Wednesday, February 20, 2013

Unique Visitor Confusion and the Hotel Problem

One of the common stumbling blocks that affects many new comers to Web Analytics is understanding exactly what a Unique Visitor is, and what it is not.

The true definition of a Unique Visitor is any single individual which visits a site during the reporting period.  If a visitor were to return multiple times during the reporting period, they would still register as a single Unique Visitor.  By this definition, Unique Visitors measures unique people.

So what's the problem?
However, the honest truth is that there is not an analytics product on today's market that is yet capable of accurately measuring actual people.  Today's analytics platforms measure the closest simulations of people which we are able to track or extrapolate.  Typical methodologies for getting at this measurement include cookie tracking or panel measurement.  So in reality, for most platforms today, we are actually measuring unique cookies, rather than Unique Visitors.  And yet, most analytics platforms still refer to these unique cookies as Unique Visitors.

Cookies typically overestimate Unique Visitors
So what is the difference and why is it an issue?  Well, while in a perfect world the cookie methodology could actually take us very close to a count of actual people, our world of web tracking is far from perfect.  But it's data, it must have some clean answer/resolution, right?  Wrong.  There are a number of accepted shortcomings of cookie tracking which must be understood in order to make the proper inferences regarding the underlying data.  For example, if I visit your site from a single computer, but in different visits I choose different browsers, then I will count as a different Unique Visitor on your site for each browser I used, because each browser will have a unique cookie associated with it.  Or, if I visit your site from a variety of devices, I again will count as multiple Unique Visitors.  Again, I may use the same computer and the same browser, but I have cleared my cookies during the reporting period on which you are running your report, then, you guessed it...I again am reported as multiple Unique Visitors.

BUUUT...Cookies can underestimate Unique Visitors at the same time...
In fact, the exact opposite can be true as well.  If multiple users hit your site from the same machine and browser (think shared home computers or internet cafes, etc.), then in that case you may be under-counting your Unique Visitors as they are all registering as a single Unique Visitor (remember, just one cookie being registered).  While Unique Visitors tend to be over counted due to the higher frequency of the above mentioned issues, you can see how the skewing can actually go both ways and make it very difficult to resolve the actual Unique Visitor count.

So, can I even trust my Unique Visitor count?
Short answer, yes.  The truth is that the insights that can come from cookie tracking of visitors are still very powerful.  In fact, in some cases understanding the variety of browsers and devices which are interacting with your site can be extremely useful.  Certainly a deeper insight into who is using each browser and device would be insightful as well, but the current cookie tracking methodologies of most platforms lends great information into whos, whats and hows of traffic on your site.  Really, the point here is not that the data is bad...the point is that the name is misleading and, if misunderstood, can lead to unnecessary confusion. 

There are even some analytics providers that are pushing for wider adoption of the Digital Analytics Association and IAB's and ABCe Terminology Guidelines for Counting Audience Size which declares that Unique Visitors should be reserved for Audience/Census measurement (cases where actual people can be counted as uniques) and that Unique Browsers should, in actuality, be the industry preferred term for measuring unique cookies[1]. (Insert shameless internal plug for comScore Digital Analytix here...)

One more thing to consider- The Hotel Problem
There is one additional pitfall that new users tend to experience when it comes to counting "Unique Visitors" on their website.  This is summarized neatly in what is referred to as the Hotel Problem.  Take, for example, a hotel with two rooms.  Let's say we wanted to measure the number of visitors to this hotel over the course of a three day time period using the following[2]:
As you can see, we can calculate our Unique Visitors to this hotel a number of ways.  First, by day.  On day 1 we had 2 unique visitors.  On day 2, 2 unique visitors. On day 3, 2 unique visitors.  So 2 unique visitors per day.  One would think then, that we could sum these to get 6 unique visitors for the 3 day period.  However, some of those visitors weren't unique across the 3 day period. They were, in fact, return visitors.  So simply summing unique visitors from a subsection of the reporting period will clearly not cut it.

Similarly, if we take a count of unique visitors within each room.  Room A had 2 unique visitors. Room B had 2 unique visitors.  Sum them together and you get 4, right?  Well, not necessarily. 

The truth is that in order to count Unique Visitors, we can't subdivide the data in any way within the reporting period without somehow skewing the data.  In this case, we can clearly count that we have a total of 3 Unique Visitors to the hotel during the time period.  We can't sum across days or rooms without losing some of the deduplication which makes the Unique visitor truly unique.  We also can not disassociate the count from the reporting period without possibly reintroducing duplication into the equation[3].

For this very reason, in many platforms if you run a report that splits the Unique Visitors out by any subdivision within the reporting period (say Unique Visitors by day over a week's time), then the total will be removed to avoid the very confusion evidence through the hotel problem.

At the end of the day, Unique Visitors, or better put Unique Browsers are an extremely rudimentary and useful data point, but some care must be taken with new users to ensure that they understand the origin of the data and methodology for its collection to avoid making the beginner assumptions which can often lead to confusion on how to use the data.


