Posts Tagged ‘google analytics premium’

Nov 07

This is the video to our sampled data post for those of you who prefer visual over reading!
For sites with heavy traffic, Google Analytics might sampled your data, which may make it hard to gain insights (the data might even become unusable). Don’t worry though, there are plenty of other options to work around that challenge. The video explains a couple.

Do you have any solutions? Tell us in the comments.

Oct 09


Google Analytics is a very powerful tool, but I think we’d all agree it might be a bit much to expect it to process monster amounts of data on-demand and return reports instantaneously without a tiny tradeoff. Some heavier trafficked sites expecting instant reports from their oceans and oceans of data (which obviously take time to generate) instead find themselves running into sampling issues in their reports – where GA is forced to make it’s calculations based on a smaller sample size of the overall data to get a report instantly. The problem is, sometimes that sample might not be statistically significant or sufficiently representative of the data, so any insights contained in the data aren’t…well…accurate.

In general, sampling isn’t an issue if all you’re looking at are the standard out-of-box reports, because they are all unsampled. However, when leveraging GA’s segmentation capabilities (which is where the real beauty of deep insights resides), whenever a data set is greater than 250,000 or 500,000 sessions within a selected time period, sampling might come into play.

Sampled data is just that….it’s sampled. It’s not fully representative of the actual data. While Google Analytics has an intelligent algorithm to ensure that sampling minimizes adverse effects on the data, the reality is that a dataset that is a 5% sample of your actual data really isn’t usable. How you determine what is usable and what is not, really depends on the nature of your data, and the type of analysis being performed, but in general, it’s best to keep the sample size as high as possible. These reports will undoubtedly be used as a reference point for marketing decisions, so it’s important that they’re accurate and provide actionable insights.

Is the Core Reporting API a solution to this dilemma? Not entirely. Sampling isn’t solved with just this API, even if you have GA Premium because the API has the same sampling thresholds applied to it as GA standard.

So what to do?

Hold tight, the following are 4 solutions to help you get clean data and clean insights again!

1. Reduce the date range.

The first solution is to reduce the date range. When looking at a report (for which you’ve met or crossed the sampling threshold), the interface displays that the report is being sampled. Instead of looking at the entire month all at once, it may help to look at a smaller timeframe, such as a week. This way, only a subset of the data is being viewed and thus, the report that is pulled contains less sessions, which keeps us under the sampling threshold. You would have to look at subsequent weeks one at a time, which is a bit mundane, but once this is done, you can aggregate this and other date ranges of the same report outside of GA, into a single report. Read onto the next solution find out what to do with all those reports.

Note: The only way to export unsampled data directly is to be a Google Analytics Premium customer. There are some third-party tools available for non-GA premium users discussed below. These tools are designed to reduce but not eliminate the effects of sampling.

2. Query Partitioning.

One way of reducing the effects of sampling is to break up the timeframe into smaller timeframes. For example, a year of data can be pulled as 12 separate months (12 separate queries), or a month of data can be pulled as 4 separate weeks (4 separate queries). For example, instead of pulling data for all of 2014, I can pull Jan. 2014, and then pull Feb. 2014, and so on. Obviously, we all have better things to do… A featured called query partitioning, available in tools such as ShufflePoint and Analytics Canvas (more details below), does the above for you in an automated fashion. The tools partition the query and programmatically loop through the desired timeframe, aggregating the report back together once done. This way, when you pull the report, the tool would appear as if making one query but in reality it’s making the number of queries behind the scenes, based on how you configure granularly you define the query partitioning. It may take some experimenting to find a balance between speed and accuracy (sample size).

More detail about the tools:

  • ShufflePoint has a drag-and-drop interface that supports Google Analytics and a few other Google products. The nice thing about ShufflePoint is that it uses Excel’s web-querying capability, so you can write SQL-like queries to retrieve your data, make built-in calculations and display the data essentially any way you want.
  • Analytics Canvas is another tool which allows you to connect to the Google Analytics API without coding. Analytics Canvas uses a “canvas” in which you can construct a visual flowchart of the query and subsequent transformations and joins of your data,to show what series of modifications will take place. It also allows for automating data extraction from BigQuery. If you using Google Sheets for your data, Analytics Canvas has an add-on in Chrome that allows you to create dashboards within Sheets.

Both of these tools have the functionality of extracting your data from Google Analytics and analyzing and creating reports.

