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 

What Women Want: Web Analytics

What is it that women want!?! Let's face it, one of the greatest mysteries in this life is figuring out what women want. Men sure don’t know and I would dare say that many women don’t either. Some might say that marketers have figured it out, these guys know what sale and what do not sale. But I think marketers have missed the mark. In this post I am going to go out on a limb and suggest that web analytics may actually be a tool to help all business persons male and female, understand what women want in online ecommerce or emergent mobile markets

Before you exit this page, press delete, thumbs down, or label me as some “crazy” feminist, allow me to explain myself. Every day, men and women and boys and girls are exposed to the ideas that are valued by those who control the purse. Unfortunately, it’s not the women who control the purse in the media and business world. Allow me to illustrate this example by sharing one of Colin Stokes’ observations he shared in a Ted video. He points out that seldom will you ever watch a movie where there are two women characters who actually have meaningful roles or even when two women converse about things other than their mutual crush. He adds that in "Argo," the critically acclaimed blockbuster, the major line said by a woman in the movie is "Are you coming to bed Honey?" Need I say more? The dearth of missing dialogue between women to women and men to women is a testament that men and some women don’t know what is important to women or that what is important to women is undervalued. Now where does web analytics fit in all this?

Web analytics, if used properly, can be a powerful tool. It enables website designers, marketers, and managers to have immediate feedback as to what “works” and what does not on a website. Web designers, marketers, and managers can implement A/B and multivariate testing to see what web pages have lower bounce rates, who seem to encourage conversion, and are the most popular among users. Web analytics can even have customer specific information that can be aggregated to show trends among clusters of users. Web analytics presents a great opportunity for designers, marketers, and managers to capitalize on the ability to experiment on their websites to actually understand better what it is that women want in a website. I will now elaborate on this idea by focusing on mobile fitness apps.

In my emergent technologies class the other night we were discussing what makes a fitness app successful. Words and phrases like “tough competition” “intensity” “muscle mass accumulation” echoed throughout the discussion (I think I even heard a few grunts of approval). But I couldn’t help but wonder if these are programmatic attributes that women want in a fitness app. The men in the class also suggested monitoring heart rate and calories burned. These are important to women of course too. But not a single classmate suggested a successful app might allow the user to track other weight-loss indicators such as the following:

- Emotional stability
- Frequency of emotional eating
- Frequency and potency of chocolate cravings caused by unbalanced hormones
- Level of pain caused by menstrual cramps

For women, these issues are inseparable from weight loss. Of course my classmates did not come up with these suggestions. They are men! It is not likely that they would come up with these suggestions...ever...Because men and women experience the world differently. Now, don’t get me wrong. Women are VERY competitive. We just compete in different ways and we are different from men physiologically. These differences need to be taken into account when designing websites.

If competition between participants is magic formula for male fitness app users, what is the female counterpart magic formula? Now is an ideal time to launch emerging market online and mobile apps that experiment and seek for the “magic” formulas that empower women.

We do need to be cautious, however, in our attempt to understand what women want by analyzing web analytics. The website needs to be designed strategically or we may take home the wrong message and find ourselves just as clueless as we were before. Avinash Kaushik explains in his book Web Analytics 2.0 that when taken out of context, the data pulled from your website can be meaningless or completely understood (2010). He also suggests adding context into your analysis by asking your viewers questions. I would dare say that he is asking us to couple quantitative analysis with qualitative research. For example, a manager might choose to A/B test two web pages that look like they came out of Cosmo and airbrushed photos of sixteen-year old girls that look like sex goddesses (Russel 2012). The manager will be able to see which page was more effective at getting conversion rates. However, the manager will be no closer at understanding what women really want then they were before. However, if a manager were to decide to implement a web pages that had more uplifting and empowering messages, followed up by a three to five question survey asking them about their experience, then the manager might have a better understanding.

Now is the time. More women have a college education then men in the United States. More women then men have taken management roles. The wage gap between men and women is decreasing. More men are taking on the role of care-givers. The world as “we” know it is changing (Rosin 2010). Why not design our websites and the tools that we use to evaluate them more strategically. Why design our websites and the KBRs and KPIs we use to judge the websites around what women really want?




Citations:

Kaushik, Avinash (2009). Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity (p. 148). John Wiley and Sons. Kindle Edition.

Rosen, Hannah (2010). The End of Men: And the Rise of Women. Penguin Press. NYNY.

