Google Analytics Parameter Reference Guide

With the official Google Analytics parameter reference being not the easiest to peruse, I’ve always wondered how many Google Analytics query string parameters there really were. While auditing, there is a small set of parameters that we refer to on a regular basis.  You know the tid (Tracking ID), t (hit), and what not. When we created Amplytics, we eventually did a full review of all the parameters Google Analytics uses.  And we were shocked – 509 parameters after including the Enhanced E-Commerce ones!

Of those 509, 400 are custom dimension and custom metric parameters.  But still, we’re talking about 109 parameters to wade through if you’re going to do an audit.

Because Amplytics needs a full list of Universal Analytics query string parameters, we started looking at these with a magnifying glass, and we realized that there are about 50 parameters that we really use and truly care about as web analysts. 

So we decided to make it a little easier for our web analyst community to wade through by re-organizing and publishing the list of most important Google Analytics parameters. 

We hope you find this list as easy and handy to use as we do.

Publish the list of parameters from the Universal tab of the Google reference file found on subversion: reference docs\Google Analytics

The list should be published in two parts. The first part will be the ‘primary’ set. The list needs to be readable by search engines, so it cannot be an image.  I couldn’t figure out how to create a table using themify – hopefully you know.


How to Justify Investing in Web Analytics For Business

Here is the fact – web analytics is a cost center. Just like finance, HR, and many other support functions, the web analytics team provides a service to its internal customers. No matter how you spin it, it is very hard to effectively tie revenue growth or business goal accomplishment to web analytics. Sure, you can weasel your way into getting a web analyst head approved for a major project. And when there are cost cuts, that’s the first thing you lose. So how does one justify an ongoing commitment to web analytics?

There is a need for culture change. But that change doesn’t come easily unless you are the CEO, CMO, or COO of the company. You will need to prove the value of web analytics in little steps. To be clear, I am not talking about getting web analytics implemented at your company. To get ideas on how to justify that, here is a good resource. What I am talking about is taking the basic installation of web analytics with perhaps a couple of web analysts and moving it to a game-changing, always in-demand team that actually makes a visible contribution to the company’s bottom line.

How Web Analytics Helps
Let’s recap at a high level what web analytics can do for your internal customers. Web analytics can tell them what happened, when it happened, and even how it happened. What it cannot do is tell them why it happened. Instead of why, we can come up with fairly accurate suppositions about the root cause for a certain change in metrics. After that, to validate the assumptions, there needs to be an investigation outside of analytics or an A/B test across these theories.

Using Web Analytics at a Small vs a Large Business
There are several things in common in using web analytics across different types of businesses. For instance, the tags have to be on the pages no matter what, someone has to run/maintain reports or do ad hoc analysis, and someone has to make sure the tags didn’t magically fall off the web pages.

However, there are a few things distinctly different between small and large businesses when it comes to web analytics. For instance, people at small businesses are typically not dedicated to web analytics. It is one of their half dozen other responsibilities. So if you are in this situation, you need to focus on automation as much as possible. I cover this point in the productivity improvements section below.

The challenge for analysts at a larger organization is not about using analytics but about using analytics effectively. As many analysts as there are at a large organization, ask yourself whether they are doing true analytical work or pulling reporting and emailing them all day long. Because teams typically grow organically and not in a highly organized and thoughtful manner, inevitably communication silos are formed and the web analytics practice as a whole becomes grossly inefficient and ineffective. More on the optimal structure of analytics teams in another post.

So as you read through the ways to improve the state of analytics at your company, carefully consider the approach that best suits your situation.

The Soft Sell
Before pitching any of the ideas below, prep your audience by making (entirely true) statements of the following nature:

  1. “If we can’t measure it, let’s not build it.”
  2. “Our understanding of our business will never increase if we continue to work at this level.”
  3. “We are still struggling with the basics of web analytics whereas other companies our size are now investing in xyz.”

These are just examples, of course. The point is that you want to do a soft pitch before you bring out your big ideas. Get the decision makers interested in what you are saying. Remember, at the end of the day, you are a marketer. You are selling something, and you need to make sure your message is strong and well received. So wait for the right opportunity and make statements that will get people to invariably ask you the question “So what do you think we should do?” Bingo. This is what have been waiting for. Now walk them through a few ideas on how to improve as described below.

Why Invest in Web Analytics
So why indeed should one invest in analytics? Simply put, because without such an investment, a business cannot leverage analytics to its true potential. There is a growth and maturity cycle of analytics in an organization. Without investing in analytics, the analytics team is never able to reach full maturity. The team is stuck doing the same exercise over and over. The days change, the executives change, but the reports essentially say the same things they did before. So what do you do? Here are five reasons to invest in web analytics:

  1. Improve Productivity
  2. Maintain Integrity
  3. Ensure Collaboration
  4. Foster Learning
  5. Continue Regular Upkeep

When talking to decision makers, though, don’t bother talking about the last two. Focus on how your decision maker can benefit from the investment. So pick from the top three – productivity, integrity, or collaboration. Let’s dive into each one to understand exactly what I mean.

Improve Productivity
Analytics can be a funny business. Everybody wants reports delivered to them all day, every day. But if you tracked how many people actually opened the reports and did anything with the information, you would be appalled. OK, so it isn’t possible to wean people off reports or supplement their satisfaction of having access to one. It is, however, possible to reduce the churn and time spent creating these reports by automating them. You will be surprised at how even the most complex reports can be automated to save an analyst’s time. We have seen analysts spend a full four months of entire year doing nothing but running reports. So the case for productivity can be very easy – take the number of hours spent each week creating reports. Multiply by the average hourly rate of an employee. Multiply by 52 and you have yearly savings. Multiply by the total number of analysts wasting their time doing the same thing across the company and pretty soon you could have a robust case for saving the company hundreds of thousands of dollars!

Savings = [Weekly hours spent creating manual reports x hourly rate of employees x 52 x number of analysts]

The case doesn’t stop there. With the cost savings, you also have freed up additional analyst bandwidth. So now you can put that to use in analyzing real problems for your sponsors’ teams. What a win-win!

