“Unique” Visitors 25%-39% inflated

I’ve been working on implementations with a number of clients who have a need for visitor level tracking in Google Analytics so that they can start using GA to measure things like customer loyalty and (try to) calculate Lifetime Customer Value. I understand that there are a number of data models available to approach these sorts of questions, and that either Custom Variables or Events can be used (using either visitor level vars, or session level vars populated from server-side values).

In general, I like pushing the _utma cookie value back into Google Analytics, as it uncovers every single visit in the API. There are lots of benefits to doing this. Justin Cutroni wrote a nice post about merging GA data with a data warehouse. This is just one (powerful) thing that can be done using this method.   What interests me in this post is “how unique are ‘unique’ visitors?” We all know that a unique visitor is nothing more than the value of the _utma cookie’s unique ID. I don’t know about you, but I certainly access websites from multiple browsers from multiple computers. I don’t find it unreasonable for a “person” to indeed have 6 or 7 unique “visitor” values. Start adding in users who clear their cookies and/or Private Browsing and the meaning of “uniques” really begins to degrade.

Luckily, I have access to data where, in addition to capturing _utma values, we’re also capturing obfuscated member ID values. These values are set as a custom variable upon login. This means that a user will maintain their ID whether or not they switch browsers or clear cookies. Here are some of the numbers that I pulled.


The data set that is decently large and no data sampling has been applied to these numbers. At first glance, it appears that the number of unique logins is 61% of the number of GA’s unique visitors. Two things that stuck out at me were the number of visits in a 28 day period by some of the most active users. The top ten most active users average 11.68 visits per day. Also, line 7 had a large ratio of unique visitors to visits. Was this one user who cleared cookies often? Was this shared login information?

In order to make sure that this made sense among users who visited the site less often, I filtered by logins that had less than 3 visits per day, 2 visits per day, and visits once every 2 days. The numbers were pretty consistent for members who came 3 times a day or less, though users who visited once every 2 days or less saw a higher percentage of unique visitors to login IDs.          

Conclusion:
The unique visitor metric was never meant to describe the number of “unique visitors” to the site, where the term ‘unique visitor’ is referring to a person. While web analysts have known this for a while, I find it quite nice to be able to quantify this in number.  Indeed, from the data above, it appears that the number of unique visitors reported is somewhere between 25% – 39% greater than the number of people who visit a site.  

Google Analytics Updates How Visits Are Calculated

In a recent blog post, the Google Analytics team made announced that they are changing the way that visits (sessions) are calculated. Interestingly, they said that, “Based on our research, most users will see less than a 1% change.”  Unfortunately (imho), they didn’t cover their bases with that statement as the comment section of the above cited blog post shows that lots of people are going pretty crazy about these changes.

**Update** On August 16th, the Google Analytics Team announced that there was a bug in the way visits were recorded after they launched the change. Now, people should be going less crazy as numbers are making a bit more sense. Nevertheless….

Bottom line, this is really a significant change and it seems that people aren’t understanding what is going on. The main things that people seem to be complaining about are:

• Increase in visits
• Increase in bounce rate
• Decreased average time on site
• Decreased pages per visit

Surprisingly (maybe), I didn’t see a lot of people complaining about a decrease in conversion rate.  Hmm… In any case, one comment that I saw rise above the negative spew in the GA blog comment section was by Peter at L3 Analytics.  He linked to his blog post which does a nice job discussing some of the implications of the change to the way sessions are calculated. I decided to add to the discussion with this post. Some of what I’ll be saying has already been formulated by Peter. Other things will hopefullly be new , including a number questions I have based on some data in GA that I am still not understanding based upon my current knowledge of the change.

**Side Note** It upsets that people can get so negative in their comments made in forums and blog posts, especially since most of their complaints stem from a lack of understanding. Simple questions in the comment section such as “I don’t understand why 123xyz is happening….” would be nicer to address than “this data is useless, (sarcastic) thanks alot!!”

Understanding the change.

Google Analytics receives hit level data and then calculates all metrics based upon that hit level data. Every time there is a pageview, event, or transaction, a gif request is sent to the Google Analytics servers with information about that hit. Part of that gif request includes session information, and other parts of that gif request include visitor level information. I’m not going to go into the UTM gif requests in depth here, but if you really want to know what is going on check out the RUGA (Really Understanding Google Analytics) series of posts from Cardinal Path.  (Kent – it would be great if you could add inner linking between posts on the blog, it’s a great series).

