Google Analytics Premium has just been announced by Google.

Here’s the launch video:

We have listed the main benefits below:

Extra Processing Power

Extra processing power means more data, more quickly. With Google Analytics Premium, you can gather, analyze and share more data than ever:

• Lifted data limits. Track more then ever—billions of hits per month and 50 custom variables give you deep insights to make more informed decisions.
• Download unsampled reports. Export high data volumes and analyze all of your data.

Advanced Analysis Tools

Advanced analysis tools deliver deeper insights: Analysis options unique to Google Analytics Premium provide a deeper understanding of consumer behavior:

• Attribution modeling. Easily perform attribution modelling on your marketing campaigns to understand the full value of all the channels in your media mix.
• More custom variables. Access up to 50 custom variables which you can customize to collect unique site usage data.

Uptime Guarantees

Service Level Agreements give you an uptime guarantee from Google. Your site activity is reliably recorded and available to you at all times. Google will compensate you if they don’t deliver.

• Data collection. Get a Service Level Agreement of 99.9% in any calendar month.
• Processing. Data freshness within a maximum of 4 hours 98% of the time. React faster than ever.
• Reporting. Enjoy guarantees of 99% in any calendar month.
• Data ownership. You own all of your data. Your contract ensures it.

Pricing and Availability

The product is offered on a flat-rate annual fee and is currently available in North America, United Kingdom and Canada. The product is not yet available in New Zealand or Australia.

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I’ve recently returned from Palo Alto from the Google Analytics Partner Summit. This year the summit was held at the Computer History Museum in Mountain View.

Whilst I can’t share any specific information as far as GA is concerned, what I can share is that upcoming changes are going to be big.

One thing that really impressed me is just how much was being shared with the GACP partners. Furthermore, any concerns or questions raised were addressed and answered on the spot. I’ve come away feeling genuinely excited to be working in the analytics industry on a platform as impressive as Google Analytics.

Another really nice thing was that we also got an update on Google+ and Android. It’s amazing how Google is pulling all this together and it has been a real pleasure hearing directly from the engineers and product managers.

And of course Avinash Kaushik did not disappoint and had the crowd laughing uncontrollably on several occasions.

Given that there isn’t really all that much to see and do in Palo Alto, I spent a couple of days in San Francisco which was excellent. One of the locals whom I met at the Summit showed me around town which was really nice. I even managed to take a pic of the bridge without any fog:

Here’s the list of speakers from the 2011 GACP Summit, responsible for draining my laptop battery to near-death levels on both days from all my note-taking:

• Amy Chang – Global Head of Product, Ads Measurement
• Dr Phil Mui – Group Product Manager, Google Analytics
• Paul Muret – Director of Engineering, Analysis Products
• Sissie Hsiao – Group Product Manager, Cross-Channel Measurement & Attribution
• Brett Crosby – Director of Product Marketing, Google+
• Enrique Munoz Torres – Senior Product Manager, Google Analytics
• Hasan Bakhshi – Director, Creative Industries, NESTA
• Juan Mateos-Garcia – Creative Industries Research Fellow, NESTA
• Marc Vanlerberghe – Director of Product Marketing, Mobile & Android
• Archana Ravichandran – Global Services Manager, Google Analytics
• Bill Kee – Product Manager, Google Analytics
• Chao Cai – Engineering Lead, Conversion Tracking & Cross-Channel Attribution
• Jesse Savage – Product Manager & Privacy Officer, Google Analytics
• Kerri Jacobs – Team Lead, Google Analytics Sales
• Lucas Pettinati – Lead User Experience Designer, Google Analytics
• Matt Ackley – Director of Product Marketing, Media and Platforms
• Michael Fink – Product Manager, Google Analytics
• Michal Neufeld – Product Manager, Google Analytics
• Nick Mihailovski – Senior Developer Programs Engineer, Google Analytics
• Sagnik Nandy – Engineering Lead, Google Analytics Backend and Infrastructure
• Shauna Gerry – Team Lead, Google Analytics Account Management
• Sophie Chesters – Product Marketing Manager, Google Analytics
• Trevor Claiborne – Product Marketing Manager, Google Analytics
• Jesse Nichols – Partner Program Manager, Google Analytics
• Timo Josten – Partner Program Manager, Google Analytics
• Avinash Kaushik – Digital Marketing Evangelist, Google
• Justin Cutroni – Director of Digital Intelligence, Cardinal Path
• Russell Sutton – Managing Director, ConversionWorks
• Timo Aden – Managing Director, Trakken
• Juan Manuel Damia – Co-Founder, Intellignos

I’ve learnt a lot and passed on my learnings to the team.