Here’s another productivity play – I believe in the concept of a hub and spoke team. I can’t imagine any other structure working well for analytics. That is a bigger discussion for another post, but you can sow the seeds now. Every business team owner wants his/her own dedicated analysts. Sometimes it is a power trip and other times they truly believe they can run their team effectively if they had dedicated resources. Play this to your advantage – educate and offload all the mundane reporting tasks to your sponsors’ teams. So you can focus on the harder, more satisfying, thought provoking, data analysis tasks! So the pitch here is to offer to train other analysts to improve productivity – your productivity.

Maintain Integrity
For a website where changes are made frequently, gaps in data or concerns about the validity of data are a way of life. A typical web analytics administrator spends up to 20 hours a month just investigating and fixing issues caused by changes to the site that were not communicated or tested thoroughly. The problem is multiplied many fold if the website uses a ‘rolling release’ approach where changes are pushed out whenever there are enough of them ready. Talk about an analyst’s worst nightmare! There are several levels of solutions to address this problem.

First is the process solution where an administrator could be part of a release cycle process, signing off on releases where the analytics changes have been considered. Then is the tool solution where a second tool could be used to validate the data integrity of the first tool. And finally, there’s the regular automated audit solution where audits can be run on the live site every day and alerts can be sent out when breaks or changes are detected.

All this requires money, which, of course, requires justification. So the formula to justify investing in an analytics integrity check at your company is similar as before. Estimate the hours an analyst could save by not having to deep dive after every release or change. Add it to the number of hours spent fixing and annotating reports. And then the hours saved because of having to triple check everything when integrity checks are not in place. The hour count doesn’t stop there! Include the time a developer then spends on fixing the issue, a QA spends testing the fix, and a release manager spends managing the change! Multiply it by the number of releases in a year and you will soon have a strong case for developing a robust integrity check program for web analytics.

Savings = [hours spent deep diving after every release/change + number of hours spent fixing reports + hours spent triple checking numbers constantly + hours spent by developers to fix issues + hours taken by QA to validate + release management hours] x [number of releases/change each year]

Ensure Collaboration
Large organizations often experience productivity losses because of lack of communication. Either that or they are notoriously inefficient because of bottlenecks caused by highly sought after resources. In other words, projects slow down because the right resource isn’t available or projects are implemented incorrectly because the correct resources were not consulted in the development phase.

So if you are going to pitch this one, then you should use the “if we can’t measure it, let’s not build it” statement in a conversation with the decision maker. See how this all fits in?

To ensure collaboration, you need to be appropriately staffed. So make a list of recent projects that did not have the right analytics in place. Of course, choose projects that the sponsor is close to. Then paint a picture of how with additional dedicated resources you will be able to better service the organization’s needs.

The bottom line is this – to leverage web analytics to its maximum potential, an organization needs to make a commitment to nurturing and growing web analytics. Anything less results in overworked, inefficient, mediocre analysts working with poorly implemented web analytics software.

Analytics How-Tos

How to Setup Google Analytics On Your Free/Non-Paid Websites

Are you ready to take Google Analytics for a test drive? Whether you were recently inspired from our latest article to start a blog about (web) analytics or you already have a blog, it’s time to sit in the driver’s seat and take Google Analytics for a spin.

Before we go into detail about how to set up Google Analytics for each of the most popular free web hosted sites, make sure you have your Google Analytics driver’s license first. Without one, none of the following steps will work for you.

Now that you’re ready, let’s drive through various Google Analytics’ setups.
Uh, oh! WordPress.COM is a dead-end for Google Analytics. Unfortunately, for those of you who have the free version of WordPress (.com instead of .org), Google Analytics is not yet available to you. Either you’ll have to stick with WordPress’s own statistics or use another tool/plugin for your website.
Depending on what theme you have on Tumblr, you’ll either have a smooth ride setting up Google Analytics or a slightly bumpy one. Tumblr has various themes on its site, so if you’re lucky enough to have the default theme or some of its select themes, all you have to do to set up Google Analytics is

1. Go to your dashboard on Tumblr
2. Click “Customize” on the far right column of information
3. Scroll all the way down on the left column
4. Under “Theme Options,” you’ll see “Google Analytics ID”
5. Enter your Google Analytics ID, save your changes, and you’re done

If you find that the previous five steps don’t work or aren’t available to you, check out Tumblr’s awesome guide on how to set up Google Analytics.
Aw, man. Another road block for free users. Wix, just like WordPress, does not allow a Google Analytics integration to its free sites. Free Wix users will have to upgrade their website to premium services, choose another platform to play with web analytics, or use other apps for website statistics (such as Addfreestats).
Much like Tumblr, Blogger has any extremely easy way to add tracking to your blog. All you have to do is

  1. Go into your blog’s “Settings”
  2. Scroll all the way to the bottom of the left navigation and click “Other”
  3. Scroll all the way down the page until you see Google Analytics
  4. Copy your Google Analytics Tracking ID into the box that says “Analytics Web Property ID”

Thankfully for Blogger, those four steps are a fool-proof method. No worrying about which theme you have or anything else. Straight forward and to the point.
Once you’re logged into and have your Google Analytics ID copied, there are three simple steps to connecting to Google Analytics:

  1. Click the “Edit Site” icon (pencil) on your site’s dashboard
  2. Under “Details” (It defaults you to this section), scroll all the way down until you see “Google Analytics Code”
  3. Paste your Google Analytics ID in the box, and click “Update Site Details” at the bottom of the page

Setting up Google Analytics on these sites is starting to sound like a song on repeat.
Much like the last couple of sites, has a straightforward and easy Google Analytics integration.

  1. Go into your dashboard
  2. Click “Blog Stats” under the “Tools” section in the left nav
  3. Choose “Enabled” for Google Analytics Service
  4. Press “Authenticate Account”
  5. Paste your Google Analytics tracking ID into the “Tracking Code” box
  6. Save your changes

Simple, right?
HubPages might have a drawn out process of setting up your Google Analytics account with your site, but let’s break it down and make it easy for you:

  1. Go to “My Account” under your name on the top nav
  2. Click “Earnings” at the top of the page (It’s right next to the default “Hubs”)
  3. Scroll down to “Reporting Settings”
  4. Click “Get started” for the Google Analytics program
  5. Check “Yes” for the question “Do you have a Google Analytics account?”
  6. Paste your Google Analytics tracking ID into the box and save

In case you skipped the step about setting up a Google Analytics account, HubPages, as nice as they are, give you links to tutorials on how to set up a Google Analytics Account and find the information you need to copy over to your HubPages account.
OK, now it’s time to change gears with Weebly for Google Analytics setup. While the free site will allow you use Google Analytics, it’s not as easy as copy-pasting your tracking ID into a box. So let’s go over in detail what you’ll need to do for Weebly:

  1. Login to your Google Analytics account and click “Admin” at the top of the page
  2. Make sure you see the account name and property associated with your website; otherwise, change both sections to reflect the appropriate website
  3. Click “Tracking Info” under Property (the second column)
  4. Select “Tracking Code”
  5. Copy paste all that’s in the box under “This is your tracking code. Copy and paste it into the code of every page you want to track.”
  6. Go to your Weebly Settings
  7. Click “SEO” on the left navigation
  8. Paste your tracking code into the box that says “Header Code”
  9. Save and publish your site

Now, that wasn’t too bad, was it?
While you won’t be able to have too many pages on your free Webs account, if you do choose to have Google Analytics tracking on this site, it’s almost the exact same steps as Weebly.