3. Download Unsampled Reports.

If you are a Google Analytics Premium user, you can download unsampled reports (you will have to export them). Google Analytics just announced an exciting new feature available on Premium accounts called Custom Tables which allows you to create a custom table with metrics and dimensions of your choice (although there are some limitations). In other words, you can essentially designate a report that would otherwise be sampled, as a “Custom Table” which is then available to you as an unsampled report, similar to the out-of-box reports. You can create up to a 100 Custom Tables. This is awesome because you won’t have to worry about the sampled data for the reports you use often.

4. BigQuery.

If you have Google Premium, it integrates with Google BigQuery which allows for moving massive datasets and super fast SQL-like querying. It works over the Google cloud infrastructure and is able to process data in the order of billions of rows. GA Premium allows for your data to be exported daily into BigQuery. In the Core Reporting API, the data is sampled at the same threshold as in GA Standard. BigQuery allows you to access unsampled hit level data instead of the aggregate level data within the user interface, which in turn opens doors for very powerful and previously impossible analysis!

Here is an examples of the type of analysis possible with BigQuery to help illustrate its use.

  • What is the average amount of money spent by users per visit?
  • What is the sequence of hits (sequence of clicks, pages, events, etc)?
  • What other products are purchased by those customers who purchased a specific product?

For more details, visit here.

There you have it, four solutions to help you deal with sampling! Happy Reporting!

Aug 28


It’s easy to sign up for cool tools without really reading the full terms of service, especially when those services are complimentary. Google Analytics is one of those “cool tools”! Really robust, really useful, sometimes so useful, our business depends on it. But do we really know the limits?

You’re garnering leads, conversion rates are increasing, the Key Performance Indicators you’ve chosen are displaying progress, and the hits to your website are sky-rocketing! Life is good when suddenly an error message starts flashing on your reporting dashboard – you’ve exceeded 10 million hits per month!

Why did you hit the data limit? Well, it’s part of the terms of service we neglect to read :( . Google Analytics terms of service states, “…the service is provided without charge to you for up to 10 million Hits per month per account.”

Is it really that big of a deal? If you’re hitting the limit, probably. Reason being, most likely at this volume, your data is important, and Google Analytics will automatically start sampling your data. That means not all your data will be available in your interface, which could lead to inaccuracies and limit the insight you can derive.

All the work and investment you put in your digital properties – websites, mobile website, mobile apps, to become one of the leading businesses in your industry – it’s obviously important to accurately analyze your data and have as much access to it as you can.

You have 3 overlying solutions:

1. Upgrade to Google Analytics Premium

The first is the easiest most straightforward way to overcome these limits. Upgrading to Google Analytics Premium gives you 1 billion hits per month, not to mention access to “amped-up” reporting features for your business. Plus, you won’t be going at analyzing your data alone – it also includes technical and implementation support, which at this volume, can be a big help.

It is indeed a paid service, but it’s worth it to avoid the hassle and get the proper processing power and support you need for your growing organization. For more information and consultation about Google Analytics Premium, contact E-Nor.

2. Limit Hits to Google Analytics by Setting Your Own Sample Rate

Since Google Analytics standard only allows a number of hits, the second option you have is to report/send fewer hits to your analytics account. A “hit” on a site is a pageview, event or any other transaction. The reason this may help is that a single visit could potentially be registered as multiple hits. That is, say a user interacts with your website and views multiple pages, fills out a form and purchases a product, these interactions will be reported as multiple hits, regardless of all these actions being associated with one unique visitor or even one visit. Setting your own sample rate will minimize the hits, but put the control in your hands (rather than letting the system choose the sampling rate for you when you’ve passed their limit).

This is done at the code level. Talk to your developers about setting a new sample rate using the _setSampleRate method in the tracking code. This method allows you to choose the number of visits after which you want to count. For instance, you choose to track one visit after every 10 visits, so your sampling rate is 10. Say you have 100,000 visits a day. If you track with a sample rate of 10, your visits will be reported as 10,000 hits. Thus, allowing you to reduce your overall data limit.

3. Selective Tracking To Minimize Hits

If you’re only interested in certain types of hits, you may be able to get rid of what’s unnecessary. For example, since every Event counts as a hit, you may want to re-strategize and figure out what Events are really necessary. Instead of tracking every unique user-interaction as an Event, pick and choose what’s important to track so that the tracking code doesn’t record excessive hits and inflates your data limit.

If you are a video heavy website, you may not need to track everything, such as when the user starts the video, when they pause the video, have reached midway, and/or reached the end. You might want to simply track when the user starts the video or when they’ve completed watching the video. This cuts downs the number of times tracking code is fired and lowers the hits.

