The 19th Annual Screen Actors Guild Awards, engaging, or was it?

News of TNT and TBS inking a three-year deal to carry the SAG Awards could be a sign that the program had enough hutzpah to move audiences. It’s hard to tell when you’re measuring engagement based on the data that’s transmitted from a sample of television sets.  More than 5.2 million tuned in, but how many were actually watching the program and was this number taken two minutes into the program?

Measuring engagement is hard and in terms of Web analytics, it just can’t be done. Not, yet.

Still baffled by engagement, Web analytic platforms still haven’t quite figured out how to apply metrics in a way that allows marketers to easily monitor online engagement. In an age where every keystroke, click, scroll, and hover can be tracked, how could it be that we can’t measure when someone is engaged with our website? The SAG Awards were streamed online, but were viewers getting inspired by the fashion the stars chose to wear on that special night or were they looking at their smartphone? You will find some Web analytics solutions touting engagement metrics, but when you actually use these metrics with the purpose of discovering an engagement level, you’ll find yourself more confused than ever.

Let’s look at three metrics commonly mistaken to measure engagement: Time on Site, Repeat Visits, and Pages Per Visit.

What does the amount of time someone spent on your site tell you? Could it be that visitors are enthralled with your content and just don’t want to leave? Possibly. Could it be that a visitor is having trouble filling out your online form? Possibly. So, which is it? You’ve got someone who has spent five minutes on your site. Were they engaged? If you’re measuring Time on Site, call it Time on Site and not Engagement (Kaushik, 2010)! You can’t conclude if your website is engaging by analyzing this metric, alone.

Repeat visits isn’t necessarily the signal flare for engagement. Unless you know why people are visiting your site within a specific time period, then you can’t tell if they are engaged.

Pages per visit may look enticing and you want to believe that it’s a strong indicator of positive engagement, but the truth is you still can’t differentiate between someone frustrated looking for information they just can’t find and someone who just can’t get enough of the goodness your site is offering.

These metrics each give us a better understanding of how a user is engaging with our website -- yet none of them hold the key. In order to use data to measure engagement, you have to get creative with what you're measuring (Rawski, 2012).

There are two fundamental terms that drastically clears up the issues of measuring online engagement—degree and kind.

My friend Theo Papadakis shared this brilliant insight with me: quantitative data (web analytics) is limited in that it can measure the degree of Engagement but not the kind of Engagement (Kaushik, 2010).  How good is that? Really good!

Degree is the level of involvement and kind is positive or negative engagement. So, if Web analytics can measure degree, how can we determine the kind of engagement? In addition to offering visitors engaging content, giving people control over the content they consume on your site is a giant leap towards measuring engagement.

Measuring interaction is the key and with this information, the three metrics above will finally have the opportunity to tell you the story you’ve been longing to hear. , Interaction could be number of downloads, shares, or video views. You get the idea. This type of activity isn’t captured in Web analytics tools, but by using other resources to track action you’ll be in a pretty strong position to talk to company leaders about the engagement level of the company website.

There are several ways you could go about measuring interaction using various resources, but if you have the opportunity to develop widgets that can be integrated into your website and then measured, then you’re lucky. Think of widgets as containers. A widget could contain a single video and a few social sharing buttons. By measuring the actions that take place on the widget, you’ll be well on your way to engagement and you’ll have a much better picture of the content that is driving engagement.

Let’s take Time on Site again, and look at the time spent on a Web page that contains a few different widgets. Taking the metrics from the widgets and combining with the Time on Site metric, you’ll be pretty accurate in determining if someone has walked away to grab a drink or if they’re digging your site. You’ll also be able to better guess the kind of engagement. If you see unexplainable high activity on a particular widget, maybe there’s a problem.  

References:

Kaushik, A. (2010). Web analytics 2.0: The art of online accountability & science of customer centricity. Indianapolis, IN: Wiley Publishing. ISBN# 978-0470529393

Rawski, N. (2012, June 15). How to really measure engagement. Retrieved on January 28, 2013, from, http://www.imediaconnection.com/article_full.aspx?id=32065

Could Manti Te’o have prevented falling for a catfish by using Web analytics?

A sham, a hugely embarrassing public heartbreak, whatever the case with Manti’s story, there is one thing we know for certain—we are extremely vulnerable to catfishes, that is, being lured by online bottom feeders swimming in the fresh waters of social media.

A catfish is someone who pretends to be someone they're not using Facebook or other social media to create false identities, particularly to pursue deceptive online romances (Urban Dictionary, 2012).

