Analytics 2.0

Much more than just tagging

Analytics 2.0 - Much more than just tagging

Google Analytics Summit 2013

As every year I’m here, in this case with Richard Dawson (Intellignos) and Diego Salama (Mercado Libre) at the Google Analytics Summit, in this case in it’s 2013 version.
This post is in real time so you will see things in draft and not finished…don’t worry, will be done after the event is finished.

Google Analytics Summit 2013

Great introduction by Paul Muret, Vice President of Engineering at Google Analytics talking about history and the Analytics Market.
Paul gave the voice to Bebak Pahlavan the Director of Product Management of Google Analytics who after saying that they launched more than 70 new feature, will introduce 14 new Features:
1. Auto-event tracking en Google Tag Manager.
2. Premium Service Level Agreement for Google Tag Manager.
3. Upgrade to Universal Analytics for the standard accounts!
4. Management UI and API.
5. New ABC report.

ABC Report Google Analytics
6. New Unified Segments.

Google Analytics Unified Segments

7. Audience Data and reporting!

Google Analytics audience analysis
8. Audience data within unified segments!
9. Export GA hit data into google big query for premium customers
10. Double click campaign manager integration – view through, click through data.
11. Double click data import into Multi channel funnels.
12. Google Play integration with GA Analytics to analyze the impact on downloads.

Plays with Analytics
13. Analytics Academy.

Oct 1: Course opens for registration!
Oct 8: Units 1-4 will be available, Google Group opens for discussions.
Oct 15: Live Hangout #1, Units 2-6 open for access.
Oct 22: Live Hangout #2
Oct 30: Course closes, get your certificate by this date!
14. In-Product help videos

SDX – more granular and complex querying of unsampled data
The upcoming BigQuery integration is a planned feature for Google Analytics Premium that allows clients to access their session and hit level data from Google Analytics within Google BigQuery for more granular and complex querying of unsampled data. For those unfamiliar with Google BigQuery, it’s a web service that lets you perform interactive analysis of massive data sets—up to trillions of rows. Scalable and easy to use, BigQuery lets developers and businesses tap into powerful data analytics on demand. Plus, your data is easily exportable.

Google Analytics big Query

APIs For Enterprise
Large companies have unique needs; they have many websites and many users. In the past, it could take many hours to setup Google Analytics. With our new Google Analytics Enterprise APIs, IT teams can programmatically setup and configure Google Analytics accounts, saving time, and giving them more time to analyze data.



After the break Tom Davenport, Professor, Author and Senior Advisor to Deloitte Analytics is presenting Marketing Analytics 3.0. Speaks how old is Big Data and ask to the attendants “Who would anytime say I work with small data?” :-)

Tom Davenport at GA Summit
Marketing Analytics 2.0 is the big data era which is
1. Complex, large, unstructured data about customers
2. New analytical and computacional capabilities.
3. “Data scientists” emerge
4. Online and digital marketing firms create data-based products and services.

Marketing Analytics 3.0
Fast, Pervasive Digital marketing:
1. A seamless blend of traditional analytics and big data
2. Analytics integral to marketing and all other functions
3. Rapid, agile insight and model delivery.
4. Analytical tools available at point and time of decision
5. Analytics are everybody’s job
6. Heavy reliance on machine learning “However we are very sceptical about it’s potential”
7. In-memory and in-database analytics.
8. Integrated and embedded models.
9. Analytical apps by industry and decision.
10. Focus on data discovery.

GE has been creating the new analytics and industrial internet model and invested 2 Billon on that.

“75% of marketers don’t know their ROI”

Recipe for a 3.0 world
1. Start with an existing capability for marketing data management and analytics.
2. Add some unstructured large volume customer data.
3. Throw some product/service innovation into the mix.

Now is the turn of Russell Ketchum Product Manager at Google Analytics In-app measurement: Going native.

Conversions, are they doing what matters?

[In Apps]: when you’re looking at behavior metrics, you’re looking at what people did / how they’re using your app.

Acting on an idea

Users should spend time with their data

The drawer is the fastest way to data

Improving the drawer helps users get to data


Lunch time at GA Summit

At 13.40hs cames along Jody Sarno, Customer Insight Senior Analyst at Forrester to talk about solving marketing challenges: How attribution can help.