Sunday, February 17, 2013

Introspection: A look at web analytics for your corporate Intranet

When we think of web analytics, most of us think of the standard B2C set up and how we can drive more customers to our web site, get more clicks, basically sell more goods. However, some of the most meaningful and complex websites that companies manage are strictly internal.

Corporate intranets are huge. They can be some of the most convoluted monsters on the web. For most major companies they house everything from human resource data, financial data, time card input, expense reports, benefits data, corporate policies, and many other company relevant data.

As a recent employee of one such company that used a corporate intranet to communicate all pressing company data, I was forced to use all the different pieces of this site, unwillingly I might add. This website was a mess. Nothing made sense. We had 3 different Oracle login pages, each one with a different username and password, all of which were on different reset schedules; two different Facebook-esque community pages, neither of which any one used, and multiple Sharepoint sites, to ya know, keep everything in "one place." What a monstrosity. This is was a travesty of unimaginable proportions. There was absolutely no flow, not to mention is was painstakingly slow. How can a company expect its employees to "happily" use something so poorly designed?

The point is, had this been a customer-facing website, or anything that a company expected customers to use, it would have been blown-up, shelved and completely redesigned years ago. More accurately, it probably would never have even gone live.

Why do companies build such poorly designed Intranets and then expect employees to willingly use them?

Our company had a steering committee, and small focus groups that "helped" to create this dastardly eyesore. But no one since had taken any time to do any analysis on whether or not anyone was using it or more importantly how they could make it better.

Using standard web analytic processes companies could make the Intranet into something that employees enjoy using. Here are a few rules that companies should think about.

1. Treat your intranet like the Internet.
If you wouldn't want your customers to use your intranet, then why force your employees to use it?

2. Use the sunk costs in the intranet to help push more adoption for web analytics
Your company probably spent big bucks on designing and implementing their intranet, so use that as leverage to get your company to invest in more web analytics in order to help them make it better.

3. Follow the same processes you use for your commercial web site.
Your employees are your new customers, so figure out you can drive more traffic to that site.

4. Be corporate hero on both sides of the wall
By helping to drive more employees to freely use the Intranet site, you will have helped the substantiate the costs required to make it. Also if you make the Intranet site more user friendly your co-workers will be much happier when they are forced to use it.

5. Lower traffic rates means more opportunity
Your corporate intranet is more than likely not crucial to your business from a revenue standpoint, unlike your commercial one. What better chance to practice your web analysis skills and efforts on something that isn't so mission critical.

You can use the same principles you use everyday with web analysis to help your companies intranet. By doing so, you can really make a big impact by improving your methodologies, proving the worth of analytics, and hopefully getting some "atta-boys" from your fellow co-workers.


Saturday, February 16, 2013

Uploading Costs to Determine ROI

      So what it the point of all of this analytics? Is it to run frequent reports and setup custom dashboards? Is it to print fancy infographics that tell a story without providing any real insight? Analysts need to realize that the reason why companies invest efforts in employing these tools is to simply generate more money. Sounds obvious. Yet many organizations blindly implement web analytics tools without developing strategies and measurements that allow for insight into the effectiveness of marketing campaigns across all ad networks. Without an effective way of measuring the ROI of all web based advertisement campaigns, companies can not gain insight into which techniques are generating the most money. 
     Google Analytics provides plug-ins that provide easy ROI reporting for advertisement campaigns that are generated through Google's online marketing tools. Information about advertisement campaigns implemented in Google Adwords can be linked to a GA account to allow for the baked in reporting tools to be used in Adword ROI analysis. But what about other online advertisement campaigns (Facebook,Bing, partner sites)? If the goal is to utilize analytics to make money, then organizations need to analyze which advertisement campaigns are the most effective across all ad networks .
       GA provides tools for users that allow for non Google ad network cost data to be uploaded from files (excel spreadsheets) into the GA suite. Uploading daily costs data allows for a one stop web analytics experience where all relevant online advertisement costs and statistics are merged together, allowing for reflection on the effectiveness of specific marketing efforts.

Where to Start?

        Before we can start uploading data into GA, we need to make sure our advertisement links are created in Google's URL builder tool. Utilizing the builder allows us to maintain effective management across campaigns, mediums, and sources[1]. Please refer to Bryce Bagley's excellent post excellent post on how to accomplish this.

      Once our URLs have been built, we then need to develop a spreadsheet that contains information that will allow us to track what campaigns we are currently running, what kind of traffic they generated, and how much they cost. Advertisement tools outside of Google’s ad network also can provide these same statistics. Google provides guidelines on what data can be uploaded and it's format.[2]

     Before we can start uploading data into GA, we need to make sure our data is formatted in a specific way that allows for Google's API to read it. Companies should format spreadsheets according to the dimensions and metrics shown in the table above. However, many companies may have spreadsheets that look similar to this one.

      Google’s data upload API is unable to read the file due to the titles in the columns being unrecognizable by the tool. However, it is easy to reformat into a API friendly file.

