The average time spent by visitors on your website is four minutes. Is this good? Or is this something to be worried about? You begin to evaluate the time spent per visit and asked yourself questions such as:
Is this enough time to complete an e-commerce transaction?
Are visitors reading enough quality content in that four minutes that will motivate them to subscribe to the email list?
Are visitors getting enough information for interest in the Contact Our Sales Team form?
And while all of these concerns may be valid, it comes down to a simple plan of evaluation and determining exactly what information you want to gather in order to reach a given outcome. Time-onsite metrics can often be very misleading when the data is viewed in aggregate, and no segmentation is applied. And the metrics are even more misleading when used to make site optimization decisions. What is missing from the standard conversion reports is a metric I believe is very important for analysts to have in order to accurately optimize the conversion funnel based on visitor’s behavior and need. This metric is “Conversion Duration” which I define as, “the time is takes a user to complete a purchase or a Goal from a defined starting event.”
I am currently aware of two e-commerce reports that are available today in Google Analytics (GA) to time track a purchase. In my humble opinion these two amazing reports (Days to Transaction and Visits to Transaction) still lack three main features:
They are limited to the e-commerce transactions and cannot be applied to track conversion time of any other micro or macro conversions (i.e. form submission, email subscription, enrollment application, etc.)
They are not applied at the session level, so we can’t tell how much time (in seconds or in minutes) a particular Goal takes to be done during the same visit.
They are applied for the last campaign only which make these metrics less robust and tell a very partial story.
So now, the question is, how do I calculate this metric?
First Things First – Determine your Goals
Whether the purpose of your website is lead generation, e-commerce, content or media, you must first define goals in your analytics tool. Do this in order to track conversions and also to track the time users take to make an action.
A Goal conversion can be a variety of things, depending on what your desired outcomes are. It could be an event registration, whitepaper download, form submission, video completion, or a product purchase. In your analytics tool you’ll be able to see the conversion rates and number of completions for each Goal you have set up. (Read more here about Google Analytics Goals.)
While tracking the number of Goal conversions is the basic metric for measuring how well your site fulfills your business objectives, other metrics need to be associated with the conversion rate to deeply understand the customer behavior both before and after the conversion.
Define your Starting Point
In order to accurately track the time is takes visitors to make it through a given process that you set for them, we need to define the starting point. This can be entering first page of a six-page application form, or when they reach the Add-to-Cart page for an e-commerce funnel, or even the Contact Us page. Regardless of the Goal that you are trying to track, make sure the starting point is an event that all visitors are required to go through in order to complete the conversion.
Develop Metric Calculations
Now after defining the starting and final conversion points, we need to set a timer between the two points. This Conversion Duration metric is calculated using the following simple equation:
Conversion Duration = (t2 – t1)
t1 = The timestamp of the first success event (i.e. landing page, add-to-cart, about-us page, etc.)
t2 = The timestamp of the second success event (i.e. form submission, thank you page, transaction confirmation page, application confirmation page, etc.)
How to Track the Conversion Duration in Google Analytics
While there are various ways to report the calculated metric to GA, in this post we will use the relatively new feature, User Timings, to pass the period of time spent in the conversion. With the new introduced feature you can track and visualize user defined custom timings about websites. I will provide you with the code needed to capture the conversion duration.
The easiest way to implement this is to create a timestamp at the starting point and create another timestamp at the completion of the Goal. Then, we calculate the difference between both timestamps and pass it to GA.
Here’s the process:
Create a timestamp at the defined starting point.
t1 = new Date().getTime();
Save the timestamp in the cookies or in the backend.
Create a timestamp at the end of the conversion.
t2 = new Date().getTime();
Calculate the time duration from start to the Goal completion. (In GA the time difference between the two timestamps is in milliseconds)
Once this data is collected, the report will consist of the Conversion Duration metrics as well as the number of Conversions, as seen below.
Go to Content > Site Speed > User Timings to see your data.
So there’s our recommended process for developing the metrics and calculate tracking using Conversion Duration in Google Analytics. This will provide you with the steps to reach your business goals when it comes to determining the passing period of time in site conversions.
We would love to hear your feedback on how you’re planning to utilize this method to work for you! Please share your thoughts with us and the analytics community
Update 07/15/2014: Google Analytics has changed “Profile” to “View”.