  1. Do steps 1-5 from
  2. Click “Dashboard” on the top nav
  3. Click “Settings” on the left nav
  4. Scroll all the way to the bottom of the page and paste your tracking code into the box that says “Google Analytics (optional)”
  5. Save your settings

So Webs’ Google Analytics setup IS like Weebly.
Last but not least, Medium. One of the newer and really popular blog sites out there, Medium surprisingly won’t let you connect Google Analytics to your account. I guess being connected to social accounts is enough to let you know what’s up with your content. We’ll get more on if focusing solely on social analytics and forgoing Google Analytics is what you should really be doing. But more on that in a later post.

To recap, most of the free blog sites fall into one of three categories: no access to Google Analytics (unless there’s a premium version of the site), simple access to Google Analytics by copy/pasting a Google Analytics ID, or copy/pasting tracking code into the header of your free blog site.

I hope this post helped those of you out there who wanted Google Analytics connected to your free hosted website. If I didn’t include a website on here that you’d like to know more about (for Google Analytics or other web analytics plugins/connections), let me know in the comments or on Twitter @Amplytics.

Are you ready to take Google Analytics for a drive? Thankfully for all of you who were inspired from

Google Analytics is free and so very well could b

Get ready to start your web analytics engine. Let the race to analyzing your website begin in 3…2…1. Wait.

Whether you were inspired to create your own blog for web analytics or you wanted to start analyzing web analytics for your free website, you’ve come to the right place. So let’s learn how to set up Google Analytics on the following free platforms for websites and blogs.
Unfortunately, for those of you who have the free version of WordPress (.com instead of .org), Google Analytics is not yet available to you. Either you’ll have to stick with WordPress’s own statistics or use another tool for your free website.
Depending on what theme you have, you’ll either have one of the easiest times connecting Google Analytics or a not-so-easy time. Given that you have the default or one of the many Tumblr themes that allows you to paste your Google Analytics ID directly into does allow free users to add Google Analytics tracking to their website through a javascript sandbox. At the time of posting this article, proved to be buggy and functionality limited. If this site is accessible in the next couple of weeks, this section will be updated.

Business of Web Analytics

What is Web Analytics?

Whether you’re new to web analytics or need a refresher course, let’s get back to basics. As more and more web analytics tools are being creating and more and more updates are being made to the popular solutions like Google Analytics, it’s always a good idea to have your web analytics basics cheat sheet up to date. So if you’re ready to learn what web analytics is or want to see if you’ve missed a few important updates recently, keep reading!

What Is Web Analytics?
Web analytics is simply put as the measurement and analysis of a website based on users’ and visitors’ actions or behaviors. The analysis of a site usually is for a business to see what’s working on its site, where the site could be optimized, and how other departments’ campaigns, advertisements, content, predictions, etc. performed.

Who Uses Web Analytics?
Depending on how large or small your company is, it could be as little as one or two people who use web analytics. Larger companies, on the other hand, can have hundreds of web analytics users. Ideally, all of a company’s departments should work together with a web analytics team (or if there isn’t a web analytics team, this might convince you to have one) to use web analytics to understand the company’s position overall online as well as its independent sectors (sales, marketing, content, risk management, IT, inventory, etc.).

Where Is Web Analytics Used?
Web analytics is used on websites and can be viewed on dashboards such as Google Analytics, given that there is tracking code implemented in the site’s HTML.

So where is web analytics actually used in a business? Let’s break it down by department! Depending on the size of your company, not all of these departments may exist (or they might overlap).

  • Sales  While sales teams will love to see how many products are being sold, they won’t get any additional sales until they know where the sales came from and what was driving the sale. So knowing which pages and what traffic drives leads is key to the sales team knowing which products and pages are working. And web analytics can help the sales team know where they are doing well and where they are lacking. Implementing custom tracking code using analytics tags will allow sales teams a better indication of their performance.
  • Marketing – Like sales, the marketing team wants to see the campaign and advertisement results. Basically, the marketing team wants to know how their efforts to market and advertise to current and potential customers performed. But in addition to that, the marketing team needs to know how to optimize and refine their efforts. Knowing about channel performance inside out is one of the biggest reasons a marketing team uses web analytics (and for this reason, marketing teams could be the largest user of web analytics at any particular company). The numbers from web analytics will tell you what happened in that specific time frame and will help paint part of the picture as to why the campaigns and advertisements performed the way they did.
  • Content/Content Marketing – Using web analytics to measure content and content marketing efforts is a little tricky if you don’t know which metrics and KPIs to focus on. Yes, page views and number of visitors are nice to know, but content is a lot more complex than the generic information Google Analytics and other web analytics tools spit out at you without any additional data refining. Since content marketing web analytics is not basic web analytics, we’ll go into more detail on this topic in a later post.
  • Risk Management – Looking at past trends helps to predict the future. Your risk management team is performing analysis on what is most likely going to happen in the upcoming quarter or year. Members in these teams will look at past data from a web analytics solution to know what performed well, what didn’t perform well, what errors were made (in any department), what trends are there, what problems kept occurring when any scheduled changes to the business will be, and so on. Again, web analytics (and big data) for risk management is a complex subject, so we’ll go into more detail in a later post.
  • Information Technology – Like the Risk Management team, the IT team would also use past web analytics data for forecasting things such as page speed or site speed.
  • Business Analysts – Web analytics will most likely be what many business analysts see every day (compared to the other departments’ occasional web analytics usage). However, only about less than a quarter of a business analyst team’s time will be analyzing the web analytics data; the rest is typically used to create analytics reports. In the upcoming posts, we’ll discuss how business analyst teams can reduce time spent on creating reports (through automation) and more time analyzing which metrics that matter.