Clear up the confusion. Marketing Mix modelling (MMM) is the process of using statistical analytics to estimate, optimise and predict the impact of paid, owned media.

Key Findings

1. Marketers leverage attribution to uncover marketing and consumer trends.

2. Opportunities. Data integrations, change management and customer purchase path.

End of Jody conference

While the next speaker begins you can take a look and register at the brand new Google Analytics Academy.

Now Bill Kee, Head of Attribution Products at Google Analytics talks about how to make attribution works.

  • In 2011 multichannel funnels
  • In 2012 Attribution Modelling tool
  • In 2013 Data Driven Attribution

There are two important new integration, the first is Youtube Display Network with Google Analytics and the second is Double Click with Google Analytics allowing to understand the full clickstream (user journey) of a user.

Data driven attribution model.

Calculate the impact of each touch point. “All models are wrong! But some are useful.” Bill Kee – Head of Attribution Products, Google.

Bill invite Melissa Shusterman, Strategic Engagement Director at MaassMedia to talk about a case study.

Melisa says that attribution allow them to optimise all displays campaigns an not just the ones that drives conversions…(sorry I don’t understand what she wanted to say).

1. Initial Analysis, click throughs.

2. View Throughs conversions, confusing. This contradicts click throughs. conversions.

Key areas of attribution:

1. Last Touch Sales.

2. Attributed Sales.

3. Percent Non-last touch sales.

4. Cost per attributed sale.

Results: The traffic decreased but the conversions with the Data Driven Model increased.

Making it work

  • Set up acurate weighted goals
  • Allow advertisements time to work
  • Dont’ think too small
  • Kill poor performers
  • Sell attribution-  Help display compete
“Data Driven Attribution forces Display teams to get smarter” 

Next presentation is Steve Yap, Head of Emerging Products at Google. Required to win: The integrated analytics imperative.

Integrations thorough principle. Today’s market and todays consumer demand more from us. They want relevancy, engaging creative and meaningful content.


1. Whatever we built has to be the best in the market.

2. The system have to work well with one other and be better together.

3. They are easy to use.

Progression toward action like Doubleclick, Teracent, Invitemedia and Google Analytics.


if you wanted to know what they are thinking just go and ask them

Multivariate or A/B testing is very cool and useful. Today’s solutions are very flexible and allows you to test several pieces of marketing (ads, lading pages, etc) in real time without the need of being an expert. You have tools for any particular need, free solutions like Google Content Experiments; paid solutions for a low price like Convert, Optimizely, Unbounce and VWO; and paid solutions for bigger budgets like Adobe Test & Target, Sitespect and Autonomy optimost among others.

Multivariate testingMultivariateTesting

Even though this kind of solutions are great and useful it is important to know what are they useful and are not useful for. A/B testing and Multivariate solutions are useful to test a “Short list” of changes or optimisations after you already know them. What they can’t do is let you know what are those changes you have to test. Let’s put it this way, if someone tells you “what do you prefer, that I give you a punch in your eye or in your stomach?” You will probably chose the eye or the stomach depending on what bother you less.  However that doesn’t mean that you like to be beaten.

Before running A/B testing or a multivariate testing I highly recommend you to run surveys to let your users chose the short list to be tested (without they knowing that). If you just show them just a few combinations that you have in mind, you could be leaving aside some other possibilities that can drive you in a better manner to your business result.

A/B testing or multivariate testing are a behavioural source of information. Behavioural information can just answer you “WHAT” the user is doing but not “WHY”. It is important to know what kind of information source can answer different questions so you can go there and get the answer. The “WHYs” can just be answered by Attitudinal information sources like surveys.

You have also a full range of surveys solutions both Free like Survey Monkey and Google Forms; and paid like Zoomerang. So, if you wanted to know what they are thinking just go and ask them, no excuses.

Universal Analytics – Hyperbolic with substance

The last monday, 28 of October 2012, was a great day with with some amazing announcements at the Google Analytics Summit 2012 (#GASummit). Google had launched its new strategy that even when it sounds a little hyperbolic  (Universal Analytics?), it has substance. With this new strategy Google Analytics is trying to move out from the Cookie centric (browser centric) measurement model to a new user centric model, which is GREAT! It means that they are not going to use cookies any more? No…means that they are still using the cookie but it will save a user ID (that will remain when the user log in and will be associated to a particular person!), qne that ID will be the new data base key, which means that reports will able to be based on People instead of cookies. I was talking about this some time ago, why not using the User id to join the cookies that a particular user has in different gadgets so you can understand the full stream of the interactions between a particular user and our brand (website, mobile app, etc).