Previously, Google Analytics had only 2 roles (“Admin” and “User”), which are very limited. They announced today that they’ll be expanding the flexibility of access – which is good news for anyone that has multiple hands in their Google Analytics accoqunt cookie jar (you have multiple accounts/properties/views but want to give…
…different employees different access/security roles to specific properties/views.)
…particular clients/agencies view or admin access to specific properties/views, etc.)
Google Analytics Accounts – this is the general account you set up for analytics. It would be on an organization basis. Example, an analytics account for E-Nor, Sony, Proctor and Gamble, etc. This is the large bucket and you get an assigned ‘UA’ number.
Properties – Under the account, you can have multiple “properties” – website 1, website 2, mobile app, etc.
Views – Within each property, you can have multiple views. Maybe for your website, you want a view that only shows E-Commerce data. Another example, say you’ve filtered out all your company traffic from your view so you only see data from “visitors”, not your developers doing work on your site. You also have a backup view (always have an unfiltered control back up view!!!). Etc.
Users – In order to give someone access to a Google Analytics account/property/view, they must have a Google Account.
The “Old Ways” and its Limitations
As it works now, the user permissions are pretty limited. You can only have admin access at the account level (all properties/views under the sun). Nothing in between.
If you have multiple properties (say website and mobile app), and you want to give a specific user/client admin access to only a portion of that (say, yet you want say the website development team to only have access to the website property/views), this wasn’t possible.
User “View Only” Access per View
Also, you could only give users “view only” access per view. If you wanted to give users “view only” access to all views within a property, you’d have to manually select each view under that property and give them access.
Enhancements and Improvements
User Permissions at Every Level!
Analytics users have been requesting more granular user permissions. Today, we got the answer from the Google Analytics Blog. They’re expanding these options and will allow you to basically set access at every tier. Set users to have view/admin specific to properties, views, and/or the entire account. No more worrying about a user having too much admin access. No more having to manually select views per property – you can set access to whole properties.
We now have the ability to give different access specific to each tier.
For example, while a user can now have permission to say view the entire property, you can also limit their admin access within that property (per view). Example, give the website developers access to view the entire web property, but only edit the E-Commerce view.
The only trick is admin access inherits permissions from its parents. Meaning, if you have full account admin access, it wouldn’t make sense then to only have “view” access to specific properties/views under that account – you have full access.
New User-Role: Manage Users
We know that today Google Analytics has 2 roles of access – “admin” access (which gives users permission to edit Google Analytics accounts/properties/views) and “view only” access.
They’ve now added a third role called “Manage Users”, which allows that that user to add and delete other users as well as assign them permissions. This is different from “edit” and “view” permissions.
Pretty cool! Give certain employees/agencies specific access so they don’t mess up other views! (NOTE: Your account may not yet have this feature, but Google is working on migrating all accounts to have this within the upcoming weeks, so keep checking for it).
A couple of weeks ago, I wrote a post about marrying qualitative and quantitative data, giving recommendations on how to conceptually combine both types of data for more useful insights. For the savvy marketers and the analysis ninjas out there, you want a tighter integration, and of course, more segmentation on the qualitative data. Visitors told you “No, I didn’t accomplish what I came to the site for” and “that pricing was too high”. Now, maybe you want to know…
…where these “No’s” and “Yes’s” are coming from
…what content they are consuming
…what campaigns are driving these segments so you optimize at the source
Google Analytics – Quantitative Data Tool of Choice
It’s no secret that Google Analytics is our web analytics platform of choice. It’s a “monster” when it comes to providing web usage stats. It’s the gold standard. And as far as pricing, the cost benefit is unbeatable – it’s free (and if you want more data and enterprise analytics consulting, Google Analytics Premium is there for you). When planned and implemented right, you can get a wealth of qualitative data on your website – what pages are visited, where they come from, what keyword they searched for, how long they stayed, who converted – etc etc etc.
Qualaroo – Non-Invasive Voice Of Customer Survey
As far as qualitative data, Qualaroo can be your best friend. As an internet surfer, I’m sure your feelings are consistent with the rest of the world – forms and surveys are kind of annoying. They’re long, tedious, and often a waste of time. What we like about Qualaroo is how non-invasive it is. A small question will pop up and a visitor has the option of quickly answering it, or quietly dismissing it. We feel this increases the likelihood of it being answered.
We also love its configuration flexibility. “Nudge” the question after 30 seconds. After a visitor has visited 2 pages. Publish the survey from a certain date, shut it off after a certain date. Display only on certain pages. Etc. Note: to integrate Qualaroo data in Google Analytics, you need to sign up for Qualaroo’s enterprise edition.