     You will notice that the spreadsheet contains aspects such as source, medium, and campaign name which refer back to the original URL that was generated for our advertisement link. The data will upload and associate itself to the specific campaign that we created. [3]

How to upload the Data

     Before we can upload the data, we must make sure that our spreadsheet file has been formatted appropriately and saved in the .CVS file format. This format is an option when saving files in excel. Once our files are ready, we can upload our data using Google’s free self service API or an independent GA application provider for a cost.

Google's Self Service API:

This method involves an intermediate knowledge of how to utilize API's and a small bit of scripting logic. Due to this falling outside the realm of this course, I have attached several tutorials on how to set up API access in Google's developer tools and how to enable data uploading in GA. [4]

API Demo

Independent Application Providers.

     Many companies have developed tools that allow for easy data integration from excel into GA. These tools provide the same functionality while providing an easy to use graphical user interface. A tool provided by Next analytics provides the ability to pull informaton from the web into excel for analysis, while another another application by GA DataUploader pushes data from excel into GA. For more information on applications that provide additional tools in GA please visit the App Gallery. [5]

GA Data Uploader Demo

Analysis through Reporting

     Once the data has been uploaded (which can take up to 12 hours) we can take a look at the effectiveness of the campaigns in relation to one another using the built in GA tools.

     Organizations must look at ROI holistically. Viewing a report showing the ROI analysis of a single advertisement campaign only tells you only a part of the story. It is through the compiling of all advertisement endeavors that allow for an understanding of how our efforts are effecting the bottom line. Yet even then, organizations need to continue to revisit the ROI that the analytics team bring to the table.


Customers Rule! Creating a Data Driven Culture that Puts Customers First

Every business needs one thing - customers.   We want to keep our current and past customers coming back and we want to acquire new customers. Surprisingly, we often fail to utilize the best resource to do this better - our customers.  There are numerous opportunities to gain insights into customer experience and how to tailor services, products and tools to improve customer satisfaction and create higher demand.  Business decisions are often driven by what Avinash Kaushik calls HiPPOs (The Highest Paid Person's Opinion), rather than what actually matters - the people interested in purchasing our products and services (Kaushik, 2010).

We can use our website to gather data on customer experience through overt methods, such as surveys and forums and through less overt means using web analytic tools.  Creating a culture in which businesses decisions are driven by data coming from our customers, rather than "gut feelings" and HiPPOs ground an organization in solid decision making practices and ensure that an organization remains focused on what matters most - customers.


There are multiple methods that we can utilize to gather data about customer experience.  Web Analytics software enables us to track metrics such as conversion rates, cart abandonment and click stream data that can all provide insights into user experience and identify problem spots.  These tools are helpful, but we often need additional data to help us identify where we can improve and to evaluate whether a proposed change will lead to better outcomes.  In this post I discuss a couple of these basic methods, A/B testing and voice of customer surveys.

A/B Testing

One of the best ways to gather data on whether a proposed change will be an effective one is through A/B testing.  A/B testing is a pretty straightforward concept in which two options are compared to evaluate which option performs best.  When considering a change to your website you can test the changes by directing a portion of your new traffic to the test page and comparing the conversion rates of interest.  Depending on the objective of the change - more sales, more subscriptions, greater number of page views, etc., you will create goals with which to compare with your original page.  Whichever page performs best in obtaining your desired objective is the one you use on your website.

From Paras Chopra's, "The Ultimate Guide to A/B Testing"

Common comparisons in which A/B testing is used include things such as color schemes for layouts and buttons, button location, general layout, pictures, price and promotions.  Using A/B testing to drive decisions will help your company to become more customer focused.  A/B testing will provide you with real world data of how customers react to proposed changes to your website.  No longer will you have to go off of what seems like a good idea, but will have real evidence of what works best.  This is important because results are often counter-intuitive.  You may think a certain layout "looks" better, but the less attractive layout does a better job of achieving the results that really matter to you.

For a more in depth look at A/B testing and how to implement an A/B test check out Paras Chopra's article, "The Ultimate Guide to A/B Testing".

Voice of Customer Surveys

One of the best ways to improve customer experience is very simple - ask your customers.  Not only are customers often willing to provide feedback to help you improve your business, they often appreciate the opportunity to do so.  Providing a forum where customers can communicate with your business in a meaningful way can increase customer loyalty and satisfaction.

Their are several ways in which you can implement a survey into your site, including an active pop-up window, a less obtrusive customer initiated tab or button on your site and a survey embedded directly onto your site. Pop-up windows provide higher response rates than customer initiated surveys, but may pose a problem of diverting users from a desired goal - particularly if you run an e-commerce site.