We are keen to help your business maximise your return from Google Analytics. Please contact us for a no-obligation chat.

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Google Shopping (Google Product Search) can be a very powerful source of traffic and conversions for online retailers. However, analysing the performance can be very tricky as currently there does not seem to be a solid integration between Google Shopping and Google Analytics (GA). As such, to get the required data accuracy for analysis and decision-making purposes, customised GA tracking solutions need to be used.

Currently, there are two GA tracking customisation methods that seem to be advocated by analytics users to track Google Shopping traffic:

1. URL filtration: using advanced filters imposed on either the referral URL or the request URI to rewrite the Source or Medium information to differentiate this traffic from Google / Organic traffic.
2. UTM variables: using UTM variables appended to the back of the Google shopping feeds which power the Google Shopping listing.

We will assess each below and demonstrate why the latter is better than the former.

Method 1: URL Filtration

This method is the simplest as it only requires adding filters onto the GA profiles where the traffic is to be tracked and analysed.

However, due to the different scenarios below, this method does not seem to provide enough reliability to guarantee data integrity.

Currently, there are three different ways for a user to reach Google Shopping search results:

Method 1: Go to Google.com.au, click on Shopping link at the top of the page, and search for “red long dress”	Method 2: Go to Google.com.au, search for “red long dress”, and click on Shopping link at the top of the page	Method 3: Go to Google.com.au, search for “red long dress”, and click on Shopping link on the left

It turns out that the URLs of the Search Engine Results Page (SERP) and the URIs to the actual product (on the retailer’s websites) vary depending on how the user reaches the SERP:

Not only that, but the lack of distinct characteristics on these three SERP URLs (as the “referring page”) is making it hard to differentiate this from a normal organic search SERP.

The URL filtration method would rely on detecting certain pattern in the referral URL (i.e., the SERP URL; e.g., matching “google\.com\.au/products”) or in the request URI (i.e., the result URL which users click on; e.g., matching “google\.com\.au/\?sa\=t\&source\=productsearch”)

As shown in the above tables, should we want to track using URL filters, the referral URL and the request URI are inconsistent from one method to the other. And since there is no clear way to establish what the pattern is, tracking using URL filters may well lead to a very inaccurate or incomplete data.

Due to this weakness, the URL filtering tracking method is thus not recommended.

Method 2: UTM Variables

Another method to track Google Shopping traffic is by implementing UTM variables on the URLs in the product feed.

This is as simple as appending the following line to the back of every product page URL in the feed:

utm_source=google&utm_medium=shop&utm_campaign=feed

• the utm_campaign value can be changed to any arbitrary identifier value
• the use of utm_term and utm_content is discouraged as this would threaten the integrity of the search keyword data

Thus, the #1 result example URLs tagged with UTM variables are provided in the table below. Note: The appearance changes are due to percent-encoding done by Google SERP processing.

When implemented properly, the landing page URL would correctly show the three UTM variables upon the user landing. Furthermore, using tools such as Firebug to audit the __utm.gif would show correct Source/Medium information (Google/Shop) while retaining the keyword data intact.

This method is foolproof for any referral URL and request URI variations, as it only relies on the final landing page URL which is specified in the XML product feed and is 100% controllable by the vendor. Thus using UTM variables, and not profile filters, is the recommended method.
Author’s Note: Google Shopping is currently unavailable in New Zealand.

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• Google Analytics Multi-Channel Tracking (Cross-Channel Reporting)

Note: Google has let us know that the feature called Multi-Channel Funnels discussed in this blog post is in limited pilot. That means that Google is testing the feature and its usefulness with a small group of trusted testers, and have not made any plans or a timeline for a full launch.

Google has this week released a new cross-channel tracking feature. This new feature is also referred to as “Big Funnels” and will allow online marketers to better understand the bigger picture and more accurately assess the true value of their marketing campaigns.