Google Analytics and Qualaroo Integration
With the ability to integrate Qualaroo data into GA, you now have a very powerful surveying, measurement tool. You can see “what’s” happening on your site by looking at the Google Analytics metrics. Then, with Qualaroo integrated into Google Analytics, you get a deeper understanding about “why”. Google Analytics itself will tie the connections between your qualitative data and quantitative data, between your “what” and “why”.
ROI by Channel
Here’s a simple example. Your company sells a SaaS product targeting CMOs and Marketing Managers at medium and large companies. You have your paid search campaigns running on AdWords and Bing and you want to expand into new paid social ad campaigns. You have ads on the following social networks:
Your landing page is converting, but it’s not converting like it should be, and you’re looking to really cut down your ad spend and optimize ROI. Your quantitative numbers tell you the following:
Your spend is about equal (budget was set by yourself).
There actually is a lot of traffic from Facebook and Twitter and LinkedIn.
However, it looks like you are getting very few conversions.
“Why is that?”
“Should you continue spending on these channels?
If you shut down these campaigns, what opportunity are you missing?”
Thus, we add the following survey:
Your goal is to understand what’s happening and why your visitors can’t find what they are looking for and thus not converting. With Qualaroo Enterprise version enabled, some cool Qualaroo event user interaction can be reported in GA (an event is fired based on user interaction with the survey). The image below shows the number of events when responders answer “No, we didn’t accomplish what we came here to do”. Here is what we see in GA (go to Content –> Events –> Top Events –> and then click on the Qualaroo Enterprise Event Category):
Your first reaction is , so many people (and a high percentage of the total responses) can’t find what they are looking for, no wonder they are not converting. Time to dig deeper and segment.
As we know, once you have the event captured in GA, you can make it a goal or segment on it in whichever way you like. In our case, we want to understand where the No’s are coming from. Let’s take this specific event and segment the report by channel (apply secondary dimension to the Top Events Report) and voila! You have the channels responsible for driving the unengaged visitors.
In the snapshot above, you see LinkedIn is a potential culprit here. We love LinkedIn and we’d expect it to generate high value leads, what’s going on here? Our paid ad LinkedIn campaign appears to be bringing visitors who are not interested in the SaaS product. After verifying the statistical significance (and correlating the number of “no” events with the traffic volume from the respective source) and looking at some of the survey responses, so of the answers to tell us why you didn’t find what you came here for were very revealing, here is a sample of these answers, once you read them, you’ll know exactly why the campaign is not converting.
”I can’t find the technical product description”
“your product appears to be solid, but I am interested in customization features and API import/export capabilities”
“how technical does the system admin need to be, I can’t tell from the info provided”
“I am a system admin and I need to know your system up-time and technical support you provide, can’t find this information”
Most of the answers/comments were of a technical nature, and appears to come from IT folks and your LinkedIn ad campaign (including the landing page) is all designed around speaking to the CMOs!
These insights are very actionable. Two immediate actions are now taken to adjust our targeting options, and really optimize our ROI (by maybe putting our budget towards these better channels).
Cost savings: Enhance the LinkedIn targeting to marketing managers
New opportunities: create a new campaign/new landing page with information requested for the IT folks.
ROI by Keywords
Now, say you’re spending on Google AdWords. You really want to know if specific campaigns or keywords are driving the traffic and unengaged visitors.
Again, we take the specific event and segment the report by keyword (apply secondary dimension to the Top Events Report) and voila! You have the culprit keywords.
Is this super insightful or what?! (keywords blurred to keep our clients identity anonymous…)
Stop bidding on these keywords or direct traffic from these keywords to a lower price-point product and see your sales go up like you’ve never seen before!
Here you have it, if you feel like you are hitting a wall and not sure what to do to improve your conversion rate, go back to the basics and “listen to your customers”!
How have you used voice of customer tools to improve your site and campaign performance? Very interested to hear your stories.
“I…i…i…i… I’m soooo in love with you… whatever you waaaant to dooo… it’s alright with meeeee…!” – lyrics from the “Reverend of Soul” himself – Al Green.
It’s Valentine’s Day, love is in the air! So, we thought we’d shoot cupid’s arrow through this next post about data and measurement. Give it some “love”, so to speak.