Tim Leighton-Boyce recommends a free text embedded survey on a thank-you page after a conversion as the best option, hands down.  Such an option doesn't get in the way of a conversion since it is presented after a purchase has been made.  Survey completion rates are higher than any other option by far and customers tend not to be annoyed with this take it or leave it request when presented on the thank you page after a purchase or other conversion has taken place.  Leighton-Boyce also recommends that you be sure to provide a free text response option with any other direct questions provided in such a survey.  This is important because customers may want to inform you about an issue, or a suggestion that you haven't thought about and your direct questions do not adequately capture.  This kind of data is much more time consuming to analyze, but is worth the additional resources to enable customers to address the issue of interest to them.

Tim Leighton-Boyce provides additional recommendations and resources in his article, "Let Your Customers Tell You How to Make Your Site Better".


These are only two of innumerable ways in which you can gather data regarding your customer's experience. The important thing is that you begin gathering this data, and even more important, that you use it!  Creating a culture in which decisions are based on quality data that is intelligently interpreted is actually a move toward a customer focused culture.  Let your customers tell you, either directly from their feedback, or from inferences made based on the way they interact with your site, what works well and what doesn't.

1. Avinash Kaushik, "Web Analytics 2.0, the Art of Online Accountability & Science of Customer Centricity," Wiley Publishing, Inc. 2010.
2. Paras Chopra, "The Ultimate Guide to A/B Testing", viewed 2/16/2013.
3. Tim Leighton-Boyce, "Let Your Customers Tell you How to Make Your Site Better", viewed 2/16/2013

Wednesday, February 13, 2013

Metrification Autometrically!

Metrics! Metrics! Metrics!  If I have to hear that word one more time I think I might just scream like Doc Brown at the end of Back to the Future II. 

GREAT SCOTT!!  I thought I sent Metric McFly back to the future!

But, that's the life of a web analyst.  They look at metrics everyday.  They read metrics everyday.  They dream about page view percentages and average time on a site.  They search through blogs and books about how to improve their SEO.  They attend conferences and workshops with names like the eMetrics Summit or any of the following:  SearchFest, Social Media & Web Analytics Innovation (try repeating that five times really fast!), or Webstock (which is like Woodstock, just without all of the drugs, half-naked people dancing everywhere and the legendary Santana and Jimi Hendrix).

Let me rephrase that, they devour metrics like 30 year old's devour Alphabits cereal.

Is that a bowl of Alphabits?!  NO, fool it's an ice cold bowl of metrics! Eat up!
Metrics are like the blood that pumps through the veins of web analysts.  They devote hours to scouring metrics with a combination of tools like Omniture, WebTrends and Google Analytics, just to name a few.  They bust out metrics at a party like ghostbusters bust ghosts.  They have Photoshopped pictures of themselves hanging out with Avinash Kaushik....what you don't know who that is?!  Click here for some edumication.


To further educate yourself in the ways of the web analyst there are is little metrics lingo that you will need to brush up on so you don't look like a complete maroon when attending those really neat conferences I previously mentioned above.

For example, you should know a little about the following:
  • Page Views - the amount of times a page on a website has been viewed.  These can be viewed by new, repeat or a return visitor.
  • Entry page - which is generally the very first page on a website that is viewed.  For example when you visit, that first page is the entry page.
  • Exit page - is the opposite of the entry page!  Exactly!  It is the last page you left from a website.  
  • Bounce Rate - is basically the rate of how many times a visitor exited a page on a website versus those that have stayed on the page.  For example, say you go to the link and search for motor oil.  Then you decide that Autozone has motor oil to and for a better price, so you type in your address bar  You bounced from the Walmart page to the Autozone page.
  • Conversion - this is a measurement of how many visitors have performed or completed some action on the web page.  For example, purchasing an item from or initiating a download from a website.  
  • Count - which is a measurement of how many visitors have entered a page on a website
  • Average time on Site - How long did visitors stay on your site.
Now with some lingo under your belt, your almost ready to get started into the world of web analytics.  The next step is to understand exactly how some of these measurements are categorized.  Generally there are 3 categories that metrics fall under:
  1. Individual - Activity of a single web visitor for a defined period of time.
  2. Aggregate - Total site traffic for a defined period of time.
  3. Segmented - A subset of the site traffic for a defined period of time, filtered in some way to gain greater analytical insight.[1]
The next step to understand, is that all of these metrics are the results of visitors...visiting a website.  This brings us back to another set of important metric lingo...visitors...

Not these visitors!

These visitors....

There are three types of visitors in the web analyst lingo.
  1. New Visitors - Are visitors that have visited a website for the first time.
  2. Return Visitors - Are visitors that have returned to the website.
  3. Repeating Visitors - Visitors that have repeatedly visited a website during a specified time.
  4. Unique Visitors - Wait didn't I say that there are 3 types of visitors?!  I did, and I am glad you caught that, because that means you are actually reading this post!
The three types of visitors: New, Return, and Repeating, are considered part of  Unique visitors.  Unique visitor is defined as "the number of individuals within a designated reporting timeframe, with activity consisting of one or more visits to a site.  Each visitor is only counted once in the unique visitor measure for the reporting period."[2]

So...uh..What Does All This Mean?

