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.

LET'S GET METRICAL!

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 Walmart.com, 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 Walmart.com 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 www.autozone.com.  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 Amazon.com 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:

Adventures in Ghostery

Yesterday's Web Analytics is Today's Digital Analytics

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

[1] http://www.kaushik.net/avinash/web-analytics-standards-26-new-metrics-definitions/

[2] http://c.ymcdn.com/sites/www.digitalanalyticsassociation.org/resource/resmgr/PDF_standards/WebAnalyticsDefinitionsVol1.pdf 

Right Web Analytic Tool is a choice you have to make.

 “You Can't Manage What You Don't Measure” is an old adage most of us may have come across. Senior level executives and managers always ask for right data to make management decisions. Right data is hard to find, but fortunately in today’s e-commerce world, web analytic tools are making life easy to find the right kind of data. The next challenge would be to choose the right kind of web analytic tool, which would give a good return on investment to make great decisions based on the right data at the right time.

A lot of information is available on the internet on how to buy the best tool to perform web analysis. We have websites like About Analytics and Ideal Observer which can determine which particular tool would be better for a particular application. In general Abobe Site catalyst and Google Analytics lead the pack for enterprise and medium retail businesses respectively.

Google Analytics continues to gain new customers because it is free and is very easy to implement and has a good functionality. Many companies do not require any more functionality than what Google Analytics offers and should get the job done. However some companies require extra integration, data control, ad-hoc reporting or unique visitor tracking that Webtrends or Site Catalyst can offer. Google Analytics is free but often requires more professional service to accomplish the same analysis that which can also be performed with SiteCatalyst (Adobe Product) and Webtrends. It reminds me of the famous saying “You get what you pay for!“.

In this regard, Omniture (now currently Adobe) really was in a way very disruptive in the field of web analytics and it left Webtrends, which once (before 2005) led the market, behind and is still catching up to Abode. Abode offers great underlying architecture and reports that greatly integrate with the user interface (UI). I have not had the pleasure of using the Site catalyst personally yet but a fellow student, an analyst showed me its UI, which looked very advanced (with 3D mapping etc) compared to Google Analytics with which I have some familiarity.

When comparing product functionality, checklists offer the most visual differentiation. Some companies have created almost 500 criteria to compare tools and some companies have considered very few aspects when comparing tools before they a make a decision to buy one.

Web Analytics is not all about data collection and different analysis tools and application, but also about human resources available to use the tools and analyze the data. An efficient Return on Investment (ROI) will be, if one budgets 20% on tool and 80% on human resources. In other words, a cheap tool and a really experienced powerful analyst will result in greater ROI than a powerful tool and a not so great an analyst.

Ultimately, we want to use web analytics tool to optimize web sites to ensure that they deliver value, and we make necessary site changes as we learn about deficiencies through our analysis. But that brings many other tools into the equation such as data warehousing, A/B testing tools, campaign management and so on. Consequently, one has to consider how well these three tools play with other vendors and how complete and integrated their offerings would be when considering the entire infrastructure.

In the end, the tool selected should be able to streamline one’s core business data to be efficiently managed based on how the customer is going to use your website. Best of Luck on choosing the right one!  

References:

(1)http://jfbelisle.com/wpcontent/uploads/2013/01/web_analytics_solutions_market_share.png

(2)http://scalabilityproject.com/answering-what-is-roi-with-analytics/

(3)http://semphonic.blogs.com/semangel/2012/05/webanalytics-tools-comparison-websites.html

Web Analytics: Reporting Vs Analysis

        Powerful web analytic tools such as Google Analytics, Adobe Site Catalyst, and Webtrends are providing businesses key insights into how their customers interact with their web assets. Much like other tools, they are only as effective as the individuals who utilize them. Managers who believe that web analytics ends at singing the license agreement and providing the intern w/ the software's user manual are ignorant of the expertise required to extract value from data aggregated by analytic tools. Without the help of knowledgeable web analysts, companies with access to these tools will succeed solely in gathering large amounts of  expensive data. Both analysis and reporting draw from the same pooled data, with reports offering questions about the data that analysis must answer.