If you’re heavy into Google Analytics, you might be “infatuated” with quantitative data - “views”, “visits”, “bounces”, etc. – and…ahem…”RAW” numbers. That’s only one “better half” though. That is, it’ll tell you “what” is happening on your site. However, what about the other half – “why”??? One way to answer that question is by “marrying” your quantitative analytics data with qualitative, “voice-of-customer” survey data.
“Why… why do people break up? Then turn around and make up… I just can’t seeeeeeee….”
Let’s take a statistic of love. Unfortunately, it looks like approximately 50% of marriages fail [Cupid, what the hec are you shooting at?] The Reverend of Soul, Al Green stated in his immortal lyrics, that he could see “what” was happening, but for the life of him, just couldn’t see “why” people break up!!!
This Valentine’s Day, let’s pretend to try to solve this urgent matter of the heart. WHY is this happening?! Is the aforementioned statistic enough for you to take action? Doesn’t look like it.
Those of you who’ve been through a tough time in your relationship know it’s not black and white – there could be a plethora of reasons:
You might have grown apart
Stresses like money could have strained the relationship
Communication could be missing
As any counselor would tell you, they wouldn’t just diagnose “what” is going on in a couple’s troubled relationship, then right away present solutions. In order to solve the issues, they would go into several deep counseling sessions so they can get to the nitty gritty of “why” this is happening.
Similarly, diagnosing “what” the issues are with your website is only part of the process. Voice-Of-Customer qualitative surveys could potentially give you insight into “why”, insight that may not be readily apparent from quantitative data.
“Let’s Stay Together” – Putting Voice-Of-Customer Together with Quantitative Data
You have several ad campaigns leading visitors to a landing page asking them to register for your event. As the example figure below indicates, your analytics may be telling you that your landing page is getting all the traffic you’re paying for, from Facebook/Google Adwords campaigns, etc. You’re spending good money, and your micro conversion, say an agenda for your event, is getting all the downloads you want. But according to your data, the bounce rate is really high and no one is registering for your conference. Why?!
We recently ran into a similar situation with a project our consultants were assessing, and tried to figure out the issue:
Was there not enough sales information? We listed all the bullet points…
Did clients not trust the organization/service? We listed impressive certifications and testimonials…
Was the registration call-to-action button not clear enough? It’s big and yellow, and it looks like people are clicking on it according to our data…Just not completing registration
At this point, even if you had all the unsampled data in the world from Google Analytics Premium, you’d still be stuck. There’s always an option of implementing A/B testing, but that would take time and coding. Plus, what do you start testing? You can make educated guesses, but you’re pretty much taking shots in the dark (like cupid).
So even before we explored A/B testing, we tried implementing Qualaroo – an awesome, quick, non-invasive survey that quietly pops up based on criteria you set (ex. 30 seconds on the page, after 2 pages have been visited, etc.). You can ask any question – open-ended, multiple-choice, etc. And after each survey is complete, get reports in the native interface or download reports into .xls for deeper crunching.
So we asked – “Second thoughts about registering? Why?”
Sample responses were as follows:
As you can see from the responses (after ensuring we had enough replies for an acceptable confidence level/ statistical significance…), it was pretty much unanimous – pricing was the issue!
It would have been extremely hard to conclude that with just the quantitative data. We may have stumbled upon this after some A/B testing, however, after marrying the qualitative, it pointed us towards a pretty reliable direction in a quick and efficient manner. Even if we wanted to verify using A/B testing, we now have an idea of where to start. They lowered the pricing and registrations started rolling in!
So when you’re having dinner by candlelight tonight, gazing into your lover’s eyes… slowly move in close, and whisper ever so gently… “My quantitative is incomplete without your qualitative…” <3
When Google Analytics makes changes to their platform, it’s usually a good thing, and helps users navigate through the dashboard easier, offers a more powerful data experience and overall is seen as beneficial to the user.
Google Analytics did just that this time around, as they rolled out some improved features earlier this month. We at E-Nor dove right into the changes and have been loving them.
The first change you notice when logged into Google Analytics is there are less tabs at the top of the screen. Just the simple Reporting, Customization, Admin and Help tabs are now available here. This area had been freshened up a bit, and some of the reports here have been moved to the left sidebar. We’ve noticed this definitely makes more sense, and offers up a more unified placement for the tools. Plus, the top orange navigation bar floats as the user scrolls down the page. Pretty cool!
