Archive for 'Engagement'

Our Engagement Metric in use at Philly.com

Those of you who have read my blog for long know that I have written a tremendous amount about measures of visitor engagement online. In addition to numerous blog posts we have published a 50 page white paper describing how to measure visitor engagement and every year I give a half-dozen presentations on the subject. Unlike some people who seem to fear new ideas and others who disapprove of anything they themselves do not create I have long been a champion for evolving our use of metrics in web analytics to satisfy business needs.

But don’t take my word for it, read about how the nice folks at Philly.com are using a near complete version of my calculation to better understand their audience.

Cool, huh?

The thing I love about this article is that Philly.com is openly talking about their use of my engagement metric.  What’s better is that their sharing prompted another super-great organization (PBS) to comment that they too have been using my engagement metric for years.

Awesome.

I have been honored to work with several companies in the past three years who have implemented my metric and variations thereof but most treat the metric as a competitive secret. Given that most are in the hard-pressed and hyper-competitive online media world I understand, but I’m certainly happy to see Philly.com and Chris Meares share their story with the world.

Anyway, check out the article and, if you’re brave, download our white paper on visitor engagement and give it a read. If you are in media and are stuck trying to figure out how to get web analytics to work for you (instead of the other way around) give me a call. I’m more than happy to discuss how our measure of engagement might be able to help your business grow.

Published on October 26, 2010 under Articles, Engagement, Presentations, Research, The Engagement Project

Our Mobile Measurement Framework is now available

Today I am really excited to announce the publication of our framework for mobile and multi-channel reporting, sponsored by OpinionLab. You can download the report freely from the OpinionLab web site in trade for your name and email address.

This paper builds on our “Truth About Mobile Analytics” paper we published with our friends at Nedstat last year and focuses on both measurement in mobile applications and, more importantly, a cross-channel measurement framework built around interactions, engagement, and consumer-generated feedback.

  • Interactions occur in every channel, digital or not. Online and on mobile sites we call these “visits” (although that is a made up word for interactions); in mobile apps the interaction starts when you click the icon and ends when you click “close”; in SMS it starts when you receive the message; on the phone it starts when you dial, and in stores interactions start when you walk up to an employee.
  • Engagement is simply “more valuable” interactions. Regardless of your particular belief about the definition of engagement, we all know it when we see it. Online it happens after some number of minutes, or clicks, or sessions, or whatever; in mobile apps it happens when you’ve clicked enough buttons; on SMS it happens when you respond to the message; on the phone it starts when you begin a conversation, and the same is true in a physical store.  We say engagement is “more valuable” because without engagement, value is unlikely to manifest.
  • Positive Feedback happens when you do a really, really good job. Measuring feedback is a critical “miss” for far too many organizations. Apples “app store” and the value of the star-rating system has essentially proven that there are massive financial differences associated with positive and negative experiences … but most companies still make the mistake of ignoring qualitative feedback altogether.

These three incredibly simple metrics can be applied to every one of your channels, your sub-channels, and  your sub-sub-channels (if you like.)  When applied you can create an apples to apples comparison between your web, mobile web, mobile apps, video, social, etc. efforts.

Then you can apply cost data, and you’re really in business.

I don’t want to say much more than that but I would really, really encourage you all to download and read this free white paper. When we put something like this out — something we believe has the power to really transform the way everyone thinks about the metrics they use to run their business, and something that has the potential to force dashboards everywhere to be scrapped and started over — we’d really like your collective feedback.

DOWNLOAD  THE WHITE PAPER NOW

Thanks to Mark, Rick, Rand, and the entire team at OpinionLab for sponsoring this work. If you’re the one person reading my blog that hasn’t seen their application in action, head on over to their site and have a look.

Published on August 4, 2010 under Engagement, Key Performance Indicators, Web Analytics Business Process, Web Analytics Demystified Business, White Papers

Measuring success in Twitter: Influence vs. Participation

I was reading a post recently outlining a somewhat incomplete attempt to measure something called “Influence” as a measure of success in Twitter. Being a champion for complicated and easily misunderstood metrics based on cognitive and behavioral psychology I was immediately drawn to the article but walked away unsatisfied … that is, until I found Twinfluence.