When Should Web Analytics be Used?
You can and should start using web analytics now if you haven’t already. Google Analytics is one of the best tools out there to use, and it’s free. All you have to do is log into your Google account associated with your business and start reviewing your data. If you don’t have a log in, check out Google’s Analytics site for more help. Or if you can no longer access your Google Analytics account but you are the owner of it, check out this blog post for help to get back into your Google Analytics account.

Keep in mind that even though Google Analytics and other analytics tools provide you with a plethora of information about your site, you’ll want to customize your data through personalized segments, reports, and eventually custom tags.

Why Should I Use Web Analytics?
No business would randomly market its product to anyone and everyone it finds, so why would you forgo web analytics and miss out on a better optimized site, a better user experience, and more conversions? Web analytics provides you valuable insight about your business and customer behavior that you couldn’t find even if you did a bunch of customer surveys and other non-web methods. Businesses love information about their company and want to know what is working and what is not. Web analytics is one of the easiest ways to know the who, what, when, and where. And web analytics trends can help produce a why and a how. Web analytics provides businesses a fairly complete picture, so why would you opt out and have only a small piece of it?

How Do I Use Web Analytics?
If you’re new to web analytics, first start using one of the free and more widely used tools such as Google Analytics. There are plenty of tutorials on Google Analytics and you can even become Google Analytics certified. Once you have a better understanding of which metrics are actually useful and actionable (more on this in a later post) and know what the metrics and dimensions really mean, then you can dip your toes into paid and lesser known tools such as Adobe Analytics, Piwik, Crazy Egg, Compete, Optimizely, 4Q by iPerceptions, or any of the social media analytics tools. Eventually, you might even want to add a backup analytics tool to Google Analytics.

What Are the Most Common Analytics Terms/Metrics And What Do They Mean?
Before you get started on you web analytics quest, let’s go over some of the most common web analytics terms.

  • Hits – A hit on your site means that some type of file – photo, text, etc. – was downloaded or sent from your site. Google Analytics tracks many types of hits on a webpage, including page tracking, event tracking, ecommerce tracking, and social interaction hits. With web analytics, all of these hits are tracked as long as the correct tags are in place on the website.
  • Pageviews – Each individual page on your website that a user visits will be counted as a pageview. Even if the same user goes back to a previous page that he or she viewed, another pageview will be counted.
  • Visits or sessions – A session is an interaction a user makes with a site, including pageviews (visiting a page), events (clicking a button), ecommerce (purchasing a product), and social (commenting on a blog post) interactions. An individual user can have multiple sessions, depending on how long they’re on a single page (For Google Analytics, there’s a session timeout after 30 minutes of inactivity and at midnight.) and depending on how they are referred to the page or site (For Google Analytics, a timeout will occur if you go to the same site using different referrers, AdWords campaigns, etc.).
  • Unique visitors – A unique visitor is a distinct visitor who comes to a site and visits a page(s). No matter how many pages this visitor goes to and no matter how many different campaigns and referral sources this visitor uses to get to the site, the visitor will still be recognized as the same person for a given period of time (depends on which analytics tool you use).
  • Bounce rate – The bounce rate is the percentage of users who come to your site and then leave after viewing a single page of a website.
  • Referral traffic – Referral traffic is any traffic that is not coming from Google’s search engine, a direct view to a site (see direct traffic), or social traffic (Note: Not all social traffic is recording only in Google Analytics’ social channel. You’ll still see some social traffic from sites such as LinkedIn in your referral traffic list).
  • Direct traffic – Direct traffic generally means traffic from people who typed or copy/pasted one of your URLs directly into their browser and visited the site. Do note that there are some discrepancies in Google Analytics’ direct traffic, specifically its (direct) / (none).
  • Conversion rate – The conversion rate is the number of completed (and successful) transactions, subscriptions, sign ups, or other business goals divided by the number of unique visitors to the site overall.

Hopefully this review of web analytics helped you. If you want to know more about anything web analytics related that we haven’t covered in a blog post, let us know in the comments or on Twitter @Amplytics.

Web Analytics Tools

The Case for Excellent Analytics

Business analysts, I’m sure you’re tired of having to manually go through Google Analytics reports and segments to update your daily/weekly/monthly reports. All that time spent on gathering data leaves little to no time left to actually analyze the data, which is, of course, the key part of your title and role at your company!

You might recall your day yesterday or last week where you spent HOURS piecing together separate Google Analytics reports and dumping them into one (or more) spreadsheets for your boss or clients. You might start asking yourself “Isn’t there something that could do this all for me for free?”

I’ve been there with you. There were days where updating reports took 30 minutes to 3 hours due to how small or extensive the project was. And generating those reports should have taken less than half that time with an automation tool.

So one day our team was fed up with forgoing precious analysis time for mundane data gathering. We wanted the reporting-analysis time management struggle to end, or, well, at least to not be as bad. We researched numerous Google Analytics API tools and plugins to end up on Excellent Analytics. While this Excel plugin has saved us numerous reporting hours, it isn’t a complete replacement for Google Analytics’ website. So let’s go into more detail about why you should look into using Excellent Analytics!

What is Excellent Analytics?
Excellent Analytics originally was created by the founders of Outfox back in 2009 as the first Google Analytics plugin for Excel. While Outfox’s site says the last update to Excellent Analytics was back in 2012, this open source program has been updated and maintained by the public fairly regularly since then. As you can see in the image below, the last update was made around late August 2014.

Excellent Analytics is essentially Google Analytics at your fingertips while navigating in Microsoft Excel. This Excel plugin pulls in most of the dimensions and metrics directly from Google Analytics, but typically the dimensions and metrics are recognized by their older names (such as visits instead of sessions). Despite the name discrepancies in Excellent Analytics, the plugin allows users to log into one Google Analytics account at a time to recreate Google Analytics reports within Excel. Now, the good thing is you don’t have to recreate everything in Excel. With Excellent Analytics, you can pull in your segments from Google Analytics directly (as seen below), but all the dimensions, metrics, filters, sorting methods, dates/time frames, number of results listed, and profiles associated with your login all have to be added manually. It seems like a lot of manual tasks to set up, but think about how much manual work you already have to do for every report you update.