Create Your Own Custom Dashboard
We all have our own preferences, right? So too is the case when working with Google Analytics, and the gurus at Google have figured that out! The platform now offers new enhanced personalized dashboards. Users can choose from a variety of layouts, giving them a handful of options available. This feature expands the way the dashboard is laid out, and how users can see their data.
1. Advanced Segments
Anyone who works regularly in Google Analytic is aware of the Advanced Segments feature. Well, thanks to this most recent round of changes, this tool has been added to the dashboard. (Yay!) Users can find this button in the upper left section of the dashboard near the Audience Overview header.
2. New Widgets
The Geo Maps and Bars widgets are both new additions to the Google Analytics dashboards. You will also find the Geo Maps have been added for custom reports. These are available on the improved dashboard, and provide yet another set of data gathering tools that help users. The Geo Maps widget allows users to color code data by country, state and so on. The Bars widget give more advanced graphic data abilities.
Examples of Widgets
So if you haven’t already noticed the improvements to the Google Analytics interface, now is a good time to jump in and check them out.
We at E-Nor have found these new and/or revised features and tools highly beneficial, and love the fact that they allow us to continue to provide top-notch analytic services to our clients. We hope you enjoy the changes too!
There are so many screens, features, tools, filters, searches, etc. For the heavy data cruncher, it’s always nice to have a set of shortcuts for quick execution of common tasks. In case you couldn’t find it on the Google Analytics Blog, we laid them out for you.
Here are a list of keyboard shortcuts in Google Analytics:
Date Range Shortcuts
Set date range to TODAY
Set date range to YESTERDAY
Set date range to LAST WEEK
Set date range to LAST MONTH
Toggle date comparison mode (to the previous period of whatever you are looking at. Example, if you’re looking at 6 days, this will compare it to the 6 days before it)
Toggle date comparison mode (to the previous year of the period you are looking at)
Open keyboard shortcut help
Search help center
open account panel
shift + a
Go to account list
shift + d
Go to the default dashboard of the current profile
Some of them not working? If you’re a genius like me, it’s probably because you pressed one of the keys on accident and it took you to the search box. Make sure you are out of the search box when you try these.
Do You See What I See?
As an analytics consultant, it’s important – strike that – mission critical, to make sure you understand how your customer uses data. Sometimes we have a tendency to assume our clients look at and interpret data the same way we do. Nothing could be further from the truth. All customers look at data differently, and uncover insights that we might consider surprising or unexpected. We owe it to our customers to ask the right questions and best understand not only what’s being measured and reported on, but how that data is being consumed and interpreted.
reCAPTCHA = evil
A recent experience highlighted the importance of understanding how a customer looks at data.
I received a request from a customer asking me to QA some Google Analytics tracking code that had been deployed to a page. In order to test this scenario, I needed to go to the page, fill out a form and submit. If all went well in my testing, I would see a hit sent to Google Analytics via my HTTP header monitoring tool.
Seems straightforward and easy enough right? Sure… except that I wasn’t able to submit the form. Huh??? Yeah you read that right – I wasn’t able to complete and submit the form. Not once, not twice, but 30 times I tried and failed! What in the darkest depths of Middle Earth (Yes, I loved The Hobbit, and watched it recently, as you might guess), would cause this issue, you ask? The culprit was that most dreaded of all online phenomena – the reCAPTCHA!
First, let me state my complete and utter disdain for reCAPTCHA. I hate it, and consider it amongst the darkest of evils on this planet. Why do I hate reCAPTCHA so much? That’s easy I certainly understand the benefits gained by reCAPTCHA. It blocks spam form submissions and only allows legitimate human submissions. Yadda yadda yadda I’ve heard it all. Still hate it! Some of those alphanumeric combinations are so out of this world that no human could ever read them.
reCAPTCHA is a marketer’s worst nightmare. Web forms are sort of like Cookie Monster “More cookies…more cookies!”. All they want is for users to fill them out. Cookie Monster is cute, and simply wants more cookies. Sure, he leaves a mess of crumbs behind, but I’ve never heard him complain about the quality of the cookies he consumes.
reCAPTCHA takes an innocent, unassuming, cute and cuddly puppet like Cookie Monster and turns him into…the Soup Nazi! For those of you who didn’t catch the reference, or were deprived of the wonders of Seinfeld, check out this link to learn more. The Soup Nazi makes you stand in line (quietly), and only gives you a very measured amount of soup. Don’t you dare look at him the wrong way, or “No Soup for you” will echo in your ear drums. The mere presence of the Soup Nazi strikes fear into the hearts of the most brave of people, and renders them the likeness of jello – jittery and paranoid. reCAPTCHA has the same impact on web forms by making conversion significantly more complicated, and frustrating the user beyond compare.