Twinfluence is this nifty little Twitter tool that lets you explore a Twitterer’s “influence” based on their reach (size of their network and second-level network), velocity, social capital, and centralization (see the explanation page at Twinfluence for the details behind each.) For example, here are some of the people I follow in Twitter analyzed by Twinfluence rank:

  • Rank #19: Jeremiah Owyang (jowyang) from Forrester Research
  • Rank #660: Bryan Eisenberg (thegrok) from Future Now, Inc.
  • Rank #2,893: Marshall Sponder (webmetricsguru) from Monster.com
  • Rank #3,577: Avinash Kaushik (avinashkaushik) from Google Analytics
  • Rank #6,124: Anil Batra (anilbatra) from ZeroDash1
  • Rank #7,195: Aaron Gray (agray) from WebTrends
  • Rank #7,591: Jim Sterne (jimsterne) from Emetrics
  • Rank #11,209: Omniture (omniture) from, yep, Omniture
  • Rank #11,786: Dennis Mortensen (dennismortensen) from Yahoo! Web Analytics
  • Rank #11,940: Nick Arnett (nick_arnett) a social media blogger

Whee, what fun! I could Twinfluence my friends and folks I follow all night and day if only client work, my family, and copious powdery snow didn’t get in the way. In case you were interested I have a rank of #5,754 based on my nearly 700 followers who are followed by over 375,000 other people and a very resilient social network.

However, after a little while I started thinking that measuring someone’s “influence” in Twitter was the wrong way to think about success in social media in general. Especially since people who have been dubbed “influential” and successful in the blogosphere have a tendency to think about their popularity in somewhat ridiculous ways … say perhaps stating publicly that they’re going to charge to re-tweet content because they want to buy expensive stuff?

Anyway, when I went down this path I immediately thought “Hey, the two things I spend the most time on in Twitter is trying to find great people to follow and trying to share interesting ideas.” To find great people I use Tweetdeck and to a lesser extent MrTweet to find folks who are having a conversation I’m interested in. To share interesting ideas I limit the majority of my updates to the sharing of links on web analytics related topics.

These combined efforts have helped me find and share ideas with hundreds of folks in Twitter interested in web analytics. So I started thinking “So perhaps the true measure of success in Twitter is being as good a listener as you are a source of information!” Being a balanced participant in your efforts, not just a “social media rock star” who spends all their time talking at people, not to them …

Of course this line of thinking let me to Dave Donaldson’s Twitter Follower-Friend Ratio (or the Twitter Ratio for short.) The Twitter Ratio is dead simple: the number of followers you have divided by the number of people you follow — the perfect Twitter key performance indicator! Dave even provides benchmarks against which we can be measured:

  • A ratio of less than 1.0 indicates that you are seeking knowledge (and Twitter Friends), but not getting much Twitter Love in return.
  • A ratio of around 1.0 means you are respected among your peers. Either that or you follow your Mom and she follows you.
  • A ratio of 2.0 or above shows that you are a popular person and people want to hear what you have to say. You might be a thought leader in your community.
  • A ratio 10 or higher indicates that you’re either a Rock Star in your field or you are an elitist and you cannot be bothered by Twitter’s mindless chatter. You like to hear yourself talk. Luckily others like to hear you talk, too. You may be an ass.

(The emphasis on that last sentence is mine … I laughed out loud when I read that!)

I think Dave’s Twitter Ratio of 10 or higher is the same thing as Perry Belcher’s “Twitter Snob” (funny YouTube video if you have 5 minutes.)  Perry comments that if your Twitter ratio is super high you may not be participating in “social media” but rather “solo media” — perfect!  Perry’s point is why are you even in social media if you don’t have time to listen to the conversation?