If you’re an ecommerce website, and heavy on products, you don’t have to track every single metric involved, maybe only product pages visits, shares, social interactions, Add-to-Carts and so on. Once again pick and choose which metrics will best reflect your website’s performance with selective metrics.

Stay tuned for our more in depth article on tips to deal with sampling.

May 29

Our team is at the Google Analytics Summit (exclusive to Google Analytics Certified Partners and Google Analytics Premium customers!) learning about the exciting new initiatives in store for the product. Most of it is top secret, but here are the things we can share.

Enhanced Ecommerce

Google will be revamping their Google Analytics Ecommerce capabilities to be more inclusive of the entire customer experience, shoppers behaviors and conversion path (rather than it’s traditional focus, which was strictly on purchases and product information). They’ve announced a beta release of their upgrade.

This will include detailed metrics out-of-box on:

  • Product detail views
  • ‘Add to cart’ actions
  • Internal campaign clicks
  • The success of internal merchandising tools
  • The checkout process
  • Purchase

Flexible and Scalable Reporting (including Unsampled Custom Tables)

Google has upgraded to some general enhanced reporting features, namely Unified Channel Groupings, traffic is now classified in-line with your unique channel definitions, and expanded Dimension Widening (now known as Data Import), which enables users to import more types of their own data and stitch it into the system.

But what really has us giggling like little children is the unsampled Custom Tables, available for Google Analytics Premium users only. For really high traffic sites, GA Standard will sample your data, which sometimes makes it hard to calculate accurate metrics. While Google Analytics Premium gives you access to unsampled data, the only way to access it was to export it.

But now, using Custom Tables you can have tables in your custom reports with pure unsampled data, which you can now crank the data through the powerful slicing and dicing of the GA UI.

There are some limits – some features like the user metrics, Flow Visualization, Search Engine Optimization, Multi-Channel Funnels, and Attribution are not available, and it takes 2 days from creation of those tables for the data to appear, but it’s a step in the right direction.

Enterprise-Class Features

For Google Analytics Premium users, you now have new integration with DoubleClick Campaign Manager and DoubleClick Bid Manager, so advertisers can get deep insights into their robust DoubleClick paid ad campaigns by leveraging power of Google’s premium analysis tool (as well as being able to combine this data with their other organic metrics).

Finally, again, for enterprise customers that manage many accounts, Google is giving acces to 4 new APIs:

  • (1) Provisioning API to create new GA accounts (invite only)
  • (2) The AdWords and (3) Filters API to manage configurations
  • (4) Embed API to surface key reports and dashboards

Roll-Up Reporting

Once again, a feature available only for enterprise customers using Google Analytics Premium. We happen to have a premium client on several domains and digital properties. They have a ton of verticals, several versions of each one, testing which has the best conversion, for example, 6 sites generating real-estate leads in different ways, 10 sites generating car leads, etc. You could even throw in some mobile apps.

Say they wanted to make an overall comparison on the performance of all their digital properties. Normally, they’d have to export then stitch everything together in an outside program.

Roll-Up Reporting is now built into Google Analytics premium – a single interface that aggregates all your site and app data into one place. A “master” Dashboard, Real-time, etc.

A huge benefit is truly holistic, universal analytics – a single view to really see your customers journey.

Coming Soon!

Most enhancements will be available immediately or within the coming weeks.

For more details, read the full Google Analytics blog announcement here.
For more details on Enterprise Roll-Up Reporting, click here.

May 01


How much of your page are visitors really reading?

If you are a publisher, a media site owner, or a content manager, you probably want to know how people interact with your content. Are they reading the full article or are they just reading the headlines and bouncing? Did the intro paragraph engage them enough to read the whole article or did it turn them off? Even for eCommerce and B2B sites, you should certainly be interested in your landing page performance and how engaging your copy is. Are visitors seeing your marketing assets or are your calls-to-action not event getting noticed?

Why should I care if people are scrolling or reading my content?

Generally, if visitors are not scrolling down your pages, it’s probably a good signal that your content is of no interest to them.

If your business model relies on engaging your audience with great content, such as a publisher or a media site, you could save time and maybe money by focusing your content writers’ efforts on what is getting traction. Knowing what keeps users engaged will help you refocus your content development plan and content marketing strategy (and get rid of what doesn’t interest your audience).

If you’re selling a product and all the persuasive “sales” points (or worse, your call-to-actions or conversion buttons) are below the fold and aren’t being read, your conversion rates and bottom lines are going to hurt.

The above is also very applicable to government sites. Analytics professionals at federal agencies can leverage insights from content analysis to inform content marketing and optimization strategies for the agencies.