Now that you understand some of the basics it's now time to jump into the why of web analytics?  One metric to notice when you are testing out some analytics tools on your website, is the Average Time on Site.  For example, why were the visitors on your site so long?  

Were they engrossed in a web article like this one 
V-Day Special, How Web Analytics is a lot like Dating or were they staring off into space?  It's a good idea to figure that one out.  

If you have a shopping cart on your site, it's a good idea to ask yourself how long did it take for a visitor to get there and make a purchase.  How many of those visitors were new visitors?  There's a metric for that too!  

So what do you do with all of these metrics?  Like any good web analyst you search for patterns.  

Do you see a pattern? Hmmm...keep looking.
Web analysts are excellent at finding patterns within their metrics.  They can see why visitors spent too much time on their website or why they bounced out after 30 seconds off a specific page.  All of these metrics set patterns and trends that are used to figure out how to improve conversion rates on a website...which is that completed event thing I mentioned before.  

What do these trends tell you?

This is the Bounce rate of this blog.

This is the Number of Visits to this blog.

This is the Percentage of New Visits on this blog
The peaks tell us that there were quite a few that visited this blog on that specific date while the valleys are the opposite.  The same can be said of the bounce rate and the % New Visitors.  Since this blog first got started in January we have had 3,771 visitors, 2,067 of them unique with an average bounce rate of 60.81%.

From these trends we can also see from where our visitors are coming from:

The darker green = where most of the visitors are coming from.
Here you can see the the United States is the main visitor demographic with India in second place.
So far what does all of this tell us?  The majority of our visitors are from the United States and let's see what page is the most viewed....looks like with a total of 10,039 page views that the post in the lead is customer analytics incorporating with 264 page views.  Ah, but look at the unique visitors!  The blog post about the problems with google analytics is leading the pack.

SO....with all of this new information swirling around your brain, it's a good idea to let it sit for awhile.  And while you're at it, read some other more interesting posts like these for our blog site:

The time has come to end this post before I get a higher bounce rate!  Please read other posts while on this site to get those rates up!  

Related Resources

Why analytics? Why me?

Why analytics? Why me?

You want to be a professional.  You want to be at the top of you game, you want your company to be huge.  You have the people skills, the money skills, you can advertise and strategize.  So why analytics, what does analytics do for you and for your business? 


If you know who your customer is, where your customer is, and what they do and who they like, you can target them with specific advertisements.  Advertisements can be targeted through social networks, and be so specific that you need not worry about wasting your effort.  Targeted advertising can be done specifically to your “followers” in Twitter.  Or based on geographical location, age, gender, interests, anything you can think of to target.  The sorting of demographic data is done through analytics and it is going to change the face of advertising.  This is a good thing for both consumer and producer.  No more will men be subjected to feminine hygiene products, or the like.  Ads will targeted, and more successful.  Also with the power of customers to react to product you are forced by your public to make ads and products even better.


Humans are deep down lazy these days.  No one wants to find small golden nuggets in your website of doom.  They bounce!  If customers can’t find what they are looking for in a few quick clicks they are done and someone else gets their eyeballs.  How can you tell if your website is successful? Analytics!  You can track your bounce rate, your click-through rate, who goes where and from what country.  You can make small tweaks based on the information gathered and make a constantly improving experience.  How often does Facebook upload code?  Twice a day!  A website is like a rough stone rolling along the bottom of a riverbed, constantly being shaped and changed to smooth perfection.


What is business about?  Making money, and with analytics you can make more.  Target smarter, sell more, improve your brand image, it’s that easy.  It does not matter which analytics software you use.  Information is power.  As a business you want to know everything about your customer (no privacy) so you can make more money off of him/her.  Get more people interested in your products, create demand, raise your prices, expand, outsource, and make more money.  If you don’t like analytics, hire someone who does.  You will have more success with it than without it.

How did it all start ?

Figured it is only appropriate to write (and learn) about the history of web analytics for the last blog of the session. In addition, it would be interesting to find out whatever happened to the firms that had pioneered this industry; spoiler alert, most of them are no longer with us.


The first idea around creating web analytics, the accumulation and analysis of traffic and visitor behavior, came to fruition around 1992 (1). During this period this was a capability needed by administrators that wanted to get transparency into activity on their sites to ensure site was actually functional and it was up and available.
Probably, shortly after marketing firms started to look for ways that this information could be utilized to reach customers in a more efficient way. And it seems like this was the moment when the responsibility for web analytics moved from the technology division to the marketing teams.

Where are they now ?