As you’re working your way through Excellent Analytics, you might not see every metric or dimension on the list. Don’t worry. There’s a way to update it in Settings. The key is to have an XML list of updated metrics or dimensions available on your computer or cloud drive. Unfortunately, you can’t just manually match those one or two metrics that are missing with an Excellent Analytics pop-up. You have to append your XML list and apply the new list to Excellent Analytics.

Yes, so far Excellent Analytics seems like a day or two project already, but trust me, it’s worth the initial time investment.

How will Excellent Analytics help cut my data gathering time?
After you’ve recreated as many reports as you can in Excellent Analytics (we’ll get into why you might not be able to recreate all of them later in the post), updating your reports is as simple as adjusting the time frame, the number of results (if it’s past your initial max results), and pressing the “execute” button.

You’ll need to give the program a few seconds depending on how complex the query is, but if you take a few moments to look away from your desk and at your wall clock and then look back at Excellent Analytics, you can see your completed update.

So now you might be thinking, “Great. I have all the data in one tab in Excel, but I still have to copy everything and paste it into a new tab to sort and add functions and pivots and so on!” Actually, no. The great thing about Excellent Analytics is that it doesn’t take up the entire tab. You can add additional columns with functions pointing to the data from Excellent Analytics. You might not want it to be for only one cell, as the data is reorganized every time you update your query.

But overall, the plugin saves you time from recreating the report on Google Analytics’ website, adjusting the date/accuracy/segments, downloading the report, and copying the new data into various tabs of a working spreadsheet. Just those little tasks alone save minutes if not hours of your time. All you need for Excellent Analytics is an internet connection, Microsoft Excel, and your Google Analytics credentials to do all the information gathering without opening your internet browser.

What are the downfalls of Excellent Analytics?
Excellent Analytics is a tool that seems like a Google Analytics replacement, but it is sadly not. If it were, then Google would have created an offline tool itself for you to use (and maybe pay for). So why isn’t Excellent Analytics the tool to use for every case? There are some problems with it. I’m not going to go into the details listed on Outfox’s site about potential lack of requirements that aren’t fully tested (Note: I use Windows 8.1 and Excel 2013/Office 360 and haven’t had any compatibility problems…yet). But the two major pitfalls of Excellent Analytics are the lack of multiple segments and date comparison as well as the lack of metrics and dimensions.Lack of multiple segments/date comparison tool
If any of your Google Analytics reports require multiple segments to show the variations of activity on (for example) Twitter, Facebook, and LinkedIn, keep in mind that Excellent Analytics will not allow you to gather all three segments’ data in one go. You will have to create three separate queries in three separate tabs in Excel to see Twitter, Facebook, and LinkedIn data even though all three segments are referencing the same report. The same rule/problem applies to week over week, month over month, year over year reports. You’ll have to create two separate queries based on two separate timelines even though the same report and segment is being referenced.

If your job requires you to report mostly comparisons, Excellent Analytics might save you some time but not as much as you hoped for. You might want to look into NEXT Analytics to take Excellent Analytics a step further to allow you to do the comparisons Excellent Analytics by itself won’t allow you to do.

Lack of Updated Metrics/Dimensions
One of the frustrating things about Excellent Analytics is that some metrics and dimensions don’t exist in Excellent Analytics that are available in Google Analytics. And to top it off, these metrics and dimensions aren’t organized in the same way as the Google Analytics dashboard and aren’t always called by the same name! Not only might it take longer to find that one metric because it’s not in as intuitive as a spot as Google Analytics has categorized it, it might be a different name (visits instead of sessions) or not available in the plugin if you haven’t updated the XML dimensions file (dimensions like full referrer weren’t available when I downloaded it in November 2014).

Final Thoughts
While there are some kinks in Excellent Analytics, it is an amazing tool that can save analysts valuable time. Besides the Excellent Analytics connection to NEXT Analytics, if we come across any additional solutions to Excellent Analytics’ current problems, we’ll update this article or create a new post about an even better analytics tool or plugin.

As always, if you found this article helpful or if you have another analytics solution that is your favorite, let us know in the comment or on Twitter @Amplytics.

Business of Web Analytics

Web Analytics Strategy for Small & Large Businesses

Whether you are a small business with one dedicated “web guy” to do all things related to the website or you are a larger business with a team of dedicated analysts who manage analytics, you need to have a web analytics strategy in place to leverage web analytics effectively.

Here is the fact – web analytics is hard. And it keeps getting harder as more and more devices and web properties (websites, mobile sites, and mobile apps) can send usage data that can be measured. Without a robust plan around how to use analytics, you could run into inefficiency issues, accuracy issues, and the basic inability to make the most of your web analytics data.

Why is Web Analytics Important?

Web analytics can help you understand what is happening on your website. You can use it to understand the behavior of visitors to your site, the actions they take on the site, the source of these visitors, and much, much more.

And then there are other advanced reasons to use analytics – to understand and optimize ecommerce/subscription funnels, to determine ROI of marketing spend, to measure results of A/B tests, and so on.

Regardless of what you use web analytics for, there are a few problems that are commonly seen across companies.

Typical Problems in Using Web Analytics

Here are a few common web analytics related behaviors found across tiny, small, large, and massive organizations:

  1. Web analytics (e.g. Google Analytics) is implemented but not used
  2. Web analytics isn’t really a priority; it is more of an afterthought
  3. Every time a report is sent out, no real action results from it
  4. Web analysts are very busy generating reports and have no time for ad hoc investigative questions
  5. Big budget decisions are often made without web analytics data

If any of these statements ring true for you, please tweet us and read on! 

So what can one do to use web analytics effectively without needing a web analytics degree?  Read on!

The Truth about Web Analytics

There are only three things in web analytics that are important to understand. Once you understand these three things, then using web analytics becomes more of a mechanical exercise. And this applies to businesses of any size. Here are the three simple truths:

  1. Knowing what metrics to measure is half the battle
  2. Understanding the context to apply when measuring is the other half of the battle
  3. Ensuring your web analytics tool is recording correct data accurately is paramount

Figuring out Web Analytics Metrics to Use

Any popular web analytics tool (Google Analytics, Piwik, Adobe Analytics, etc.) comes with a standard set of reports. Once your web analytics tags are implemented, then data starts getting recorded in your tool. At this point, you can open up the interface and pull a bunch of reports to get a read on site performance.