Reporting
   

     Reporting is defined as the process of presenting information with the purpose of allowing areas of a business to be monitored. Reports can offer a framed window into the vast amounts of raw data, which would other wise be unintelligible . Useful reporting can quickly allow managers to track day to day quantitative stats, compare current data w/ historical figures, and automatically alert managers when data falls out of predefined ranges. Web analytic software  reporting tools can allow users to deliver reports utilizing pre-define metrics ,while also  creating custom "dashboards"  based on the organization's KPI's. 


Examples of Reporting Deliverables

• Standard Reporting Tools
• Created by Google engineers to  quickly address the "who, what, when, where" of web data
• Saves analysts time by having preformatted reports 
• Little contextual value

Standard Report : Created by Google developers

• Dashboards
• Allows users to create custom reporting tools to address data that is relevant 
• Users can create reports that directly address their specific organizations KPI

Dashboard: Customized to Address KPI

   
    With so many ways of formatting raw data through analytic software suites , companies can fall risk to believing that reports offer insight into accomplishing mission critical goals. Take for example web site visits during a weekend . A web analytic suite can offer a report on how many people visited your site, but can not tell you how to leverage it achieve your goals. The role of reporting is to push information in order enable users to starting formulating questions based on what they see in the formatted data. Once the question has been asked , we can then formulate a strategy on how to address it. 

Analysis

   Analysis is the process of delving deep into data and reports in order to find useful information that could be useful to your organization. Unlike reporting, analysis requires that individuals pull conclusions from specific data regarding  business problems and questions. Many reports are automatically generated and can both illustrate and quantify some change that has occurred.  Although useful, a report is unable to describe the cause of the change, or how it can be addressed.  Good analysis tells a story. It provides context to the presented data in order to correctly frame the scenario or problem that needs addressing. 
      Once the problem or goal has been identified, the context defined (using specific data applicable to the problem), analysis then requires individuals to advise on the course of action that managers should take. It is this last step that empowers managers to act and ultimately obtain returns on web analytics.


Example of Analysis Deliverable

• Formal Presentation
• Highlights qualitative data pulled by reports in order to provide 
• Clearly defines what insights were discovered 
• Provides a recommendation on what actions management should take 

References 

http://www.kaushik.net/avinash/difference-web-reporting-web-analysis/
http://www.zoommetrix.com/online-strategies/role-of-web-analytics-analysts-and-expectations/
http://www.kaushik.net/avinash/how-should-web-analysts-spend-their-day/
http://blogs.adobe.com/digitalmarketing/analytics/reporting-vs-analysis-whats-the-difference/

      

Discover Adobe's Best Kept Secret

DISCOVER ADOBE’S BEST KEPT SECRET

Adobe Discover is one of the newest digital analytics tools on the market with the most graphically supported interface. Discover creates comprehensive, multidimensional reports and allows layered filtering, giving analysts a deeper level of consumer use.

Discover vs SiteCatalyst

Both analysis tools, Adobe Discover and SiteCatalyst, offer complex trend reports; Discover, however, allows the data to be dynamically segmented anywhere needed. This extra step develops a deeper picture and offers the ability to run quick comparisons. Additionally, Discover can be filtered within layers. Segmented data can be filtered multiple times until a specific criterion is met.

Discover provides its users with the ability to compare different segmentations side-by-side in multiple columns. One column may be used to represent page views from Android devices while another column could display iPhone use. This side-by-side view creates a quick picture of user trending and immediately describe a user story.

The downfall with Adobe Discover is found in its limited pre-defined reports. SiteCatalyst offers a range of ways to examine conversion rates, such as funnel and fallout reports by a few simple clicks. Discover, however, requires a greater range of complexity to determine this same rate of conversion.

Site Analysis

The two most unique and graphically appealing features in Adobe Discover are the Site Analysis and Virtual Focus Group.  Site Analysis is a 3D look at the top page views and flows. This tool can be used for research and immediate presentation. An analyst can move around a multidimensional graphic to show a high level view of its site’s use. Pages with the highest user interaction will appear as the largest cylinders and bidirectional animation will demonstrate where users generally travel after each page.