Here is a graphic I quickly put together.  (As you can tell, I’m not much of a graphic designer).


The idea that this is trying to illustrate is a visit (session) is made up of hits.  A visitor can visit the site multiple times.  When a “visitor” has two or more visits, they change from being counted as a “New Visit” to a “Returning Visitor.”

**Side note:  We use the term “visitor,” but technically this means “__utma Cookie.”  Cookies are browser specific.  So if I, Yehoshua Coren, visit example.com in a 5 minute span from 3 different browsers, GA reports that 3 “unique visitors” came to the site.  Similarly, if 3 different people in my household visit example.com at different times throughout the day, this is 1 “unique visitor.”  Lastly, if I visit a website repeatedly using Private Browsing (Firefox) or Incognito Mode (Chrome), etc, my cookies are cleared on browser close so I’ll be an additional “unique visitor” (with a ‘new visit’) on every subsequent visit.

So how does Google Analytics calculate visitors and visits?

Continue reading “Google Analytics Updates How Visits are Calculated”

Multi-Touch Attribution with Google Analytics

It is well known that Google Analytics relies on last touch campaign attribution.  In short, this means that conversions and transactions are attributed to the most current traffic source of the visit (i.e. the ‘last touch’).  It goes without saying, but you should read  Avinash Kaushik’s Web Analytics 2.0 (attribution models are discussed chapter 12) if you haven’t done so already.  It’s a great intro to the issue at hand.  Additionally, there has been lots written about this already, with a number of different solutions given for how to overcome the limitation in Google Analytics.  After many hours of scouring the web, I must admit that I didn’t find a solution that adequately met my needs for Google Analytics.

To be fair, I just came across http://www.multitouchanalytics.com/ which uses it’s OWN cookie to pass data into GA using event tracking (use a 2nd tracker so you don’t skew your bounce rate).  I’m interested in trying it out.  I especially like how YourAmigo has a number of out of the box reports that automate a number of attribution models.  Very cool.

Also, what we’re about to see here isn’t really an attribution model. The *value* assigned to the first touch, middle touches, and last touch aren’t addressed here. What this post does address is the lack of visitor level data in GA. Using the method below, you’ll be able to IDENTIFY all of the touches that bring visitors back to you site. So while branded keywords may indeed be the best converters on your site, wouldn’t it be nice to know how many of those visitors first visited your site after clicking on a PPC ad?

In any case, here’s how I push VISITOR level data back into GA (which of course can and should then be sliced and diced via advanced segments and custom reports – yummm!!!).

The UTMA Cookie.

Google provides an excellent introduction to the cookies that GA uses here.  If you are new to the cookies, definitely watch the presentation.  Here is a screen cap of the __utma cookie info. ( © 2008 Google)
The UTMA cookie is central to a TON of the reporting in Google Analytics.  Visitor Loyalty, Visitor Recency, Days to Purchases, Visits to purchase, etc etc… all of these metrics rely on this visitor level cookie.  As can be seen in the image above, every visitor who comes to your site is assigned with a random unique ID. Continue reading “Multi-Touch Attribution with Google Analytics.”

I recently discovered a discrepancy between the way that a client’s Google Merchant Center account was performing on Google Product Search and Google Product Ads.

When I first set up Google Product Ads, I had assumed (oops!) that I would see the Adwords campaign in Google Analytics like the rest of my auto-tagged campaigns, complete with click and cost data applied.  I was wrong.  Nothing there.  (For now, cost data for Product Ads is being shown exclusively in Adwords, not in GA).  So I thought I wasn’t getting any clicks on the campaign, as I tend to look at Google Analytics first and only later delve into the Adwords interface for additional data I might need. What I did notice in my GA account was that my Google Merchant Center Feed (which I tagged as Googlebase), took a big hit in traffic recently.

I did my due diligence, and started asking questions in the organization  like, “did anyone update the feed recently?” etc, and almost wrote off the dip in traffic as an algorithm change in Product Search. When I checked my Google Merchant Center backend, I found a big surprise…

My feed was DISAPPROVED, but I was still getting impressions.  Huh?

