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.
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.
7. Audience Data and reporting!
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.
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.
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?”
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
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.
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.
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
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.
I leave you this very interesting video presented at the Google I/O 2013 about how to use Google Analytics (Universal Analytics) to optimize Web and Mobile Apps across devices. Enjoy it!
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 (yoursite.com 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!