So how do you catch these fish, or better, prevent them from swimming around your line? The answer, Web analytics. This doesn’t mean you have to understand programming code.

Primarily used in business, Web analytics is the process of analyzing the behavior of visitors to a Web site (Rouse, 2005). Web analytics will reveal abnormal online behavior and it is an essential tool for discovering the root cause of such behavior.

Web analytics can and should be used for personal use to protect your online identity and prevent you from falling victim to a catfish. By analyzing online behavior, you can identify when you’re building a relationship with a catfish early on, and most likely, before you’re fully invested. Manti had a long-term relationship with a catfish and probably never took a close look at all the conversations (traceable data) he had with his “girlfriend” online.

Catfishes will avoid in-person meetings and telephone calls, after awhile these actions become glaring signals that something’s amiss.

Lesson #1: Don’t ignore abnormal behavior; look into it.

All of the activity that is taking place online between you and another person is conveniently stored for you to analyze whenever you want. Start by look through the history of the conversations you’ve had with this person, and the conversations they’ve had with others. By viewing the conversations collectively, you’ll easily spot inconsistencies, lies, and unusual behavior.

In fact, odd behavior is what sparked exhaustive online research conducted by Deadspin reporters who broke Manti’s story. As major news media began covering the death of Manti’s girlfriend, Deadspin reporters began to research online to verify her death. They found nothing by searching Social Security Administration records. Additionally, there were no online news articles that recounted her death or the actual accident. These reporters identified unusual online behavior and looked further.

Lesson #2: Google is your best friend.

Eventually, they found the night crawlers that ultimately brought Manti’s catfish to the surface—image files. The perpetrator used photographs from a 22-year-old California woman’s social media account to create the fake social media profiles. But, it’s not the image of the woman in the photographs. It is the filename.

By taking the filename of an image (i.e., My-Celebration-Party-2008.jpg) and conducting a quick search using Google Images, Manti could have uncovered his catfish long before he began publicly expressing his sadness.

Google has proven to be the best method to hook a catfish. If you find it hard to obtain the filename, ask your new “friend” to email you a photo. Then take the filename and plug it into Google Image’s search bar. If you just can’t obtain filenames, then use the person’s first and last name to conduct a search.

Lesson #3: Don’t make it easy to become a victim.

If Manti Te’o is telling the truth, then there is a second victim—the woman who is pictured in the photos that were used to create the false identity. Can you imagine what it would be like to turn on national news and to see your photo alongside reports that the woman pictured is dead? Imagine the impact this would have on your family.

Check your privacy settings when using online sites. Social media sites can restructure privacy settings in a single upgrade exposing your online information to catfishes and other digital fraudsters. Social media networks upgrade often, so check your privacy settings often. Select the highest, most restrictive, privacy settings.

Lesson #4: Take time to inform your family and friends.

Our world has changed significantly. More people are spending more time online than ever before.

In as little as six years, Facebook has connected more than one billion users worldwide. Social media now accounts for 18% of time spend online (Fox, 2012) and for the first time, half of adults 65 and older are online. As of February 2012, one-third of Internet users age 65 and older use social networking sites such as Facebook (Zickuhr, Madden, 2012).

Ten years ago, your grandmother didn’t know what the Internet was and she certainly didn’t want to hear about it. Today, Granny is sharing photos of her winning Bingo card and giving a digital thumbs up to her grandson, Billy, who just posted a video of his teammates celebrating over pizza.  

We’re wildly connected, but there’s no manual. Take the time to stay informed and teach your loved ones how to identify abnormal behavior and protect their online identities. And, by all means, share and tweet this valuable information to friends in your social network.

References:

Urban Dictionary (2012). Catfish. Retrieved on January 24, 2013, from, http://www.urbandictionary.com/define.php?term=catfish

Rouse, M. (2005, September). Definition Web Analtyics. Retrieved on January 24, 2013, from, http://searchcrm.techtarget.com/definition/Web-analytics

Fox, Z. (2012, November, 28). This Is How Much Time You Spend on Facebook, Twitter, Tumblr. Retrieved on January 25, 2013, from, http://mashable.com/2012/11/28/social-media-time/

Zickuhr, K., Madden, M. (2012, June 6). Older Adults and Internet Use. Retrieved on January 25, 2013, from, http://www.pewinternet.org/Reports/2012/Older-adults-and-internet-use/Summary-of-findings.aspx