However, you will get the best bang for your buck and time by knowing what metrics to use when measuring performance of your website. However simple your website, you need metrics that make sense for your business and not just the ones that are common across all websites (visitors, sessions, pageviews, etc.).

So let’s walk through a quick guide on how to identify metrics that make sense for your business:

  1. Write down how your business makes money (i.e. services rendered, products sold, subscriptions acquired, etc.)
  2. Write out how your website contributes to that business goal (i.e. it drives phone calls, it captures potential customers’ interest, it processes transactions, it provides information about your business, etc.)
  3. Now write out how a change in the website’s contribution changes how much money your business generates. For example, fewer phone calls means fewer appointments and fewer subscriptions means less ad revenue
  4. Figure out which of these changes drives 80% of the impact to your bottom line. Select at most three such drivers

These will be the three metrics you can use as Key Performance Indicators for tracking the performance of your site. Let’s get just a bit fancier – The three drivers you have identified are probably absolute metrics such as visitors, subscriptions, sales, etc.

The problem with absolute metrics is that they hide the root cause for changes. A good metric is some sort of a comparison to another metric. So it is relative (a ratio or a percentage). Why? Because there is almost no scenario where a single absolute metric tells the entire story. Let’s use the example of a site that generates revenue by selling a widget. For this simple example, a potential KPI could be total revenue. And you could look at total revenue per day. A week over week change in revenue could tell whether the site is selling enough. However, it doesn’t tell you whether the change was because you sold more widgets to the same number of visitors or because more visitors came to your site and you sold fewer widgets per visitor! The point of a good metric is that you need to make it actionable. So instead of looking at just visitors or just revenue, you could create a new metric called Revenue per Visitor and track that over time. Now you are tracking two metrics with a single report! If that metric changes, then you can dig into the report to understand which of the two core components – revenue or visitors changed. Below are two charts that show the contrast between using three metrics to tell a story vs using a single actionable metric. Note that there are other issues with using RPV, but I won’t get into those now.

This is just a simple example of how to build a KPI. The key takeaways here are that a great metric is

  1. Easy – It is simple to communicate and simple to understand
  2. Contextual – It is relative to another metric
  3. Actionable – It can drive further investigation or immediate adjustments

It isn’t easy to create good metrics. So start simple and generate reports using these metrics. Once you see these reports on a daily basis, ask yourself – so what? So what if my report shows an improvement or decline in a metric over time? Why should I care? Once you start poking holes in your own reports and metrics, creating a great metric will be just a matter of time.

How to Apply Context to Web Analytics

A famous web analytics guru once said “all data in aggregate is crap.” Perhaps I am paraphrasing, but he is right. The whole point of web analytics is to understand what is happening on a site and then to find the reason that it is happening. And that is only possible by slicing the data and looking at it from various perspectives. And that is what I mean by “applying context to the data.”

Context needs to be applied to standard web analytics reports as well as to ad hoc reports that are run to investigate website issues. I am going to use the example of a typical web analytics investigation to explain how context is created.

Let’s say you have seen a drop in transactions in the past week. And you’ve seen this to be a 5-7 day trend. It is very tempting for even the most seasoned analysts to just dive into analytics to solve a problem rather than talk through the approach. Instead of opening up the web analytics interface to investigate, the first step actually is to create a plan of how to approach the problem. In this particular example, we would first make a list of possible reasons why transactions dropped. Here are some things we would want to check:

  • Did the conversion rate change (transactions/visitors)?
  • Did site visitor counts drop?
  • Did transaction counts for a type of product change?
  • Was there a change in marketing tactics recently?
  • Was there an update to the site recently?

The purpose of such high level questions is to help understand the areas that we need to dive into. So it is just a start. Let’s say we found that there was an update to the site. Note that in the real world, it is highly improbable that only one change happened. The issue is typically a result of half a dozen things happening at the same time. But for the sake of simplicity, let’s say the only change was a site update. Then the next step will be to check whether the update to the site caused an unexpected problem. Here are five things you would want to look into:

  1. Is this a real problem? In other words, has the presence of a problem been validated by any other means such as an automated analytics audit or a backup web analytics solution?
  2. Does the problem show across all platforms (i.e. mobile devices, desktop, various popular browsers, etc.)?
  3. Is it a sitewide technical problem such as slower than normal response times?
  4. Is there a particular page in the eCommerce funnel that is now showing higher exit rates?
  5. Is the problem limited to a particular channel (e.g. Paid Search)?

Once such questions have been formulated, then and only then should you even bother to open up a web analytics interface for investigation. Using such context, reports can now be run to see if the data trends can reveal the root cause of the problem.

As I said before, creating context is not just for investigating problems. This similar exercise needs to be executed even when putting together the daily dashboard or set of reports that you will use to track the performance of your website.

Ensuring Your Web Analytics Tool is Accurate

Ensuring that the web analytics tool continues to report reliable information is probably the most overlooked area in web analytics. Accuracy of data should be a fundamental concern for every analyst. Yet so many CMOs, CEOs, and other executives ask every day “How confident are you about this data?” Or worse, they are the ones that point out obvious problems with the data and then they lose complete confidence in the data, the analyst, and the analytics tool!

So let’s step back and ask ourselves “How do we ensure the data in the web analytics tool is accurate?” To be clear – the issue is not with the tool itself! The root causes are bad data was fed into the tool, the tool was improperly configured, or bad code broke analytics tags on the site.

So there are three approaches to addressing this problem:

  1. Pre-Implementation Processes – Implement a dozen processes on the front side to ensure incorrect data is not fed into the tool, to ensure changes to web analytics configurations are done correctly, etc. The problem with this approach is that there are a dozen points of failure that need to be monitored and covered, so this should be used in conjunction with the other two options.
  2. A backup analytics tool – A second analytics tool could be used to compare and confirm that the data being reported by the primary tool is directionally right. Here is a great write up about why a backup analytics tool makes sense. While this is a good solution, it still falls short in the scenario where a technical team could make identical incorrect changes in both tools. So now you end up with two tools that don’t have the correct data!
  3. Post-Implementation Checks – A third approach that addresses shortcomings of the first two is to monitor changes made to the configuration and to web analytics tags by running automated audits every day and reviewing the changes. The problem is that no tool on the market does a good job of monitoring such changes in a cost-effective manner. We hope to change that. If you are interested in hearing more, please sign up to get notified when Amplytics launches.