If I apply the Twitter Ratio to all of the fine folks I analyzed still ranked using their Twinfluence score here is what we get:

  • Jeremiah Owyang earns a score of 2.95 indicating that Jeremiah “may be a popular person” and “people want to hear what [Jeremiah] has to say” plus he “may be a thought leader in [his] community.” Sounds pretty much perfect to me, but I like Jeremiah.
  • Bryan Eisenberg earns a score of 1.04 indicating that Bryan is “respected among [his] peers” (or that he follows his Mom and she follows him, but with 1,951 followers we can assume the former is the best explanation)
  • Marshall Sponder earns a score of 2.30 which is pretty similar to Jeremiah’s score against his 851 followers.
  • Avinash Kaushik earns a score of 105.5 indicating that Avinash is “either a Rock Star in [his] field or an elitist [who] cannot be bothered by Twitter’s mindless chatter” who “likes to hear [himself] talk” but “luckily others like to hear [him] talk too.”
  • Anil Batra earns a score of 1.27 putting Anil in the same category with Bryan above although with only 266 followers his reach is somewhat lower than Bryan.
  • Aaron Gray earns a score of 1.49 pushing Aaron more towards Jeremiah Owyang than Bryan Eisenberg, at least on Dave’s scale.
  • Jim Sterne earns a score of 17.48 which is in the same “Rock Star” range as Avinash (although an order of magnitude less rock-starry  than Google’s own analytics evangelist)
  • Omniture earns a score of 1.26 indicating respect among the company’s 247 followers
  • Dennis Mortensen earns a score of 13.85 showing that Dennis, like Jim and Avniash, is a true web analytics rock star!
  • Nick Arnett earns a score of 0.58 which indicates that Nick is trying but alas, “not getting much Twitter love in return.”

My own score is 3.13 against 697 followers which I’m pretty happy about (especially the part about not “being an ass!”) Incidentally Perry Belcher’s Twitter Ratio is 0.98 … about as balanced as it gets!  If you have 30 seconds you can go to Dave’s site and calculate your own Twitter Ratio.

What do you think?

Is “influence” the best measure of success in social media? Or should we pay closer attention to something like the Twitter Ratio as a measure of our likelihood to actively participate in the larger conversation? It’s not hard to imagine the Twitter Ratio combined with a measure of tenure or update velocity or even something like influence to come up with a system to help us better discover which members of Twitter are providing real and substantial value to the community.

I welcome your thoughts, comments, suggestions, and perhaps more selfishly, recommendations for great and interesting people to follow and tools to help with the discovery process.

Published on December 29, 2008 under Engagement, Key Performance Indicators, Random Thoughts, Web 2.0, Web Analytics 2.0

Track Visitor Engagement using Google Analytics!

One of the major complaints about my work on measures of Visitor and Audience Engagement is that unless you have Visual Site (= Omniture on Premise), Unica, Coremetrics, SAS, or a custom data warehouse solution you’re somewhat limited in your ability to make the calculation. Now, thanks to the recent upgrade to Google Analytics and the availability of session-level segmentation everyone can use my calculation to explore engagement patterns on their site.

Yep, free measures of Visitor Engagement from Web Analytics Demystified and Google Analytics!

It was a post from Alec Cochrane about engagement that got me thinking about the application of my calculation using Google’s segmentation features, thanks Alec! Heck, had I been paying more attention to his blog I would have noticed that even Avinask Kaushik (who persists in his dogmatic assertion that “engagement cannot be measured”) refers to GA’s ability to make the calculation.

Keep in mind, what I’m describing in this post is not a full-blown measure of Visitor Engagement for a lot of reasons. Still, as I’m kicking it around it appears to be a pretty good start and per my entire approach towards measures of engagement, I’d rather have all of you banging on the idea than work in a vacuum.

So how does it work?

Step 1: Gather Your Threshold Values

The first step is to determine what thresholds you want to set for your Click-Depth, Duration, Recency, and Loyalty indices.  You can get the first two from GA’s Visitors > Overview report (shown at right) while Recency and Loyalty come from Visitors > Visitor Loyalty > Recency and Visitors > Visitor Loyalty > Loyalty respectively.

Depending on your site you may need to be creative in how you set the Loyalty and Recency thresholds, especially since GA’s reporting on these measures is not super robust. Fortunately, since the segmentation tool is pretty flexible you can play with the threshold values once you’ve set them.

Step 2: Create Your Engaged Visitors Segment

The next step is to create a segment that lets you identify “engaged” visitors on your site. I’ll first describe the basic calculation, which is essentially the same as Audience Engagement only applied to click-stream data, and then expand in a follow-up post on the idea leveraging the Interaction Index, the Brand Index, and the Feedback Index.