Russel, Cameron (2012). Looks Arent’ Everything, Believe Me, I am a Model. http://www.ted.com/talks/cameron_russell_looks_aren_t_everything_believe_me_i_m_a_model.html

Smith, Dorothy (1987) The Everyday World As Problematic: A Feminist Sociology. Northeastern University Press

Stokes, Colin (2012) How movies teach manhood. Ted: Ideas Worth Spreading.http://www.ted.com/talks/colin_stokes_how_movies_teach_manhood.html. .

Falling in Love

The focus for most firms is to get their customer to convert.  Whether it’s a visit, a click, or a purchase, conversions come in many different forms but they are the ultimate goal.  Sometimes this conversion can take weeks.  Other times the conversion can be immediate.  Conversions are just like falling in love.  There are some that fall in love at first sight.  Others can take many months.  Everyone is different. 

Multichannel Funnels

(Source: Inviting Smiles)

In love, there is no secret love potion to cause two people to love each other.  There is no Cupid flying around shooting arrows causing people to love.  The same goes for conversions.  There isn’t a magic potion that unlocks the secret of how to convert people.  Almost everyone is converted differently.  As such, in creating a marketing strategy, several different campaigns have to be implemented.  This is called multitouch or multi-channel funnels; there are multiple touches by the user before they are converted or there are multiple channels through which they arrive at conversion.

For example, a user might take a path similar to this in his/her process to conversion:

1. Day 1
1. See a banner ad for the first impression but didn’t click
2. Day 7
1. Do a organic Google search for the product
2. Visit the site and sign up for email promotions from the company
3. Day 14
1. See the product featured on a friend’s Facebook news feed
4. Day 21
1. See a sponsored tweet from the company while surfing Twitter’s timeline and click on the link that leads to a YouTube video about the product
2. Watch the YouTube video and take no further action
5. Day 28
1. Receive email promotion directly from the company
6. Day 35
1. See a banner ad on Yahoo
2. Click on the banner ad and visit the website
3. Make purchase and become a converted customer

As we can see, the process can be very lengthy.  It can be almost impossible to determine exactly when the customer finally fell in love and converted.  They could have been converted on Day 7 but didn’t pull the trigger until Day 35.  Or it could have taken them the full 5 weeks to finally become a paying customer.  For companies, it becomes very important to diversify their strategy, as it is not clear through which channel their customers will come.  They must also determine which of the many channels is the most successful in conversions so that they can best allocate their marketing dollars. 

Who gets the credit?

We all want to take the credit.  These multiple touches and channels lead to the issue of attribution.  The attribution problem is knowing who deserves the credit for the conversion.  Just like George, Google, Yahoo, Facebook, Twitter, and the actual company itself can all claim to deserve at least some of the credit in the conversion in the example above.  They all possibly contributed in the user falling in love and as a result, feel like they deserve some of the revenue. 

The most common and standard model to an attribution problem is Last-Click Credit.  In the example above Yahoo would receive the credit for the customer’s conversion, as they were the last click.  Even though there were several other impressions and touches before the Yahoo banner ad, the last channel receives the revenue credit. 

Google Analytics

As web analytics develops in 2013, there are more and more tools that are available to help better distribute credit among different channels.  Now that Google Analytics is available to everyone, more and more website owners have the ability to better track credit and solve the problem of attribution.  Google Analytics gives users the ability to choose the model that best fits their situation and allows them to allocate their budget accordingly.  Here are a few of Google’s models that give users something more than just the Last-Click Credit model:

(Source: Google)

This is just the beginning.  Attribution modeling that addresses the multiple channel issue is just part of the story.  Going forward there are other issues that will arise.  Mike Shaw from comScore pinpoints the challenge of web analytics.  “Analytics providers need to adapt to this changing digital world to become a trusted resource for understanding cross-platform consumer behavior and enabling multi-platform unification of all data” (Source: The Drum).  As analysts try to understand the true value to each little piece of content on the web through the many interconnected channels, it will become more and more important to unify that data.  The current attribution models still don’t paint the whole picture and can’t tell us how users are falling in love but it's definitely a start. 

References

• Kaushik, A. (2010), Web Analytics 2.0, Indianapolis, IN: Wiley Publishing, Inc. 
• http://www.google.com/analytics/features/multichannel-funnels.html
• http://www.thedrum.com/news/2013/02/05/what-s-biggest-challenge-web-analytics-2013
• http://www.marcelmedia.com/blog/understanding-conversion-attribution-multi-channel-funnels-from-google-analytics-2/
• http://services.google.com/fh/files/misc/marketing_attribution_whitepaper.pdf