Web Analytics as a Whole

Web Analytics is an essential component of any organization’s tool chest today. It is also one of the harder components to use effectively. In a nutshell, we recommend measuring what can be actioned, keeping the measurements contextual, and maintaining data accuracy. We hope that our review of web analytics strategies has provided you with enough information on how to best leverage a web analytics tool to benefit your organization. We would love to hear from you! Please drop us a line at

Analytics How-Tos

How to Learn How to Do Web Analytics

So you want to do web analytics. It sounds like the name of a game show or competition, and maybe the process to learn web analytics is like one. But no matter if you’re in school wanting to know what web analytics really is about or at a job wanting to know more about how to progress in web analytics, you’ve got to start somewhere. So let’s get to it!

Why Learn Web Analytics?
Why SHOULD you learn web analytics? Usually, you’re wanting to learn web analytics to solve a problem for your website. Instead of searching in the dark about how your website is performing, you’ve been hearing about how this strange concept of “web analytics” will fix it. Well, web analytics won’t automatically fix your problem of decreased visits to your site nor will it fix other site issues. Web analytics will give you data and various metrics to let you know what’s happening on your website. But you have to understand what those numbers mean to form conclusions as to why and how certain things are happening on your website. So taking just numbers to actual analysis is the reason you need to learn web analytics.

Where To Start With Web Analytics?
Most people usually don’t jump right into a project with foreign terminology and expect to learn it instantly (or very fast) and not make mistakes. Not every web analytics tool out there has an abundant amount of information about it, and you might not even know which analytics tool you want to use. So how do you go about learning web analytics if you don’t know where to start?

Get familiar with influencers in the web analytics realm and start reading their stuff! Usually every single one of these influencers have blog posts, videos, infographics, ebooks, and more about the basics of web analytics. Once you get the basics down, everything else they’re talking about will start to make sense.

Get Help From Popular Blogs/Influencers In The Industry
As mentioned before, to start understanding web analytics, you need to study the work of the people who know it best. Begin reviewing the following influencers’ work (and follow them on Twitter for even more web analytics goodies):

  • Avinash Kaushik – Avinash is the go to person about web analytics and digital marketing. He’s been writing about web analytics since 2006 on his blog and has published some best-selling web analytics books in the past nine years. No matter which post you read of his, you’ll learn something new each time.
  • Marshall Sponder – This web analytics and SEO/SEM expert writes a plethora of information on both web and social media analytics. After working with companies like IBM and Monster on their web analytics, you can trust that Marshall knows what he’s talking about.
  • Web Analytics Land – While this blog has been inactive since March 2013, it is one of the rare blogs that talks mostly about Adobe Analytics.
  • Usability Tools – Like its name indicates, if you’re itching to know more about analytics tools, check this blog out. It will show you which analytics tools you should look into for both desktop and mobile.
  • Data Set Go – While this blog hasn’t been around for very long, it will help you better understand web analytics and all its parts, especially if you are new to web analytics.
  • Anil Batra – Anil is another Google Analytics guru who provides insight to anything and everything digital marketing, digital analytics, web analytics, and more.
  • Gary Angel – Gary’s on top of new tools and resources for web analytics. What helps him be ahead of the industry is that he is the leader for Semphonic’s web analytics development.
  • Nathan Gilliatt – For all of you executives and business leaders who have no idea the value of web analytics, Nathan will make all of you become web analytics evangelists. Nathan explains all the complicated concepts of web analytics in simple terms, so anyone can learn from his blog posts.
  • Eric T. Peterson – Another go to person for web analytics, Eric has been involved in the analytics field since 1998 and also has a couple of books on web analytics that everyone should check out.

Take Classes and Get Certified
The next step after learning from the top web analytics influencers is to take classes and get certified. Here are some of the best and most detailed lessons and courses you can take to become an advanced analytics expert:

  • Google Analytics – All the lessons and practice tests you need to become certified in Google Analytics (FREE).
  • – Web analytics tutorials, specifically Google Analytics focused ($25-$37.50/month, $250-$375/year, or free with certain .edu email addresses)
  • Market Motive – Learn more than just web analytics. Lessons in web analytics, multi-channel analytics, social media analytics, Google-specific analytics, and more ($299/month with access to every class they offer)
  • The University of British Columbia – Learn everything about digital analytics, including metrics, statistics, measurements, and more ($2500-$2840 for the 5 month course; DAA members receive discounts)
  • Adobe Analytics – Get Adobe Analytics’ certification in 3 days ($3600)
  • Adobe Analytics (Adobe TV) – Get a basic tour of Adobe Analytics from the Adobe/Omniture team (FREE)
  • Coursera – Learn anything and everything to do with web analytics (FREE)

Applying What You’ve Learned From Web Analytics
After spending time every day for a while (however long it takes you to understand and consume this new information), it’s time to put your lessons to work. Apply what you’ve learned from web analytics industry leaders with

  • Your own blog – Set up a Google Analytics account and connect it to your blog. Start playing around with the metrics in Google Analytics and test yourself as you’re going through each of the metrics. What do they mean? Are they actionable metrics? Pretend you’re a business leader, and ask yourself what a company leader wants to see from web analytics. Then start creating reports so that you can write about them on your blog. Besides it giving you practice with web analytics and justifying what the numbers mean, it can give you content to publish on your blog, too.
  • Custom analytics – Take advantage of Google Analytics’ reports, segments, and tags. Create customized versions of each to take your understanding of advanced analytics to a new level.
  • A different web analytics tool – Are you getting a bit comfortable with Google Analytics? Give yourself a challenge with a different analytics tool. Check out Piwik, Open Web Analytics, and CloudFare for some free web analytics tools to get started!
  • Your own web analytics articles – Any the above three exercises will give you inspiration for blog posts, but you’re not limited to just those exercises. Try commenting on recent web analytics news or giving your audience thoughts on what you think of [web analytics’ guru]’s recent blog post.
  • Social media – Social media is one of the biggest ways to get your name out there, connect, and chat with other web analysts. Join Twitter chats and start sending your blog content regularly through social media channels.