Let’s take the website We’re on the “How Can I get Coverage Page” and I’ve taken a screenshot of how the page looks on my desktop browser (see below).
Apparently, the “Apply Now” button is below the fold, as well as some other important deadlines. Are these deadlines being read? If the “Apply Now” button is getting a low number of clicks, is it because people are seeing it and not choosing to click it or are visitors not even getting that far down the page? If we could figure out how many people get that far down and it turns out to be a low number, we could potentially optimize conversion by placing the button higher on the page.

Either way you slice it, knowing what portion of the page your visitors are reading allows you to optimize your site in a way that could lead to a better user experience and a more loyal audience.

Complementing Your Heat-mapping/Mouse tracking tools

You could always use cool heat-mapping tools like, to see which areas of the page are getting the most attention and what links are being clicked. But maybe your are in a hurry and don’t have the time or the budget to acquire a new tool (and you really like to stitch the “scroll” data nicely with the rest of your Google Analytics/Google Analytics Premium reports).

  • With GA, you can create advanced reports/goals/segments based on the hits provided (ex. tie the page scrolling events with a contact us goal)
  • Compare page performance
  • See the reports in whatever format that fits your needs, and also export them with the same Google Analytics scheme
  • Trend and compare to past analysis (e.g. month-over-month page performance)

Implementing the Page Scroll Tracker using Universal Analytics

While there have been some great articles written about this already (Part 1 and Part 2 by our friend Justin Cutroni), we wanted to show this approach using Google Universal Analytics.

In Universal Analytics, there might not be a specific metric for this, but we can create “clues” that at least indicate how far a visitor has scrolled down towards the bottom of the page. We can track, for example, if a visitor has scrolled down 10%, 25%, 75%, etc, towards the bottom of the page.

The implementation process itself is easy, but you’ll need to know HTML, a little bit of JavaScript/JQuery, and of course, a good understanding of Google Analytics to read the segmented reports and slice and dice the data.

Using Javascript/JQuery, we’ve customized the code to send a Google Analytics event every 10% increment the reader scrolls down. See the code below.

Note: You can adjust the algorithm to send events based on intervals of your choice – ex. 25% or 50%.

Step #1: Make sure you’re referencing the JQuery Library.

You’ll need first to confirm whether your website already references a “JQuery” library (or not). If your website already does, you can skip this step. Otherwise add this to the header of the page.

<script src=""></script>

Step #2: Page scroll tracking plugin code
At the bottom of ALL your website HTML pages, just before the closing of the tag, add the following code.

<script language="javascript">
//     This script is used and customized to measure the page scroll / interaction with Google Universal Analytics.
//     Author: E-Nor Inc.
//     Created By: Mohamed Adel
//     Last Update: 04/25/2014

/** Predefined variables **/
EventNONInteraction = false; // This variable determines the event will be a noninertact event or not
Frequency = 10; // This variable determines the Frequency the event will be fired, MAKE SURE THE NUMBER ENTERED CAN BE DIVIDED BY 100 (10 means each 10 precent the event will fire)

GA_EventCategory = 'Page Interaction'; // Google Analytics event category
GA_EventAction = 'Scroll Down'; // Google Analytics event action.

/************ DON'T EDIT BELOW THIS PART ************/
_frequency = Frequency;
_repentance = 100 / Frequency;
var _scrollMatrix = new Array();
for (ix = 0; ix < _repentance; ix++) {
    _scrollMatrix[ix] = [_frequency, 'false'];
    _frequency = Frequency + _frequency;
$(document).scroll(function (e) {
    for (iz = 0; iz < _scrollMatrix.length; iz++) {
        if (($(window).scrollTop() + $(window).height() >= $(document).height() * _scrollMatrix[iz][0] / 100)  && (_scrollMatrix[iz][1]== 'false')) {
            _scrollMatrix[iz][1] = 'true';
            ga('send', 'event', GA_EventCategory, GA_EventAction, _scrollMatrix[iz][0]+'%', {'nonInteraction': EventNONInteraction});  

You’re done!

NOTE: If you are a Google Analytics Premium user, you’ll have a quick access to the data. If you are on GA Standard, allow a day or so and then check your Google Analytics interface for events and you’ll find an event category called “Page Interaction”. Within the event label, you’ll be able to see the percentages you set.

Through advanced reporting techniques, you can now aggregate pages based on the percentage of page scrolling!