As part of our partnership with Google, First Rate has been testing the new cross-channel tracking feature prior to its release and we are happy to share some of our findings and provide a quick overview of this new and exciting feature.

What is Cross-Channel Tracking?

Put simply, Cross-Channel tracking (also called Multi-Channel tracking), allows online marketers to discover traffic sources and keywords that assisted in a conversion beyond just the last click or visit. By default cross channel tracking tracks and reports on interactions within the 30 days leading up to a goal completion or e-commerce transaction on the site but can be adjusted to 60 or 90 days as required.

Google Analytics (GA) cross-channel tracking encompass all digitally trackable channels, including Paid Search (all search engines), Organic Search (all search engines), Referrals, Affiliates, Social Networks, Email Newsletters, Display ad clicks and impressions, and even offline sources such as TV, Radio etc. via vanity URLs.

Prior to cross channel tracking, GA attributed conversions to the last traffic source that led to the conversion. The problem with this approach is that in some cases all the hard work is accomplished during the previous visit as can be seen in the case below were a user discovered the First Rate site by clicking on an Adwords ad (Google CPC) and later returned via an organic search for the brand name “First Rate” which lead to a conversion on the site. It would make more sense in this case to attribute most of the credit for the conversion to the Adwords ad that drove the first visit rather than to the last visit before the conversion.  With Google Analytics multi-channel reports online marketers will be able to easily discover such assisting traffic sources.

Google Analytics Multi-Channel Reports

Cross channel tracking includes five new report types:

• Assisted Interactions
• Assisted Conversions
• Top Path
• Path Length
• Time Lag Report

The cross-channel tracking report section in Google Analytics can be found within the left sidebar navigation under:  Conversions > Cross-Channel.

Below is a quick overview of each of the new cross-channel reports.

Assisted Interactions Report

This report shows the number of assisted interactions from each traffic source.

Assisted Interactions refer to the number of times the traffic source assisted towards a conversion excluding last interactions from the traffic source.

Traffic source can be grouped into several dimensions including Traffic type, Source, Medium, Source/medium, Keyword and more.  For more information about traffic source dimensions refer to the cross-channel tracking terminologies section below.

Assisted Conversions Report

This report shows the number of assisted conversions from each traffic source.

Assisted conversions refer to the number of conversions the traffic source assisted towards excluding conversions where the traffic source drove the last interaction.

The difference between an assisted conversion and an assisted interaction is that a traffic source can assist more than once towards the same conversion but can only register one assisted conversion for the same conversion.

Top Path Report

The Top Path report describes the sequence of interactions leading up to a conversion and can be grouped by both a primary and secondary traffic source dimension. You can further segment the report by conversion, conversion type and path length.

Below is what a typical top path report might look like grouped by Source/Medium and Keyword

Path Length Report

The Path Length report shows the number of interactions it took for visitors to convert on the site and can be segmented by conversion and conversion type.

Time Lag Report

The time lag report shows how long users took (in days) to convert on your site and can be segmented by conversion and conversion type.

Cross-Channel Report Terminology

Source (traffic source): The traffic source name ex. Google, Bing, Newsletter, Direct, Twitter, firstrate.com.au

Medium (traffic source): The traffic medium type ex. Organic, CPC, Email, Social etc.

Campaign (traffic source): The Adwords campaign name or URL tagged campaign name.

Last Interaction: The last interaction (visit) with the site on the conversion path leading up to a conversion

First Interaction: The first interaction (visit) with the site on the conversion path leading up to a conversion

First Interaction Conversions: The number of conversions where the traffic source drove the first interaction on the conversion path leading up to a conversion

Last Interaction Conversions: The number of conversions where the traffic source drove the last interaction on the conversion path leading up to a conversion

Frequently Asked Questions

Do I need to make any changes to my Google Analytics code or account?

You do not have to implement any further changes to your Google Analytics code for Cross-Channel reporting to work, however,  at least one goal or e-commerce tracking needs to be set up multi-channel tracking to work.

Can I track other ad networks not currently supported?

You would need to make changes to the GA code to track non-standard ad networks.

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The newest major web browsers all seem to carry advanced Privacy Features designed to reduce a user’s Internet footprint by allowing them to tune down what websites can track.