Continuing With Web Analytics/The Future of Web Analytics
Web analytics is constantly changing, so there are plenty of things to read and watch out for in the coming years. Talks of more sophisticated web analytics tools and solutions that resolve the context problems with numbers; the disconnect between social, web, and internal information; the overload of big data; and the web analytics communication silos across departments in companies are already being talked about, so get in on that conversation. Let us know about your journey with web analytics in the comments below or on Twitter @Amplytics.

Analytics How-Tos

How to Build a Great Web Analytics Metric

Let me begin by telling you a story about shark attacks. The summer of 2001 was christened the “summer of the shark” by Time Magazine because the focus was on shark attacks that year. This story was then picked up and further propagated by other news channels. Before we knew it, the big story that summer was about an unexpected increase in shark attacks that year.

Before I go on, let me say that I do not intend to minimize the gravity of shark attacks on anyone during that time or any other time. The shark is a top predator of the ocean and behaves as such. The point I want to make is this – the media used a useless metric (number of shark attacks) as a way to drum up interest in a pre-9/11 summer. If they had actually put it in the context of relative measure such as the number of shark attacks in 2000, they might have opted to focus on the lifecycle of the ant instead.

This brings me to the first rule of creating a great metric. That’s what you are here to read, right?

The First Rule

Numbers without context are notoriously poor measures of performance. Performance is best understood in relation to another quantity. So the next time someone throws out a metric “number of blah blah blah,” your first question should be “In relation to what?”

Here’s the first rule about building a great metric – It should be a relative measure.

Let’s work through a few examples:

  1. There were 3,000 visitors to in Jan 2015.
    1. eCommerce firm ACME made $5 million in income in Q4 2014.
    1. AAPL stock was trading at $119 on Feb 5, 2015.

Do you know the action you can take based on these statements? None. Nada. Here’s why – There is no comparison for any of the numbers listed above. Bay Leaf Digital’s visitors were 3,000 in January compared to what? Was that a good thing or a bad thing? Do I need to panic or celebrate??? Apple’s stock was at $119. Was that an increase because of the recent earnings call? Or was that pretty much a flat day compared to the day before? So metrics make sense only in relation to something else.

That relative measure can be the same metric for a different time or a directly related metric for the same time. For example (using Bay Leaf Digital’s 3,000 visitors in Jan 2015):

Root MeasurementInteresting MetricType of Comparison
3,000 visitors in Jan 2015 vs 2,200 visitors in Jan 2014800 more visitors in Jan YOYTime series for the same metric
3,000 visitors of which 2,500 were new visitors5x new visitors compared to returning visitorsRatio comparison
3,000 visitors of which 1,800 saw more than 1 page.60% visitors are engagedPercent based comparison to a directly related metric

The relative measures above provide contexts to these metrics. Now we know the visitor count in January changed for the better. Or that we are getting more new visitors than repeats. Or that there is a small and interesting segment of highly engaged visitors. Now we have something to chew on. In web analytics, relative measures are way more useful than absolute measures.

The Second Rule

Of the above relative measures, the percentage based metric feels the most intuitive because of its use in everyday life. From announcing sales (50% off!) to discussing interest rates (11% APR), the use of a percent based metric is widespread. That brings us to the second rule of a great metric.

The second rule about building a great metric – It should to be simple to understand.

This rule “It should be simple to understand” sounds easy, but there’s a lot more to it than you may imagine. By simple, I mean it should have the following characteristics:

  1. A great metric should be easy to communicate. In a conversation, you should not have to pause to explain what it means. In the example above, if we said “60% of our visitors saw more than 1 page on the site,” people will get it. There is little room for misinterpretation.
  • It should be linear. When a change occurs, the size of the change is directly related to the impact on the business goal. In other words, the metric should not have a complex exponential or a logarithmic relationship to the goal. Below is a simple example of how linear and non-linear metrics behave with respect to a business goal.
  • It should be directionally intuitive. An increase/up is good. A decrease/down is bad. This is how we have grown up to understand life. Keep it that way.

The Third Rule

The third key characteristic of a great metric is that is can be applied in a variety of contexts. These could be time, marketing channels, visitor type, content, etc. So the greater the ability of the metric to be universally applicable, the better the metric is.

We are going to continue using our earlier example of the percent based engagement rate metric to explain further:

Context – TimeInteresting Metric – Engagement RateDerived Metric – Rate of change Context – Visitor TypeInteresting Metric – Engagement RateDerived Metric – % difference
Nov 201457% First Time50% 
Dec 201453%-7% Repeat75%50%
Jan 201460%13%    

In the above two tables, we have applied the context of time and visitor type to the engagement metric. In both cases, the reports make a lot of sense. We see that engagement rate was higher in January compared to December and that repeat visitors have a higher engagement rate compared to first time visitors.

Because we were able to apply these contexts, we have even more information about the factors affecting engagement rate.

So here’s the third rule about building a great metric – It should have universal applicability

A great metric can transcend the silos of teams in an organization. It can be used as a universal language across marketing, operations, design, and many other teams. Think about a designer looking about engagement rate of the home page before and after an upgrade. Now think about the SEO expert analyzing engagement rates of her highest performing pages compared to the engagement rate of her channel as a whole. And across all these teams, the definition of the metric remains unchanged, but yet it is widely accepted and applicable.

The Fourth Rule

The most important characteristic that sets great metrics apart from poor metrics is their actionability. The report you generate should drive action from its recipients. The simple question to ask yourself or the person requesting the report is “What action are you going to take when there is a change in this metric?” If the answer is vague, then you know this is not a great metric. It might not even be a good metric. More on that topic in my article on how to identify poor metrics.

So here’s the fourth rule about building a great metric – It has to be actionable

By actionable, I mean a change in this metric should spur further segmentation and analysis to identify possibly causation. One more thing about being actionable, a great metric is closely tied to an organization’s business goal. So when it is time to action, the actions have a direct impact on the organization’s ability to achieve its business goal.

This fourth rule wraps up the key rules to be used when building great metrics. Remember, all four rules are needed to build a great metric. Satisfy three and you have a good metric. Satisfy four and you’ll get a great metric.

A Last Word

Remember every metric you build will not be a great metric and every report will not be a great report. However, you should have the one report that becomes the reference report across the organization. And that report should have all your great metrics.

To recap, here are the four rules to create a great web analytics metric:

  1. It should be relative
  2. It should be simple
  3. It should be universally applicable
  4. It has to be actionable

What are your thoughts on how to create great web metrics? Let us know in the comments below or on Twitter @Amplytics.


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