Customizing the Code

In the code above, you’ll notice that there are 4 variables that you can customize:

  1. EventNONInteraction: If this variable value is set to true, the event will be set as a noninteract event in GA, and will not affect the reports in Google Analytics.
  2. Frequency: This variable determines the increments or intervals that will send events to Google Analytics. For example if you set the value to equal 25, this means that the Plugin will fire the Google Analytics event when reaching to 25%, 50%, 75%, and 100% of page scroll. In other words, this is the percent interval that will be tracked for scrolling, so it’s important that it’s a clean factor of 100.
  3. GA_EventCategory: This variable determines on what do you like to see the event category in Google Analytics. You can change the category name to your liking.
  4. GA_EventAction: This variable determines on what do you like to see the event action in Google Analytics. You can change the action name to your liking.

How It Will Look

Looking at the example report above, we see the following:

  1. The first 25% of the page has been shown 7,587 times
  2. There are 5,045 users have scrolled to the middle of the page (50% Scrolling percentage)
  3. There are 806 users have scrolled to the third quarter of the page (75% Scrolling percentage)
  4. There are only 514 users who have scrolled to the end of the page (100% scrolling percentage)

Let’s pretend this is the example from above. While you would have thought the FULL page was viewed 7,587 times, it seems only 60% are scrolling halfway down the page, only 7% of these visits scrolled to the bottom of the page. Since the “Apply Now” button is below the half way mark, that means only 60% of those visitors even saw it. Placing it to the top may increase task completion rates.

If your great content, calls to action or product/service benefits are located at the bottom of your pages, it’s time to re-think your page layout/content structure and improve your user engagement. You can (and should) dig a bit deeper and segment each of your channels and assets to see which campaigns bringing engaged users and which campaigns are bringing you bouncers!

Next Article: Measuring Responsive Scrolling

What happens when your design actually looks different on different devices? Your conversion button may be below the 50% mark on your desktop but not on your mobile! Stay tuned for the sequel to this post!

Happy Analyzing!

Would love to hear your thoughts and comments below.

Oct 21

63% of Fortune 500 Use Google Analytics

Two years ago, Google announced Google Analytics Premium to solidify their presence in the enterprise market for Analytics. Today, Google Analytics Premium is the leading analytics solution for highly trafficked sites as well as large-scale government-wide initiatives.

Google Analytics Premium offers more horsepower, dedicated support, and groundbreaking solutions to meet enterprise analytics needs. As business data needs grow, Google Analytics has proven it can rise to meet any demand.

But don’t just take our word for it! While we can’t disclose the number of Google Analytics Premium clients, the benchmark data speaks for itself – Fortune 500 are increasingly adopting and finding success with Google Analytics.

Usage in Fortune 500 on the Rise

63% of Fortune 500 websites now use Google Analytics. This percentage was calculated by examining the main website of each of the Fortune 500 corporations. In 2011, usage of Google Analytics among the Fortune 500 was 45%. In 2012, usage of Google Analytics had increased up to a 51% market share. This year, the trend has increased significantly, with 61 new enterprises having adopted Google Analytics or Google Analytics Premium.


Not Slowing Down

The above image illustrates that Google Analytics and Google Analytics Premium are clearly resonating within the Fortune 500. The below chart shows a 24% increase in 2013 compared to 2012, which represents a huge increase in market share.


Like I did in 2012, I attribute this to the aggressive improvements Google has been making over the past 12 months (70+ enhancements to be more precise). We have seen Universal Analytics, a new interface, Enhanced Attribution Modeling, Visitor Segmentation and an integration with Doubleclick just to name a few updates. At the Google Analytics Partners summit Google announced a significant number of Enterprise features that will be coming out (14 announcements in one day!), so I expect this trend to continue into 2014. Data integration and access is a core need of the Enterprise market. Google is clearly taking these needs seriously, as is shown by Google announcement of their Cloud Platform and the availability and integration of Google Analytics Premium hit-level data into BigQuery.

Method: Data collected using Ghostery and analyzing the main website for each Fortune 500 company. Numbers in the bar chart add up to over 100%, due to some companies deploying multiple Analytics Tools.

Last years post:

Feb 27

Googel Analytics Real TimeWhen working with high volume sites, slicing and dicing the data can be challenging, and looking for insights is very much like Tom Hank’s famous quote from the movie Saving Private Ryan – it’s like “finding a needle in a stack of needles”!

At E-Nor, we’re proud to work with some of the most recognizable brands in the world. Our clients expect sophisticated measurement and deep insights, more than “ancient” basic metrics like “total visits” or “site wide conversion rates” :) .

So what do you do? One thing I’ve learned over the years (the hard way at times!) is that as a consultant, and the same applies to marketers and practitioners, you should focus on what matters most to the business. Find creative ways to measure/report/analyze and find “gems” that truly impact the business. Along the way, that will make you look like a hero (and will ensure long-term employment!).