When looking at my individual product performance, what Google told me was that my feed was good enough for Adwords Product Ads, but NOT good enough to keep sending me free traffic.   While I’m not showing the product names in this next image to honor my client’s privacy, note that each line in this chart represents an individual product.

WOW – a lot of checks for Product Ads (paid), but the free clicks are all offline. I guess that quality of this product feed is good enough to make Google money, but (recently) not quite quality enough to include in their product listings.

I recently had the pleasure of speaking at SphinnCon 2011. I’m sure that anyone there would agree that this year’s conference was really great.  Big thanks to Barry Schwartz for putting it together. Below is a copy of my presentation together with a screencast I put together for anyone who couldn’t make it (or would like to review). Enjoy!

I finally got around to taking the Google Analytics Individual Qualification Exam…

My results are here:

Continue reading “Google Analytics Individual Qualification (GAIQ)”

Segmenting traffic in Google Analytics (or any other web analytics pacakge) is key for any analyst who is looking to get the most out of their data.  I have seen far too many businesses look at their analytics data in the aggregrate, without taking advantage of multiple profiles, advanced segments, or advanced filters.  The “slicing and dicing” of data that can be done in Google Analytics can really provide a tremendous amount of insight into one’s online marketing efforts - be they SEO, PPC, or Social Media.

The following series of brief blog posts highlight some real client cases that provide you some tips about how to segment your traffic in different ways (and “why” you should be doing it).

Segmentation & SEO

I was doing an SEO review for a client recently who was interested in getting the most ‘bang for their buck’ from there SEO efforts.  In particular, they were interested in knowing which terms should they be spending their time (=money) working on internally and outsourcing to SEO firms.

So my question was, “how have your SEO efforts been performing to date?”

Their answer: ”Pretty good.  Traffic seems to be going up.”

Organic Traffic

This was true.  Their traffic was going up.  But which traffic?  Branded searches?  Non-branded searches?  I knew that they were doing some marketing recently and that there has been some good buzz about their brand name.  The quickest way to get a sneak peek at branded and non-branded search terms, is to filter by keyword at the bottom of the page.

Continue reading “The Analytics Slice and Dice | The Importance of Segmentation Part I – SEO”

I recently started working on PPC and Analytics for a new launched website.  In this case, it is an Optometrist in Lincoln, NE.  I’m particularly interested in knowing if anyone has any bounce rate benchmark information for medical practice sites.  Please comment below if yes.  It seems to me that many private practices could greatly benefit from a knowledge Analytics expert.  I’ll keep everyone in the loop as to how this newly launched site is panning out.  I’ll even probably share some groovy charts.

One report that I find extremely useful in the Goals Section is the Conversion Rate Report.  In a moment, I’ll explain how a quick export of data from the Conversion Rate Report can provide valuable insight into one’s abandonment rate.  But first, a quick intro into conversion rates, abandonment rates, and conversion funnels.  In general, I assume that my readership is relatively erudite in Google Analytics.  So if this next paragraph is below your level of expertise, feel free to skip ahead.  That said, I believe it is worthwhile for all us out there who are trying to be successful on the Internet to get back to the basics and remember the fundamentals.

Fundamental #1 –>  It’s all about conversions.

Really.  It is.  Especially when it comes to e-commerce.  There are soooo many people about there who are still interested in how many people come to their site.  But if they aren’t taking the desired action(s) that you would want them to, then most likely you’re wasting time and money.

Take a step back.  Take a deep breath.  And say to yourself, “what do I want people to do when they come to my website.”  The answer(s) should be easy.  Lead generation?  (Filling out a form).  Signing up for an email list?  Online purchase?

Once you define your goal and properly configure it in the settings section, you’re ready for the next step.

But before we get there….

Fundamental #2 –>  Lowering Abandonment Rate is the best way to increase conversions.

Continue reading “Conversion Funnel Analysis – Abandonment Rate by Time of Day”

Proper configuration of Goals in Google Analytics is a critical to the ability to make good business decisions.  Especially for those of you out there spending money on PPC, in order to make the best return on investment possible (and stop wasting your money), properly set up goals are a must.

In my humble opinion, Conversion Rate and Bounce Rate are two of the most important key performance indicators (KPIs).  As you can see below, when I configured Goal Conversions for a client, I was able to make the types of decisions necessary in their PPC account to drive conversions up and bounce rate down.

Continue reading “Google Analytics Goals & Bounce Rate”