Start by “creating a new custom segment” and adding the visitor dimension “Page Depth” (Google Analytics’s measure of Click-Depth during the session) setting the condition to “Greater than or equal to” the Click-Depth threshold value you discovered in Step 1:

Make sure to test the segment and confirm that things are working. In the example above you can see that about 25% of the sessions to my site last May were of at least three page views. Next you’re going to add the Duration Index by adding an “and” statement and dragging in the visitor dimension for “Visit Duration” and setting the condition to “Greater than or equal to” the time on site threshold determined in step 1:

Because you’re using an “and” statement we are getting the number of sessions that were both at least three page views and at least three minutes in duration; while this is imperfect compared to the visitor by session scoring strategy we described in the longer white paper the use of “and” ensures that we’re identifying visitors who are paying Attention as measured by clicks and session duration.

The next step is to roll in the Loyalty and Recency indices using the visitor dimensions “Count of Visits” and “Days Since Last Visit”.  As I mentioned above you may need to play with the thresholds here, perhaps creating a visitor segment of goal converters (purchases, leads, etc.) and examining the return visit behavior for that segment.  Also, when you set “Days Since Last Visit” be sure to use the condition “Less than or equal to” to capture visitors who have been to the site recently:

If your site is like mine you’ll see a noticeable drop in the number of matching visits when you add “Count of Visits” or “Days Since Last Visit” because of the use of the “and” operator.  But this is good and to be expected since if everyone coming to your site was truly engaged then you wouldn’t be reading this post, you would just be rolling in money.

All you have to do now is name and save the segment and you’re in business!  I called my segment “Engaged Visitors” which is not technically correct — really what I’m tracking is “Engaged Visits” — but when you see the final application of the segment below you’ll understand why.

Step 3: Mine Google Analytics for Engaged Visitors

Once you’ve created your “Engaged Visitors” segment you can start to apply it to the various reports in Google Analytics.  I recommend comparing the engagement segment against “All Visits” to get context — and GA does something nice here in calculating the percentage of segment members (= sessions where all four engagement criteria are met) for you.  Here you can see how this comparison looks in the Visitors > Map Overlay report:

While I’m only drawing a moderately engaged audience from Australia I am feeling the love from Spain! Probably since my good friend Rene Deschamps is Spanish or perhaps since I’m talking to a web analytics consulting group in Spain about coming over for a presentation and a big Web Analytics Wednesday event this coming Spring … who knows?

Now, I am pretty delighted with how easily these segments can be applied to the various reports in Google Analytics … hell, just the fact that the segment stays applied when I navigate from report to report is nice.  And yes, there are some obvious improvements that could be made but for a first effort this is pretty nice.

The same segment can be applied to reports that are more critical to how you run you business, for example the keyword report.  When I look at three top keywords driving traffic to my site you can see a clear pattern begin to emerge (and this is without adding the Brand Index into the engagement calculation):

Here you can see an obvious difference in the level of engagement associated with external searches for my brand’s name and “Web Analytics Wednesday.”  Even searches for Judah Phillips driving traffic to my site are bringing in a highly engaged audience (Judah, since I know you’re sensitive about this, nearly 30% of the visits associated with searches for you are scoring as engaged … nice work, buddy!)

If you’re willing to keep drilling down you can learn all kinds of wonderful things.  Here is a comparison of network traffic coming from WebTrends and Omniture:

Finally, if you’re using your one user defined field to capture some type of visitor identifier (hopefully doing so in line with your privacy policy) you can actually apply the engagement segment to individuals or groups interacting with your web site and actually begin to measure true Visitor Engagement.  Here you can see my very good friends Judah and June who are highly engaged at the Web Analytics Demystified web site, shown in stark contrast to another very active visitor who does not appear to be paying me any Attention at all:

This has become a long post so I’ll stop here for now and leave you with the following summary points:

  • Google Analytics, like any session-based system, is not perfectly suited for calculating a true measure of Visitor Engagement;
  • That said, given the recent availability of segmentation in Google Analytics, I would encourage those of you running GA to explore the use of my Visitor Engagement calculation;
  • My belief is that you will begin to see for yourselves that this measure will help you identify opportunities not easily uncovered using traditional measures like average time on site and bounce rate;
  • But you don’t have to take my word for it, do you? Play with the ideas I put forth in this post and let me know what you discover.  I would absolutely love to hear what you learn using this segmentation strategy or learn about applications of the segment that I haven’t thought of yet!