In this post, I’ll cover the challenges and solutions for understanding user behavior and cohort analysis across multiple platforms. The business we’re using as example is a large media organization where you “Sign Up” to access content. You then have the option to upgrade and buy a “Premium Subscription” as well as access to buy exclusive products and services. And in case you are just getting used to this type of analysis, a typical cohort analysis will show you the behavior of a “class” of visitors, typically segmented by an action on a specific date or date range.

We’ll cover:

  1. The Cross-Platform Challenge
  2. The Measurement Approach
  3. Integrated Reporting & Insights

1- The Challenge – Web, Mobile and Offline

In this multiple device, multiple platform world, understanding user behavior and measuring “what matters” require planning (and a bit of process) way before jumping into tagging and JavaScript. Especially as we start shifting our thinking from measuring visits and conversion rates, to visitors and customer lifetime value. I won’t get into the technical instrumentation of cohort analysis (just Google it, you’ll find many posts on the basic “how-to”). I’ll focus on the user-centric approach and marketing insights.

“Out of the Box” Cohort  - Mixpanel Analytics
Some analytics tools have cohort analysis built in their standard reports. For example, you could be using the Mixpanel platform (and assuming you’ve planned and implemented your events properly), you’ll get very meaningful cohort reports that can shed some light on user behavior over time. Say you have a content site where a subscription to access is required, you’d want to know if your users are coming back and consuming that great content you produce, if a cohort of users whom you acquired in a specific time period, are likely to be more loyal than others.

A standard Mixpanel cohort report will look like the snapshot below:

cohort mixpanel screenshot

The first column shows the date at which the “Sign Up” occurred. The “People” column shows how many people signed up on that day (e.g. 10,324 on Feb 5th, 2013) and the percentages represent the percent of people who come back after x-amount of days (where x is 1 to 12 in this chart). So for the Feb 5th cohorts, 1.84% of them came back and consumed more content two days after signing up.

In addition and without a lot of digging, you can clearly see that the segment of visitors who signed up on February 8th are super engaged in the first seven days, and they are coming back for more every other day. On the other hand, those who signed up on February 6th, behave completely different. They are interested initially and then their interest taper off.

Campaign Segmentation in Mixpanel
You are hungry for more insights, what do you do? Jump over to Mixpanel’s Segmentation reports (you can segment on the fly by region, referral and even Google Analytics utm parameters) and dig deeper to find the source of those who signed up on the 8th. You’ll notice here that the campaign that peaked on the 8th (in green) is likely to be a very good driver of such an engaged audience.

That’s good to know, but you are still not yet satisfied. The data above is from the web channel only. You are missing other acquisition channels.

In line with our cross-platform and the 360-degree customer view approach, let’s take this analysis to the next level for a large media site. Visitors register to access and share content, and with a paid “Premium Subscription” option then have access to discounted/exclusive products and services. You’d want to track user behavior across all potential channels on which a prospect might convert. A prospect can buy a Premium Subscription:

  • On the website
  • On their mobile app
  • Dial the 800 number and sign up “offline”

2- Solution Design – Analytics & Measuring Across Platforms

This large media site uses Google Analytics Premium and also has Flurry Analytics for their mobile app. But no matter what analytics platform you are using, this multiple-way conversion presents quite a challenge to cohort analysis in any one analytics product. Time to stitch and tie!

To have the 360-degree view of the customer, and stitch all the disparate data from the web, mobile and offline, there is a technical implementation aspect and a little bit of process. So yes, work is required, but the anticipated results are definitely worth all this pre-requisite hard work.

The highlights of what is needed include:

  • Web data — extract data from Google Analytics Premium
  • Mobile data — download your events data from Flurry
  • Offline — export your purchase data from your backend

And here are the details:

Web Analytics: Google Analytics Premium unsampled reports with the following metrics/dimensions:

  • Revenue
  • Date of transaction
  • Cohort date – The date the Premium Subscription took place, and is stored in a visitor-level Custom Variable 1
  • Unique customer ID, (of course, no personally identifying information), stored in visitor-level Custom Variable 2 (we will use this for the advanced user-centric tip at the end of the post)

Note: As previously stated, this is not a technical post, but here is how you’d set up Custom Variables. You’ll also need the help of your IT/dev team to coordinate the passing/managing of the unique ID in the backend system.

