and online marketing blog.


Written by: Adam Singer at http://analytics.blogspot.com/2013/04/universal-analytics-business.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+blogspot%2FtRaA+%28Google+Analytics+Blog%29

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

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.


Written by: Google Analytics team at http://analytics.blogspot.com/2013/03/get-useful-insights-easier-automate.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+blogspot%2FtRaA+%28Google+Analytics+Blog%29

Learn About the 7 Factors of Bid Optimization

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


Written by: Google Analytics team at http://analytics.blogspot.com/2013/03/learn-about-7-factors-of-bid.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+blogspot%2FtRaA+%28Google+Analytics+Blog%29

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).


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.


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.

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:


And for brand new users:

Posted by Andrew Wales, Google Analytics Developer Relations



Written by: Google Analytics team at http://analytics.blogspot.com/2013/02/5-things-you-should-be-doing-with.html?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+blogspot%2FtRaA+%28Google+Analytics+Blog%29