The Virtual Focus Group randomly chooses a user based on defined criteria and displays the individual’s entire web visit through time-lapse animation. This tool can be especially helpful to determine why users tend to fallout of a shopping experience.

Avinash Kaushik states in Web Analytics 2.0  “SiteCatalyst, its flagship web analytics tool, is now just one of its core offerings...Pretty soon Omniture will be able to wake you up with a gentle tap and help you into your work clothes.” 

Avinash made this comment before Adobe purchased Omniture and released Discover. Today, his statement rings even more true. Discover allows analysts to pull back additional statistical layers and be one step closer to fully understanding  their client base. On top of that, Discover puts these layers into a nice, attractive package ready to be presented. 

Want to learn more? View my personal blog to see all my posts and receive a FREE HTML Programming Manual

Reference:
Kaushik, Avinash (2010). Web Analytics 2.0. Indiana: Wiley Publishing Co. 3.

Alternatives to Google Analytics

Google Analytics is a great tool. And it’s free, which only adds to the greatness. But let’s explore some alternatives so we know what else is out there. After all, every business has its own unique goals and objectives, and Google Analytics cannot serve them all. There could be many reasons to look at other options. Perhaps a company wants multiple analytics programs. Even though this could get complicated, it can also confirm the data. Maybe you just don’t trust Google. This might sound crazy, but there are definitely companies out there that don’t agree with Google’s service and privacy terms, so they avoid it as much as possible. And lastly, maybe GA just doesn’t cut it and additional functionality is needed. I’ll walk you through three decent alternatives for SMBs that could provide the right analysis to help optimize a company’s website and online business.

Number 1: KISSmetrics

The first thing you see on KISSmetric’s website is this:

 So this company is obviously comparing itself to Google and boldly suggests it does even more. KISSmetrics claims to differentiate itself from GA in the following ways:

·         It helps you to get to know your customers

·         It offers simple design and usability for the most complex businesses

·         It provides actionable analytics and insights

One of the great claims it makes is that it has solutions for any business’ unique needs. If you’re looking for a platform, KISSmetrics provides a 14-day free trial to help you know if it’s right for your business. If you decide to use KISSmetrics, you’ll be paying anywhere from $49 to $499 based on the plan.

Number 2: Coremetrics

Coremetrics claims to have sophisticated analytics that provide businesses with real time insight into how consumers are interacting with their brands online. I’ve never used it personally but I have heard good things. It was recently acquired by IBM and is now one of IBM’s marketing products. Here are a few things it offers that stand out to me:

·         Real-time reporting

·         Easy setup with a unified tag manager infrastructure

·         Integrated social media analytics and reporting

·         Unique, event-driven customer segmentation

There are actually a lot more I could add to the list, but these four were my favorites. Some of the others were similar to features the other platforms have that I mention so I left them out. I’m not sure how much this would cost, but I’m going to assume it would be around the same as KISSmetrics.

Number 3: Hubspot

I saved this for last because I personally like it the most. Hubspot provides all sorts of marketing tools and platforms and one of these is its closed-loop analytics. This platform provides actionable analytics that track the effectiveness of your marketing efforts across various channels. You’ll know how your customers found you, any critical touch points for conversion, which campaigns are most successful, and what actions generate the highest quality leads.

Here are three features that really stick out to me:

·         you can track up to ten competitors

·         you can automate detailed reports

·         you can view industry benchmarks

You can create pretty in-depth reports in Google Analytics, but to my knowledge, you cannot accomplish the other two features I just mentioned, and for these reasons, it would be smart to look at Hubspot’s state-of-the-art analytics tool. There is a tom more I could discuss about Hubspot’s analytics, but I’d rather keep this short and to the point. I’m unsure on the monthly cost of using Hubspot, but I would guess it’s more expensive than the aforementioned.

So there you have it; three more great options for web analytics other than Google Analytics. I did not mention Adobe’s SiteCatalyst or Discover because these are the platforms I currently use at work, and I wanted to explore and learn about other options. But they are also very popular (and very expensive) options. I hope you’ve learned something. Please feel free to mention some other great options in the comments or some other cool features of the ones I have listed.

Refer to the following sites for more info:

KISSmetrics
Coremetrics
Hubspot