Server Side Sessionization: With “Universal Analytics” the “sessionization” occurs at he server side. The new analytics.js will not maintain any tracking information (other than an anonymous identifier).

It represents important advantages allowing to add new search engines in traffic sources, configure the timeout of a cookie, classify some cookies as direct traffic ( in a search engine), and last but not least the unique ID allow us to integrate the behavioral information of a user to al the information from that user, stored in another sources like a client database or CRM.

Customized segments and metrics: The above mentioned enable to configure the custom dimensions and metrics right on the tracking code and in the administration section as shown in the following image.

_gaq.push(['_setCustomDimension',1,'Custom Dimension 1']);







Measurement protocol: This is similar to the well known EDS (External data sources) from other platforms. This feature allows to send information from any source to Google Analytics scaling its possibilities to a new level. So now you can send to Google Analytics information related to external sources  Call Centers or CRM, among others, to measure even measure a conversion generated offline. This will be done with the current method  __utm.gif (image) and the information is send with the GET or POST method. As long as you use the Google Analytics protocols in a correct way, it will always accept the sent data.

Besides, will be possible to assign marketing and other costs to a particular user. So we can start talking about the model proposed by this blog, the Analytics 2.0 model, in which instead of analyzing ROI we will be able to analyze ROCI (Return on customer investment) allowing us to understands which segments of clients are making us earn money and witch other we are losing money so we have to stop investing money on them.

Dimension Widening: This feature permits generating more and better decision making scenarios based on custom dimensions and metrics, helping us understanding the impact (if there is any) of thousands variables towards a particular conversion (sales, registration, etc). Some post ago we talked about how to do that by using variance analysis (anova). This feature is an insight generating machine! in my opinion one of the best features ever introduced by Google Analytics.






This is becoming interesting. I went with very low expectations to the summit, waiting for “Much ado about nothing” but finally we see a light at the end of the tunnel, it’s far, but it’s there!

Dispersion Analysis with advanced segments in Google Analytics

In statistics, statistical dispersion (also called statistical variability or variation) is variability or spread in a variable or a probability distribution. Common examples of measures of statistical dispersion are the variance, standard deviation and interquartile range.

A measure of statistical dispersion is a nonnegative real number that is zero if all the data are the same and increases as the data become more diverse (Wikipedia).

Dispersion Analysis is one of the most basics and powerful statistical analysis, allowing you to understand how homogeneous is a population or sampling you are analyzing. Why would that be important? Well, if we talk specially about digital analytics one of the biggest problems that that we love to use “average” (or even rates), average time on site, average pages per visit, bounce rate, etc. The problem with averages and rates is that you put in the same bag apples and oranges, driving you to very Inconsistent decisions. Disperse points can drive you to a wrong understanding of the reality, ergo, will make you make wrong decisions.

One way of analyzing dispersions is taking off the disperse points from the analysis. I mean, if most of your visits have an average pageviews per visit ranging from 8 to 15, and have some with just one pageview (maybe bounces if the can’t do any other measured action in that particular page) and you know those ones are non qualified visitors, you can just take them out of the analysis. It will allows you to analyze a more homogeneous set of data, effectively increasing the certainty of the decision making scenarios.

So, let’s do this in Google Analytics. In order to select a particular set of data we will use Advanced Segments.

1. Take the set of data you wanted to analyze. In this case, let’s take the above mentioned case, Average pages / visit.

2. Create a new segment that takes only the set of data you are looking for. In this simple case we will just select all the traffic but those that visited just one page.

Dispersion Analysis

Dispersion Analysis with Google Analytics

3. Analyze the information with the new set of data. Take a look at the difference in the results with one and the other set of data.


Segments in Google Analytics

Looks like a huge difference right? Is much more than that, you can avoid disperse points and having not a huge difference in the results but you have the certainty of using the correct information.



The Yahoo! Web Analytics Merchandising Report

If you have an eCommerce project and have installed Yahoo! Web Analytics, or you haven’t but you are thinking about implementing it I drop you some interesting tips about the Merchandising Report.