In Google Analytics, run an eCommerce -> Sales Report, and choose your Cohort date (CV1) as a secondary dimension, you’ll have something like this:

The table above shows the dates/revenue after the Premium Subscription date (e.g. CV1 = 20130109, January 9, 2013) during which a transaction took place, along with the revenue associated with these transactions. Now, say you want to see how the January 9th cohorts did compared to the January 12th cohorts. , Simply create an advanced segment for each of the Cohort dates and the graph will look as follows:


Here are the findings:

  • The January 9th cohorts brought in 2.84% of the revenue
  • The January 12th cohorts brought in 1.94% of the revenue
  • There were no more sales from either cohorts after January 21st

Once the “Cohort date” data has been collected, you can also plot it in Excel or Tableau in a traditional cohort chart. We will cover this later in the post.

Before we move on to more analysis, insights and actions, we still need to complete the picture. Remember, our subscribers can upgrade to a Premium Subscription using the mobile app as well. So let’s take a look at the mobile data next.

Mobile Analytics Data – Flurry Analytics come to the rescue
This client is an early adopter of mobile and has leveraged the powerful capabilities of Flurry Analytics since day one. The client has done their homework and planned out their user actions (aka Events) and did not just settle to track screens and buttons, but is also tracking outcomes like In-App Purchases (IAPs).

Similar to what we gathered from Google Analytics, in Flurry Analytics we want the following information:

  • An event when the Premium Subscription purchase takes place, date stamped for our cohort analysis (e.g. 20130109)
  • Revenue associated with purchases
  • Unique user ID (and again no PII please!). The user ID will show in your Flurry Event Log (we will use this for the advanced user-centric tip at the end of the post)

flurry event

As you see in the image above, your “time stamped” mobile app eCommerce data is now available.

We can create a segment in Flurry under Manage –> Segments and choose the Premium Purchase Date as the custom event/parameter for the segment, and then export this data (download as CSV or leverage the Flurry API). If you are downloading the CSV, your parameters (revenue, etc.) will be available in one cell in Excel, and you can easily parse the parameters you want to include in the report.

Offline – Backend Data
Well, we are almost there! Since the client’s system allows for offline Premium Subscription upgrades as well, we need this offline data integrated too. These elements are required from your backend:

  • Premium Subscription date
  • Date of purchase
  • Revenue
  • Unique customer ID

And finally, we download the offline data in a CSV file, and we are ready to rock and roll!

Note: if you are forward thinking and like to get your hand into the latest and greatest, then Universal Analytics (from Google Analytics) will be an option here. With Universal Analytics you will be able to import the offline user interactions into Google Analytics and map it to existing data.

3- Integrated Reporting – Customer 360-View, Tableau Dashboarding & Insights

If you’re using a data warehouse or a database one approach would be to present all this data in Tableau using its optimized connectors available for many data environments. If you haven’t made the investment into your infrastructure yet, or you don’t have the IT resources to leverage these connectors in Tableau, you could extract your data into CSVs from Google Analytics Premium, Flurry and your backend system and upload it into Tableau.

Now that you have your data ready from the three data sources (web, mobile and offline), it’s time to tie it all together, visualize, analyze and find some awesome actionable insights. Your common element for this analysis is going to be the Premium Subscription Date.


Aha! The cross-platform full picture is ready for you. You see the value of those who signed up on the 9th of January (in terms of sales and revenue), and you start understanding where they came from, what offer resonated with them among other factors.

Advanced Tip# 1 – Google Analytics Tableau Integration
If your business is a pure online play, and your subscribers can only buy on the website, I have a very nice surprise for you (kept this till the end to reward the loyal readers :) ). With Tableau version 8, there is a native integration between Tableau and Google Analytics (in beta but works well), meaning you can pull your dimensions and metrics directly from GA and into Tableau. You bypass the need to download files, reformat and upload. I’ll share the tips on doing so in a post by our Tableau geek extraordinaire Shiraz in the next few days. (Please note if you have a large site with high volume and you are getting sampled reports, this method will not work for you. For now, you’ll have to download the unsampled reports and then upload into Tableau).

Advanced Tip# 2 – User Centric Analytics
In-line with our emphasis on concepts such as user-centric analysis (and Universal Analytics), and in capturing data from different sources, I emphasized the need to include a unique user ID when customers buy the Premium Subscription. Once you have this common user ID (aka key) you can run all sort of sophisticated analyses in your Tableau or a BI tool of your choice. Examples include:

  • Customer Lifetime Value (LTV)
  • Recency & frequency across platforms (web, mobile, offline)
  • Segment high ticket item customers
  • Map demographics data (available in Flurry and/or your backend system) with web data
  • Plus more and more scenarios that I’ll leave for future posts

So there you have it, how to create a cohort analysis across mobile, web and offline platforms. I hope you found this post useful and I look forward to hearing your comments and input!