Last but not least, keep in mind that I have always put forth my work on Visitor and Audience Engagement as a hypothesis, one that is still being evolved and subject to testing and application in a variety of business situations. The thing I love about our community more than anything is the willingness that most of us show to explore new ideas and have an open mind.

As always I welcome your comments and feedback.

Published on October 30, 2008 under Engagement, Vendors

Visitor Engagement + comScore = Audience Engagement!

About six months ago the management team at comScore approached me with some questions about my Visitor Engagement calculation and the Web Analytics Demystified engagement framework. Their Chief Research Officer, Josh Chasin, had taken an interest in my work and wondered how it may be extensible across multiple properties using the comScore dataset.

It was an excellent question, and today I’m happy to give readers a preview of what we believe to be an excellent answer. Today we’re announcing a measure of Visitor Engagement that, thanks to comScore, can be used to compare levels of engagement across multiple properties in a similar category.

Brand Marketing’s New Measure: Audience Engagement

Audience Engagement is a simple modification of Web Analytics Demystified’s Visitor Engagement calculation that focuses on the core site behavioral attributes, measured through the comScore panel. If you remember, the Visitor Engagement calculation is:

Σ(Ci + Di + Ri + Li + Bi + Fi + Ii)

The components of the Visitor Engagement calculation are:

  • Click Depth Index: Captures the contribution of page and event views
  • Duration Index: Captures the contribution of time spent on site
  • Recency Index: Captures the visitor’s “visit velocity”—the rate at which visitors return to the web site over time
  • Brand Index: Captures the apparent awareness of the visitor of the brand, site, or product(s)
  • Feedback Index: Captures qualitative information including propensity to solicit additional information or supply direct feedback
  • Interaction Index: Captures visitor interaction with content or functionality designed to increase level of Attention the visitor is paying to the brand, site, or product(s)
  • Loyalty Index: Captures the level of long-term interaction the visitor has with the brand, site, or product(s)

(More information about the measure of Visitor Engagement, including the details behind the calculation and several example use cases, can be obtained by reading the white paper that Joseph Carrabis and I recently published, Measuring the Immeasurable: Visitor Engagement which is freely available on this web site.)

The Audience Engagement simplifies Visitor Engagement by applying a “zero weighting” to the Brand, Feedback, and Interaction indices. By removing these values from the core calculation we are left with Click-Depth, Duration, Recency, and Loyalty:

Σ(Ci + Di + Ri + Li)

In English:

“Audience Engagement is a function of the number of clicks a visitor generates at a site, the amount of time they spent at the site, the frequency at which they return to the site, and their loyalty to the site as a member of the category for all of the sessions to that site during the reporting period.”

We’ve selected these four indices for one very simple reason: When scored using category-level thresholds (with the exception being the Loyalty Index, see below) comScore is able to automatically generate Audience Engagement values and engagement distributions across all of the sites they track.

The result is unique view into the relationship visitors have with the thousands of web sites comScore tracks around the globe. Now, for the first time ever, marketers and advertisers are able to gain insights into the level of engagement using a much more robust measure than session duration, page views, or recency alone.

Using Audience Engagement we can say with a high level of certainty that a greater percentage of Internet users find CNN more engaging than MSNBC and Yahoo! News:

More importantly we can also say that CNN has a larger population of “highly engaged” visitors to their site (22.5% of visitors at CNN versus 15% at MSNBC and less than 10% at Yahoo! News.) We believe that assessment of the audience distribution will provide advertisers an entirely new way to evaluate sites, focusing on audience quality over more simplistic measures of quantity.