reCAPTCHA sits at the forefront of the classic battle between IT and Marketing. Both sides have valid arguments as to it’s usefulness, and as much as I dislike it, reCAPTCHA obviously does filter out spam. At what cost though? Are legitimate customers jumping ship out of sheer frustration? Let’s see what the data has to say…
Data’s Turn to Talk
When I was unable to complete the form, I started thinking how this must be affecting other users. Surely, this must be giving other users the same headache it gave me. Luckily we were tracking all sorts of details about the form, so the answer was in the data. Time to dive in
In this type of analysis, it would be good to look at:
Percentage of users running into reCAPTCHA errors
How many users are receiving multiple reCAPTCHA errors?
Here’s what I found: (all data referenced below is for a period of one month)
# of times the form was viewed:
# of times the form was submitted:
Unique # of times a recaptcha error occurred:
# of times multiple recaptcha’s were encountered:
What stands out from looking at the data above? 1. There were more reCAPTCHA errors (270,969) than submissions (241,803)!!! That means the problem is very widespread, and means that a majority of people are running into this problem. In fact many are hitting multiple reCAPTCHA errors. 2. 76% of form submissions resulted in reCAPTCHA errors!!! This was nothing short of shocking for me. 76%??? That’s insanely high! I happily wrote an email to my customer, full of excitement at what I’d found. I received a prompt reply thanking me for the analysis. The marketing team was also flabbergasted by these results and understandably wanted the reCAPTCHA removed.
Wait…A Different Perspective?
A few days later I received a call from an IT manager in the same organization. First he asked to verify how I had come up with this data. Upon confirming its validity, he also thanked me and said this data was immensely useful for them. He then went on to point out that their reCAPTCHA error rates are inline with industry standards and he wanted to make this a monthly report that he could trend with the intent of making sure the rate isn’t going down. Not going down??? What? You see he was in IT, and his focus was to make sure the reCAPTCHA was doing what it’s designed to do. The harder it is to read those ridiculous patterns, the more effectively it must be working. I thought they would be motivated to remove the reCAPTCHA, and instead they want to make it harder!!!
Always understand how your client will use data. It’s of critical importance and helps you provide meaningful insight, and ensure that data is actually being used to impact the business.
Thoughts, comments? I’d love to hear your perspective!
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.
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.
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.
What Kinds of Questions Can You Answer With Google BigQuery?
Let’s say you are the marketing manager of a large B2C business that is driving traffic to your site through many channels including paid search, affiliates, email, and some offline campaigns as well. You really want to tie the campaign cost data all the way to your qualified leads, opportunities, and sales data, which typically resides in a CRM system, such as SalesForce, SugarCRM, or the like.
But why stop there? You also want to throw in your web analytics data and get engagement metrics in the mix. Last but not least, you’ve done your homework and implemented an integration strategy to tie all this data together (e.g. using a primary key).
You want to produce a very actionable report that shows:
Campaign cost data
Web analytics data
A super actionable metric cost per qualified lead, broken down by campaign!
In the above report, the cost per qualified lead for the Software Demo campaign in Google Adwords was just a little bit over $30, and you can start trending and optimizing accordingly. Run experiments and gather user feedback to bring that cost down!
Other Useful Features
Metrics like cost per qualified lead can be recalculated with amazing speed as often as the data is refreshed.
Results like this can be saved as a table, allowing you to build up layers of useful reports and then combine them to build even more useful reports.
Reports can be downloaded in CSV format for integration with Excel, PowerPoint, or whatever presentation and integration tools your business might use.
Unsampled reports from large data sets in Google Analytics Premium are the perfect kind of data set to upload to Google BigQuery.
Technical Details about Google BigQuery
Google BigQuery is a tool which allows businesses to gain insights from large data sets without any initial hardware purchases or software investments.
The BigQuery service is an online analytical processing (OLAP) system designed for terabyte-scale datasets.
The service supports SQL-like queries against those massive datasets.
BigQuery is surprisingly developer friendly, as it supports the straight forward REST (REpresentational State Transfer) Web service for pushing data to Google’s cloud and then querying it.