The Merchandising report it’s an awesome additional reporting solution if you already have the eCommerce report installed.

With the Yahoo! Web Analytics Merchandise Report you will be able to track not just the regular stuff from each sale like the individual products your customers preview, add to their shopping cart and purchase but also it allows you to define categories for your products, create custom reports, and update your reports to reflect cancelled orders or changes to order amounts by sending API requests to the system, and even doing up selling / cross selling analysis. You can also upload your individual products costs and calculate how each campaign contributes to your profits.

Yahoo! Web Analytics implementation is pretty simple if you, at least, take a look at the manual :-). The merchandising report has the very same logic, you simple add functions to your tracking code like:

Amount; setAmounts records the total price of a product (i.e., the individual price of an item multiplied by the total number of units of the respective item).
SKU; Corresponds to the specific products you wish to track. The product SKU information is usually expressed as a function employed by your shopping cart. However, it can also be expressed as a constant (e.g., TEN114/S/03)

It is important to mention that each Yahoo! Web Analytics function is used to inflate a function from your shopping cart. Since shopping carts have different setups, you need to be familiar with the functions used in your shopping cart.

Another interesting feature is “Tracking viewed products“. By adding the ‘PRODUCT_VIEW’ action to your action function, as in the following example, you can track how often potential customer view your products and which products are the most popular.


If you wanted to track more product views at the same time you can just do the following:


Another feature that you will love is ADD TO CART TRACKING. By activating this feature (you simply add the ADD_TO_CART action to your action function, as in the following example) you can track which products your visitors added to their online shopping cart.


Important: The SKU does not have to include a letter and eight digits. It can be any combination of letters and numbers that you use to identify your products.

So we measured the viewed products, the added to cart products and now you can also track the Purchased products. To do so you simply activate the Sale action (01) action, as in the following example:


If the tracked currency is important to you, let me suggest you to take a look at this link where you will find the total currencies supported by Yahoo! Web Analytics

To enter the price of your purchased products, you need to use the setAmounts function. Unlike the setAmount function, the setAmounts function does not need to have the currency code entered, as the currency is inherited from the setAmount function.
You can also track more products purchased at the same time:


One fantastic thing in Yahoo! Web Analytics is that the Tracking code has a very simple logic that it is replied in all the tracking functions. So once you understand the basics is not hard at all implementing another features.

Back from Google Analytics Summit 2011

It was a very intense week at the Computer History Museum. Jesse Nichols, the tireless Program Partner Manager for Google Analytics and Google Optimizar, did a great job with his team, arranging an event for more than 500 people from all over the world in in a very exquisite way.

I’m not sure what it is and what it is not under NDA so I wont go in depth about new launches and developments. The most important things I wanted to mention are:

1. The first day was basically an overall presentation about the platform improvements. All of them where Woowww… not all of them really useful, but the ones that does are awesome…at least are the ones I was expecting for such a long time.

2. The second day, Thursday, we went in depth about the Partner program and Technical aspects of the current and future launches. I had the honor of being part of the Panel with Justin Cutroni (Cardinal Path), Timo Aden (Trakken) and Russel Sutton (ConversionWorks), moderated by the awesome Sophie Chesters (Google).

Panel at GA Summit 2011

At the end of the event it was a Web Analytics Thursday with a Rock Band, networking, drinks and food…anything else? Oh, yes…amazing people from all over the world willing to talk about their experiences without constraints. See you there next year mates!!!

Google Analytics changed the session (visit) definition

Google AnalyticsLast week Google Analytics changed the way the session (or visit) is calculated. Why is this so important? Because your past information will not change, just from now on, ergo when you analyze trends you will identify a variation in you reports comparing before and after the new visit definition, so the first thing you have to do is loading a new event note in your Google Analytics platform so when you analyze the information in the future it will be easy to understand the cause of the variation.

Last visit or session definition:

More than 30 minutes have elapsed between pageview for a single visitor. No changes.

At the end of a day. No Changes.

When a visitor closes their browser: It changes by “When any traffic source value for the user changes. Traffic source information includes: utm_sourceutm_mediumutm_termutm_contentutm_idutm_campaign, and gclid”.

How is this new session definition different?

Basically if the person leave your site and come back from a different source (any of the above mentioned values changed) this will count as a new session. According to Google, this change will just generate a variation of not more than 1%.