This same type of analysis applied to popular network sports sites yields similarly interesting insights:

Here we can see that ESPN, while trailing Yahoo! Sports across all traditional measures (page views, sessions, minutes spent, active days) dominates Yahoo! from an Audience Engagement perspective. A closer examination of these two sites shows that ESPN’s dominance is driven largely by the frequency at which their audience members return to the site (Recency Index of 47.2% versus Yahoo! Sports at 27.0%) — an insight that has clear value to advertisers looking to create brand awareness and drive brand impressions across a sports-minded audience.

While comScore and Web Analytics Demystified are still working on how this data will be packaged and presented, another way of visualizing the relationship between two sites or a site and the category average is using a spider chart:

This chart visually tells the same story as the table above — ESPN has a higher level of Audience Engagement (bigger footprint) that is largely driven by Loyalty and Recency.

We believe that brand advertisers, advertising planners, and marketing managers will be able to use this data to make better decisions during the ad planning and media buying process. The whole debate over the definition of engagement manifest largely from advertisers desire to find more engaged audiences juxtaposed against a lack of faith in the simple measures being proposed as proxies for engagement. Thanks to comScore, these simple measures are about to become a thing of the past, giving way to a significantly more robust measure of the level of Attention audiences are paying at advertising powered sites around the world.

Interpreting Individual Data Points

In case you don’t want to spend the time reading the 50 page white paper I wrote recently on the subject with the mathematician and cultural anthropologist Joseph Carrabis, I’ll provide a brief summary of how the data comScore is reporting can be used.

Here is a sample of sites from comScore’s automotive category:

The first line in this table says that 42.8% of the audience to KBB.com is appreciably engaged with the web site. Engagement at KBB.com is largely driven by visitors clicking deeply into the site and spending an appreciable amount of time doing so, with nearly 85% of audience members exceeding the category Click Depth threshold and over 60% exceeding the duration threshold. Finally, using the distribution data, we can also see that 63% of the audience is highly engaged versus less than 3% who are only poorly engaged.

Audience Engagement data provided by comScore can also be used in a comparative context. Looking at the most and least engaging sites in this group, the data suggests that the audience going to KBB.com is over 400% more engaged than the audience going to About.com Autos (42.8% versus 8.5%.)  This is not to say that advertising at About.com Autos is a bad idea — over 90 percent of the site’s audience appears to be moderately engaged and in some instances a moderate level of engagement may be exactly what the campaign is looking for.

A Technical Note about Audience Engagement’s Loyalty Index

In the Audience Engagement calculation, the Loyalty Index is calculated differently than in the Visitor Engagement calculation because of an advantage conferred by the comScore system. Instead of simply counting the number of times a visitor has returned to the site as we’re forced to do using a site-centric data model, comScore allow us to better approximate loyalty as more commonly used: a measure of your likelihood to prefer a single site or brand over all others in the category. This model is essentially a “share of requirements” model used traditionally in the brand advertising industry and is calculated as:

Li(AE) = Visits to Site / Visits to All Sites in the Category

So, for example, if a comScore panelist is going only to eBay in comScore’s “Auctions” category, their Loyalty Index for eBay would be 100%:

Li(AE) = 10 visits to eBay / 10 visits in the “Auctions” Category

Conversely, if another visitor goes to eBay half the time and Bidz.com half the time, their Loyalty Index for eBay would be 50%:

Li(AE) = 5 visits to eBay / 10 visits in the “Auctions” Category

The result is a distribution of Loyalty Index scores for auction sites tracked by comScore in September that looks like this:

As you can see, eBay’s Audience Engagement component indices are higher than those of their competitors, but their Loyalty Index is much higher and tells us that nearly visitors in this category strongly prefer eBay to their competitors.

One of the challenges comScore and Web Analytics Demystified face regarding the Loyalty Index is the refinement of categories. Some categories like “Auctions” are well defined and represent logical competitors in a sector; others, like “News/Information” include diverse sites like Weather.com, Discovery.com, and Court TV Online. Over time we hope to refine these categories in partnership with comScore clients to provide the most accurate view of category loyalty possible. If you’re interested in participating in this work, please contact me directly.