In the beginning, 2 different schools of web analytics emerged, front runner to the birth of 2 types of analytics vendors  (2); software retailers similar to WebTrends (still in business as an independent company!) and NetGenesis (which hasbeen acquired by SPSS in 2001) and ASPs (Application Service Providers) including Urchin (acquiredby Google  in 2005) , Unica (now part ofIBM), CoreMetrics (anotherIBM target) and WebSideStory (boughtby Omniture which was later acquired by Adobe) which offered browser tag based approach. The “in-house” approach used installed application on a local hardware and meant to review data gathered in the web-logs. The other approach, Application Service Providers, tried to utilize the hosted solutions to view throughput and other relevant information. (3)

In the mid 90’s introduction of affiliate concept spurred an era of predictive web analytics shops and the first the company to came up with this idea was a small two year old company called Amazon (4) and popularized mainly by on hobby sites. At any time, a site could have a bar on top of their site with banners that connectedtheir affiliates. Usually, these sites (or affiliates) would have some common attribute with the other affiliates. As these relationships got more sophisticated, affiliate concept started to include sophisticated software which enabled the site to order links based on the visitor volume they got form their affiliate sites, basically incentivize affiliates to include links on their sites to swap visitors. Near these banner bars was usually a link to an external analytics service, where affiliates and site visitors could see how many unique visits the site received per day. (5)

Increasing amount of websites utilizing this sort of affiliate program drove the explosion in external web-analytics start-ups, and subscription driven services slowly started become more prominent. In the beginning, these subscription based offerings primarily targeted large enterprises, but over time started to target smaller entities and personal websites as they became more available and affordable with the emergence of e-commerce.

Following the dot com bubble burst, early 2000s, the web analytics industry entered an era of consolidation that even continues today and very evident a few paragraphs ago.

The Dilemma

I think the main question at this point is, whether such a rapid consolidation of a new field like web analysis raises the question of less innovation down the road. Obviously, in a very short period of time the huge technology companies Google, Adobe and IBM acquired almost all small, agile start-ups that had started the analytics paradigm. However, the obvious counter argument is these corporations have the means, both intellectual and capital, to fund costly software initiatives which could very well spurs growth and innovation. Only time will tell.

Meanwhile, Microsoft with 66 billion in equity (6) is nowhere to be seen in this market since 2009. I think it is not unfair to categorize this as an “historical” surrender and an interesting side note.

What is next ?

Finally, as the field matures, the new frontier and sure to be an historical milestone one day is the web ethics, that raises the question of the data should be collected and used. One thing is for certain, as the debate around ethics on the web, the utilization of cookies, tags and storing data increases, the analytics area will be impacted. It is going to be fascinating to see where the web analytics go next.


1)      Brief History of Web Analytics. Dems, Kristina

3)      Application Service Provider. Rouse, Margaret

4)      History of Affiliate Marketing. Collins, Shaw

5)      History of Affiliate Marketing. Vredenburgh, Evan

6)      Microsoft, Wiki.



What, No Cookies?

    On goes the discussion of whether cookies are bad for you. Real cookies are bad for you, all that sugar and fat. Cookies on your browser aren't bad, but they get a bad rap for being something that invades your privacy. Cookies are simple files that are placed on your browser. These cookies contain pieces of data that uniquely identifies you.(1) "So the cookie on my browser has my personal information in it?" Umm, maybe 1 time out of billion would hold something unique about you. They hold a variable value that is sent in on any request to servers that come from site.

For example, if you login into your online bank account. You get a cookie, this cookie sets your session and tells the website that you are the same visitor as you travel along the site.(2) Good, right? I wouldn't want to login on every page that I go to on my bank site. In this example if you are banking at BankofAmerica the cookie would be set from a domain or, which is a First-Party cookie, because it set on from the domain of the website you are currently visiting. A third-party cookie would be a cookie set from another domain. A third-party cookie would include analytics and digital marketing cookies. Some examples would be Double-Click, Adobe, or Google Analytics.

First Party Cookie

3rd Party Cookies

This images above are so scary right? I thought I saw my social security number in there, but of course it doesn't.

    "Well what about 3rd party cookies, aren't they some outside company gathering information about me?" Yeah, but not private information about you, just what you are doing on the site. I explained above that it assigns a unique ID to the visitor and keeps the visit together. This is the same thing that happens with the 3rd party cookie. For example, Double-Click would set a cookie on your browser when you land on a website. They would track your interaction with ads or content on the website. They can use this information to form a profile for you based on that cookie value. These profiles provide analytics companies with details what type of content you woud be interested in and then provides the ability to show relavant content. Some think this might be intrusive to have a there actions tracked as they travel around the internet, which is okay, because all browsers give you the ability to disable cookies or block on 3rd party cookies. Doing this can cause some website to not function as intended and wouldn't provide online marketers with ability to provide you relavant content.

So cookies aren't bad you just need to be aware of what they can do and how they make the internet awesome. If you are still scared you can block them. If you want more information on cookies look on the internet :)

Mobile Analytics

Data analytics, big data, data scientists, etc., are terms that you hear about more and more every day. The ability to collect and turn data into knowledge and make decisions based on that knowledge can give one company a competitive advantage over another.

Another technological area that is taking over the world is mobile. Smart phones and tablets are popping up everywhere and are quickly replacing laptops and desktops as the main source people use to access the internet.

Put these two fast growing areas together and what do you get? Mobile analytics.