Internet Explorer 9 provides Tracking Protection, while Mozilla Firefox 4 offers a Do Not Track feature and Google Chrome 10 allows users to add an Opt-Out extension to the browser.

While all three are similarly promoted as privacy features, they are massively different in how they work and how they impact the user experience.

Internet Explorer 9 – Tracking Protection

This privacy feature in IE9 requires the user to specifically opt out from a list (or lists) of ad networks. The browser will then watch for clickstream tracking and targeted ads coming from the sites/servers/networks in the opt-out list and simply block them. However, where this list(s) will come from is yet to be finalized.

The main concept is that users will have to create their own lists of ad networks they want blocked, or choose from lists compiled by privacy groups. And it will be left up to the users to continually update their IE9 browsers with the latest, most comprehensive lists.

Jonathan Mayer, a Stanford doctorate and law student who is also a research fellow at Stanford’s Center for Internet and Society, says that the IE9’s blocking lists “should at minimum cover the NAI companies and possibly more” – USA Today – Technology Live

Firefox 4 – Do Not Track Feature

When enabled by the user, Firefox’s Do Not Track feature works by inserting a Do Not Track HTTP Header into the request sent to every website you click to requesting no clickstream tracking. This header tells the receiving website(s) that the user would like to opt out of the Online Behavioral Advertising (OBA).

“The idea is to standardize a way of asking people to not track you, and then send that to everyone,” says Mayer. “You’re relying on the honor system for people not to track you.” The Firefox approach is “essentially a universal form of an opt-out cookie that goes along with every request to every ad network. Not just those on the NAI list or other lists.”

As opposed to actively managing opt-out lists, the user can simply turn the feature on to start sending the opt-out request to every server by default.

While this feature is the closest to being ideal in terms of comprehensiveness and ease-of-use, it may still take a few more Federal Trade Commission (FTC) rulings before the concept can work as intended, as the onus is now on the receiving servers to honour the opt-out request while there is still no clear FTC requirements as yet that defines which servers can and cannot track what.

Google Chrome 10 – Opt-Out Browser Extension

Google Chrome requires the user to install the Opt-Out browser extension as the privacy feature. This browser extension allows users to ask to opt out of being tracked by the Network Advertising Initiative (NAI) members. The participating members then embed a cookie in the user’s browser noting that request.

Upon such request, some NAI members stop tracking the user’s clickstreams while some other members merely stop sending targeted ads to the user and carry on with clickstream tracking. Should the user ever delete his or her cookies — which is wise to do periodically, for security reasons — the NAI’s opt-out cookies get wiped out, too. The primary added functionality of Keep My Opt-Outs is designed to “prevent accidentally clearing those cookies,” says Mayer.

In Summary

To me, after analysing these features in more detail, what has lately generated much hype in the market seems to be a great concept that is currently lacking in execution due to no follow-up regulation to enforce the opt-out requests.  However, in the future this may be resolved to allow the features to function as intended.

What about Web Analytics?

I can see some concerns out there on how these Privacy Features (especially “Do Not Track”) will impact the web analytics tracking.

My estimation is that the above is a misconception and that there should not be any real impact at all to Web Analytics. Here is why:

1. Rate of usage: Early observations seem to indicate heavy user involvement in setting those features up properly, especially for the Google Chrome browser extension install and IE9’s opt-out list management. This would limit such usage to a minority rather than majority-by-default.
2. Blockage Target: All the three Privacy Features specifically target 3^rd party cookies, which is the type of cookie normally used by the ad networks. There is currently no indication that 1^st party cookies are going to be impacted. And since most web analytics (Google Analytics included) use 1^st party cookies, the data integrity will very likely remain unaffected by these do not track features.

OTHER SOURCES:

• http://content.usatoday.com/communities/technologylive/post/2011/01/what-new-privacy-features-in-chrome-firefox-and-ie9-really-do/1
• http://firstpersoncookie.wordpress.com/2011/01/23/more-choice-and-control-over-online-tracking/
• http://donottrack.us/
• http://www.open-mike.org/entry/thoughts-on-do-not-track
• http://blogs.msdn.com/b/ie/archive/2011/03/14/web-tracking-protection-minimum-standards-and-opportunities-to-innovate.aspx
• http://www.kaushik.net/

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