Jul 20

Join E-Nor’s Principal Consultant, Feras Alhlou, at the upcoming SES conference in San Francisco as he leads a roundtable forum on both Tuesday, August 14th and Wednesday, August 15th. In these “Meet the Experts” sessions, Feras will provide insight on new features in Google analytics as well as Google Analytics Premium, and answer your questions on all things Google analytics, conversion, testing and multi-touch attribution. These roundtable discussions allow for participants to learn, network and share information amongst the attendees.

If you haven’t yet registered for SES, do it now and take advantage of a 15% discount by using this special promo code: SPKRFA.

SES San Francisco is a leading industry conference geared towards marketers and SEO professionals. This 3-day conference brings people together to network and learn about topics such as PPC management, keyword research, SEO, social media, local, mobile, link building, duplicate content, multiple site issues, video optimization, usability and more. The conference takes place at Moscone West.

Again, register now, and use the promo code SPKRFA to take advantage of the 15% discount.

Jul 09

Updated: Google Analytics Solidifies Lead in Fortune 500 Adoption in 2013

Last year Google Analytics announced Google Analytics Premium, so that enterprises could derive benefit from dedicated support, more horsepower and services they require from an analytics solution. Just last week EConsultancy published a report indicating that 5% of Google Analytics users they surveyed were using Google Analytics Premium.  Many people wonder, who is using Google Analytics Premium? While there isn’t a published list, a good indicator of Google Analytics Premium’s success, could be gleaned from looking at the adoption of analytics platforms by Fortune 500 companies in relation to Google Analytics market share.

In October of 2011, our friend Stéphane Hamel wrote a blog post announcing that Google Analytics was installed on 45% of Fortune 500 websites. Another post by TechCrunch highlighted that Google Analytics is used by more than 55% of the top 10,000 websites. Both of these confirm that Google Analytics is the dominant measurement platform used across the web.

Usage in Fortune 500

I collected data over the past few months and have documented which Analytics tools each of the Fortune 500 corporations are using on their main website. Google Analytics is now in use on 51% of Fortune 500 websites. This is an increase from previously reported data showing a 45% market share.  More than half of Fortune 500 companies are now using Google Analytics or Google Analytics Premium and 30 new enterprises have switched to or added Google Analytics in the past 9 months.


Google Analytics is most used Fortune 500 Analytics Tool

Not Slowing Down

The above images illustrate that Google Analytics is on the rise within Fortune 500 Enterprise.  I attribute this to the aggressive improvements Google has been making over the past 12 months.  We have seen Real-Time Reports, Multi-Channel Funnel, Google Analytics Premium, Content Experiments, Attribution Modeling, and Social Reports just to name a few.  Google Analytics is showing continued signs of growth with a brand new Mobile SDK and completely new Mobile Reports coming later this summer.  This is all likely to lead to expanded adoption of Google Analytics Premium, due to the ever increasing sources of data, or Big Data I should say, and the ever increasing need for smart people to transform the data into actionable insights.

Method: Data collected using Ghostery and analyzing the main website for each Fortune 500 company.  Numbers in the bar chart add up to over 100%, due to some companies deploying 2 or 3 Analytics Tools.


Jun 13

For those of us using Yahoo! Web Analytics (YWA), on Tuesday Yahoo announced the discontinuation of this product for analytics-only customer accounts and the Yahoo! Web Analytics Consultant Network.  If you have a Yahoo! Store using the analytics platform, you are still supported.

We want to take this opportunity to thank Dennis Mortensen, the Yahoo! Web Analytics team, and the YWA Consultants Network for their contributions to the analytics and marketing community over the years.

Most of the analytics-only customer accounts will be discontinued on August 31st, 2012 and current YWA users are assessing the impact and looking for guidance.

Don’t worry, you are not stuck. We will help you migrate to Google Analytics and make sure the data you were measuring is captured, the reports you need, and key performance indicators (KPIs) you rely on are available to you in GA. We have a digital marketing optimization framework and a proven methodology ready to assist you in a hassle free migration. In addition, you’ll benefit from the plethora of new reports and enterprise-class measurement features available in the Google Analytics Standard and Google Analytics Premium Editions.

We’ve assisted some very well-known brands successfully migrate from SiteCatalyst, WebTrends and YWA to Google Analytics so connect with us for any questions you may have and we’ll be glad to help you plan and execute your analytics migration project.

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