As more and more people use mobile devices to access the internets, it is going to be crucial that business be able to track and analyze how they are interacting with their websites. Some of the types of data collected as part of mobile analytics typically include page views, visits, visitors, and countries, as well as information specific to mobile devices, such as device model, manufacturer, screen resolution, device capabilities, service provider, and preferred user language.

 Collecting and analyzing this data is not only needed to help improve marketing and sales, but is also important for maintain and updating company websites. With cell phones and operating system versions being released a break neck speeds, it is important to know exactly what devices are accessing sites so that the site can be continually optimized to function with each successive device and operating system.

Unfortunately, traditional analytics do not always work well when applied in a mobile environment. Traditional analytics software on a mobile website may only provide data for HTTP requests coming from the most advanced mobile browsers, such as those found in the iPhone and other smart phones and PDAs, with no data on other mobile devices browsing the site.

Traditional web analytics software that uses server log parsing and associates different IPs with "unique visitors" may fail to identify unique visitors, because the IPs from which cellular wireless network HTTP requests originate are the gateway IPs of the network access providers. Several dynamic server-side platforms are used to develop mobile sites. Server-side tracking code is recommended for more accurate analytics reporting.

 Solutions for Mobile Analytics

So what are some good solutions that business can use to improve their ability to analyze mobile traffic?
  1. Bango 
  2. Google Analytics
  3. Pinch Media & Flurry Analytics


Bango is an analytics platform for both mobile apps and the mobile web. Bango provides identification for every user accessing the app, providing information like the user’s carrier and connection speeds. You can also use Bango to drive mobile app campaigns and implement tracking for other application features. Bango offers integration of their analytics into most major mobile smart phones, including Blackberry, iPhone, Android, Palm, Windows Mobile and Symbian.

Google Analytics 

Google Analytics is most famous for providing a free and powerful analytics platform for websites, but unknown to most, it also offers a leading mobile analytics platform. The platform is easily incorporated into a mobile app for Android and iPhone, in addition to mobile web applications and sites. Their mobile web analytics are available to developers in PHP, JSP, ASP.NET and Perl to allow for easy implementation. The service is free and the interface is very similar to their website analytics.

 Pinch Media & Flurry Analytics

Pinch Media, and Flurry, two leading mobile analytics platforms, recently merged. They provide a free specialized service for analytics in mobile apps. They allow you to tap into user info with the approval of the user, giving you location, age, time, session lengths and more. You are also able to send messages and information to the analytics for later analysis. Sending information about what the user does while in the application is a great way to take advantage of such features. Pinch and Flurry also offer advanced analysis features like the ability to judge a user’s loyalty based on the number of sessions they have spent within the app.



Recently I saw a tweet directed to a company’s customer service with the hash tag #nevahold. As I was unfamiliar with this, I searched for this term and was surprised at what I found.         
Nevahold is a Ghanaian startup company which went online in late 2012. This company aims to take “customers’ requests and questions through their platform, and if the company doesn’t reply in reasonable time gives negative reviews on social sites.”
Additionally, the hash tag #nevahold can be added to any tweet to increase awareness of a possible customer service issue and, ideally, spur action by the company regarding that customer service issue.
Nevahold does not exist simply to complain about bad experiences with companies. There is a “praise” option on the website, which can provide positive support and visibility for a company. Additionally, “nevaHold is different from others because not only does it want to help the customers, it also seeks to improve the experience for the company’s side as well. It offers a customer service tutorials in addition to their quicker and more complete support resolution, and less negative social media feedback. NevaHold believes that great customer service is more powerful than promotions and discounts, and hopes to help brands leverage smart CRM to help them stand out in a crowded digital economy.”

It remains to be seen how this will affect companies. Currently, according to, Nevahold is ranked 2,499,790 among websites. According to Twitter, Nevahold currently has 585 followers. This data clearly suggests that Nevahold has a ways to go in accomplishing its goals in influencing responses from large companies. However, as the company is really only about six months old, there is plenty of time for this to catch on and start influencing customer service for the better.

V-Day Special: How Web Analytics is a lot like Dating

V-Day Special: How Web Analytics is a lot like Dating

In slew of Valentine’s Day and wrapping up my course in Web Analytics, I thought it’d be amusing to compare the subject to something we can all relate to: Dating. While some may roll their eyes to this thought or balk at the idea of comparing web analytics to the frivolous world of dating, hear me out. Have you ever thought that having a website and trying to reach customers and/or sell products is a lot like finding a soul mate? Well the two topics have more in common than you may think and for those who are just coming to the world of Web Analytics, it’s a nice way to learn key terms and concepts to apply to their own online ventures.

In dating, it all begins with boy meets girl or vice versa. This could also be the same when a customer finds a website or a website seeks potential customers. It’s all about initial attraction, alluring content, and ultimate retention. One could say that both dating and web analytics reside in competitive markets. With so many options for individuals to choose from (we can only hope in terms of dating), it’s beneficial to understand the factors that can help us reach our goals and environment for which these two topics exist in.