Important Note from Google, remember add a note in your platform:

Update 8/17/2011 2:10 PM PST:
“We identified an issue responsible for unexpected traffic changes following our recent update to how sessions are defined in Google Analytics. A fix was released at 2pm PST Tuesday August 16th”.

The issue affected some sites using the following configurations:

1. If a user comes to a customer’s site with a space in some part of their traffic source data, then revisit the same landing page during that session by refreshing the page or later pressing the back button, a new session will be created for every hit to that page. (Clicking a link elsewhere on the site that leads back to the page should not matter.)

2. Google Analytics implementations using multiple trackers (an unsupported configuration) are also affected when a space is included in the traffic source data. These sites will see fewer visits from new visitors, and more visits from returning visitors (with some variation due to different implementations).



Campaign comparison Google Analytics vs. Yahoo! Web Analytics

Which is the best Web Analytics solution? That’s one of the most frequently asked questions in this industry. Unfortunately things in the world are not that easy, because if there is one “best” solution why would people buy another one? If there is one “best” solution why competitors don’t just work on the same line of development?

Most of the current solutions are the best…for a particular need. So the first thing to define it is what is your need.

Let’s see a very simple example comparing Google Analytics vs Yahoo! Web Analytics (or Y!WA) :

1. Campaigns with Google Analytics: Google Analytics has it’s own query parameter structure for campaigns. Which means that you have to use one utm_source, utm_medium, utm_term, utm_content and/or utm_name for any of your campaigns. The good thing is that you don’t have to do anything else to do it. I mean, you don’t need to configure anything on the tool, just add those query parameters with the specific values on the url you are advertising and that’s it. You can even use the Google Analytics URL Builder which concatenate all the values you provide on each field generating the campaign url in a very simple way.

URL builder tool

So, if you have an standard campaign structure Google Analytics is a great solution, simple, faster and very effective.

2. On the other hand Yahoo! Web Analytics or Y!WA count with a more flexible solution that allows you to track campaigns based on different conditionals and variables that allows you to track almost everything. More work but more flexibility and more in depth analysis.

First you can select type of campaign, free campaign, one time cost, cpc or cpa.


Secondly you can fill the standard query parameters or even change them by others you are already using on campaigns (ie, adserver)

Finally you can select the required conditions for that campaign like “Url parameters equal”, “Url parameter contains”, etc…

So, none is the best. The best one is based on each client requirement… or just enjoy both! ;-)

Report your +1 in Google Analytics and Webmasters tools

Well, it is great to find out that at least some players in the industry are taken seriously the importance of information integration, the importance of having all the pieces of the puzzle available when creating the decision making scenario. A way to revert the situation displayed in “Top Performance Metrics“.

Last week Google released the +1 button, kind of a “Like” but for the Google+ Project. The best part of this released is that Google thought about integration from the beginning and you can measure the +1 button from Google Webmasters tools and Google Analytics understanding how +1 affect your traffic and traffic behavior.

Regarding Google Webmasters tools:

  • The Search Impact report gives you an idea of how +1‘s affect your organic search traffic. You can find out if your clickthrough rate changes when personalized recommendations help your content stand out. Do this by comparing clicks and impressions on search results with and without +1 annotations. We’ll only show statistics on clickthrough rate changes when you have enough impressions for a meaningful comparison.
  • The Activity report shows you how many times your pages have been +1’d, from buttons both on your site and on other pages (such as Google search).
  • Finally, the Audience report shows you aggregate geographic and demographic information about the Google users who’ve +1’d your pages. To protect privacy, we’ll only show audience information when a significant number of users have +1’d pages from your site.

Regarding Google Analytics, the good news is that is you configure the Javascript for Analytics, it reports not just the +1 but also other buttons allowing you comparing the different sharing actions by using Social Plugin Tracking in Google Analytics.
  • The Social Engagement report lets you see how site behavior changes for visits that include clicks on +1 buttons or other social actions. This allows you to determine, for example, whether people who +1 your pages during a visit are likely to spend more time on your site than people who don’t.
  • The Social Actions report lets you track the number of social actions (+1 clicks, Tweets, etc) taken on your site, all in one place.
  • The Social Pages report allows you to compare the pages on your site to see which are driving the highest the number of social actions.
Here I leave you a link to enable tracking for other social plugins in just a few simple steps.