To make this argument on how web analytics is a lot like dating, I’ll create a side-by-side comparison of the two concepts and illustrate the technical terminology to finding and maintaining the perfect companion.

Key Business Requirements (KBRs)

"If you don't know where you're going, you probably won't get there" -Yogi Bera

Web Analytics

For any website, a KBR constitutes an overall objective that the business is trying to achieve. Since every business (online or not) is unique, there will be varying KBRs across the board. They do however share a common goal: to contribute to the overall improvement of a business.

Examples of a KBR include:

-Selling more products
-Expanding to new markets or attracting different types of customers
-Improving the customer experience
-Increasing brand awareness


Like any business, individuals in the dating world also have objectives he or she is trying to achieve. Often times these goals (or KBRs) can depend on varying circumstances that can often coincide to what a business is experiencing. Everything from timing (dating: where an individual is in life; business: a product life cycle) to resources (dating and business: available funds).

Examples of a KBR include:

-Wanting your partner to "pop" the question
-Looking for a long-term relationship
-Needing free meals (hey some girls are on a budget and need to eat!)
-Looking for a fling or casually dating

Key Performance Indicators (KPIs)

"The measure of love is to love without measure" -St. Francis De Sales

In a nutshell, a Key Performance Indicator (KPI) is a metric that helps businesses understand how they are doing in relation to their KBRs (see above description if you've already forgotten). It's imperative for a business to not only understand their KPIs, but also choose those that properly illustrate their website's performance and provide insights on how to re-evaluate their online presence.

To provide a useful comparison of KPIs in both Web Analytics and Dating, I'll outline four popular metrics and provide descriptions in Web Analytics terminology how they would translate to the dating world.

1. Visits, Visitors and Unique Visitors

-Web Analytics: The number of visits correlates to the number of arrivals to a website. These arrivals can be broken down into visitors and unique visitors. The difference between a visitor and a unique visitor is that a visitor only visits the website once, while a unique visitor returns at least once.

-Dating: The number of visits in dating could translate into the number of dates an individual goes on. A "visitor" would a potential mate that never turned into a second date, while a "unique visitor" found themselves making "the cut" to the second date.

2. Time on Page and Time on Site

-Web Analytics: The "Time on Page" represents the time a visitor (whether unique or not) spends on each individual page within a website. On the other hand, "Time on Site" represents the total session time a visitor spends on a website.

-Dating: While there are many ways I could compare these two definitions to dating, I'll stick to the most appropriate representations.

The "Time on Page" could represents time spent discussing different subjects on a date (i.e. "What are your hobbies?", "Where do you see yourself in 5 years?" (Let's hope you're never asked this on a date)). The "Time on Site" would therefore represent the total time spent on date.

3. Bounce Rate

-Web Analytics: As stated by Digital Marketing Evangelist Avinash Kaushik, "the Bounce Rate is the sexiest web metric ever!". The bounce rate measures the percentage of visitors who enter a website and "bounce" (leave the site) rather than continue viewing other pages from that website.

-Dating: In terms of dating, the bounce rate would represent the percentage of potential suitors who unfortunately leave a date without giving any second chances (which would of course never happen to any of us!).

4. Exit Rate

-Web Analytics: While this may sounds similar to the Bounce Rate, the Exit Rate signifies how many visitors left your site from a certain page, meaning whether they left from the "home page" or the "about me" page. Knowing the exit rate provides great insight to a business by showcasing where on their site needs potential improvement.

-Dating: Knowing the "Exit Rate" in dating correlates highly with one's self-improvement. An example of an Exit Rate would be the amount of people that no longer stay interested after knowing how many felonies you've committed (yes it's extreme, but possibly true in some cases) or knowing how many divorces you've had within the last year.

Measuring Success

After outlining the KBRs and KPIs and their representation in Web Analytics and Dating, it would only be appropriate to analyze the final outcome. Often times we need to both step back as a company or individual and determine how we measure success. While success can often be numerical valuations from a company's viewpoint, dating on the other hand is incredibly subjective. For the sake of this post however, we'll focus on the most common success metric for websites: conversion rate.

The technical meaning of a Conversion Rate is defined as Outcomes divided by Unique Visitors (or Visits). Whether a business chooses to use "Unique Visitors" rather than "Visits" depends on their business objectives. If they were to use "Visits" as the denominator, it would be assumed that for every visit, the website has a chance to have the individual purchase a product and thus convert the user. Alternatively, if the company were to measure the conversion rate with "Unique Visitors", it would imply that a visitor could visit the website multiple times prior to purchasing a product. The difference between the two depends entirely on their marketing scheme and how much customer loyalty exists.
Measuring success in dating often depends on the individual and their unique circumstances. Therefore translating the Conversion Rate into dating terms could go many different directions.

Here are some examples Conversion Rates in Dating:
   The percentage of potential suitors that ...
   -Making it to the second date
   -Achieving a "yes" answer to a marriage proposal
   -Reaching the "boyfriend-girlfriend" status

Additional Resourses on Dating and Web Analytics