Next Steps for comScore and Web Analytics Demystified

This is the first time we’ve been able to apply the Web Analytics Demystified Engagement construct to a syndicated audience data base.  We’re just announcing this work today, but we can already see possibilities for the measure’s evolution. Potential next-generation enhancements could include:

  • Allowing comScore clients to provide a set of branded search terms to support the inclusion of Visitor Engagement’s Brand Index (Bi)
  • Allowing comScore clients to provide a set of key site interactions designed to promote visitor Attention, supporting the inclusion of Visitor Engagement’s Interaction Index (Ii)
  • Incorporating third-party data sources measuring more qualitative aspects of the audience relationship with the site, supporting the inclusion of Visitor Engagement’s Feedback Index (Fi)
  • Allowing comScore clients to define their own competitive set in order to drill down into a more specific engagement profile in support of the advertising sales process
  • Providing comScore clients access to the details behind the Audience Engagement calculation for their site and category
  • Providing comScore clients custom access to Audience Engagement data, to provide a measure of Visitor Engagement in situations where the web analytic technology deployed does not support direct measurement

These are just a handful of examples of where this data offering can go. We’re presenting this model and starting the conversation because we want to hear from you. Regardless of whether you’re a current comScore or Web Analytics Demystified client, we would love your feedback regarding the calculation, the data, and the type of insights Audience Engagement is likely to provide to your organization.

Want to Know More about Audience Engagement?

Any reader of this blog knows that I have a passion for talking about the new measures of success on the Internet. I’m tremendously excited about this announcement and happy to talk if you’re interested in how you might be able to leverage Audience Engagement data.

Also, don’t hesitate to contact us if you have concerns about how we measure Audience Engagement or, in the extreme case, don’t think engagement can be measured at all. I firmly believe that the measures of Visitor and Audience Engagement I have proposed and the work I’ve done with Mr. Carrabis and now with comScore are only the beginning of the search for more useful measures of success on the Internet. Because these measures attempt to approximate something we agree is difficult to quantify, we believe that these measures will evolve over time; nothing is set in stone.

But we also believe that Visitor and Audience Engagement are better measures than “page views” and “average time spent” and far more useful to the measurement industry as a whole than simply sticking our head’s in the sand and exclaiming “engagement is an excuse” or worse, taking a Luddite’s view and declaring that complex measures are destined to fail.

For the time being, comScore is previewing additional details on the measure of Audience Engagement with their clients selectively.  If you’d like more information about how to be added to comScore’s list, or would like to discuss the measure of Audience Engagement with me, please email me directly and we can arrange a time to chat.

Published on October 19, 2008 under Engagement, Research, The Engagement Project, Vendors, White Papers

 


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Adam Greco, Senior Partner

One of the things customers ask me about is the ability to profile website visitors. Unfortunately, most visitors to websites are anonymous, so you don't know if they are young, old, rich, poor, etc. If you are lucky enough to have authentication or a login on your website, you may have some of this information, but for most of my clients the "known" percentage is relatively low. In this post, I'll share some things you can do to increase your visitor profiling by using advertising campaigns and other tools.

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A Primer on Cookies in Web Analytics
Josh West, Partner

Some of you may have noticed that I don't blog as much as some of my colleagues (not to mention any names, but this one, this one, or this one). The main reason is that I'm a total nerd (just ask my wife), but in a way that is different from most analytics professionals. I don't spend all day in the data - I spend all data writing code. And it's often hard to translate code into entertaining blog posts, especially for the folks that tend to spend a lot of time reading what my partners have to say.

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Excel Dropdowns Done Right
Tim Wilson, Partner

Do you used in-cell dropdowns in your spreadsheets? I used them all the time. It's both an ease-of-use and a data quality maneuver: clicking a dropdown is faster than typing a value, and it's really hard to mis-type a value when you're not actually typing!

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The Downfall of Tesco and the Omniscience of Analytics
Michele Kiss, Partner

Yesterday, an article in the Harvard Business Review provided food for thought for the analytics industry. In Tesco's Downfall Is a Warning to Data-Driven Retailers, author Michael Schrage ponders how a darling of the "analytics as a competitive advantage" stories, British retailer Tesco, failed so spectacularly - despite a wealth of data and customer insight.

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Creating Conversion Funnels via Segmentation
Adam Greco, Senior Partner

Regardless of what type of website you manage, it is bound to have some sort of conversion funnel. If you are an online retailer, your funnel may consist of people looking at products, selecting products, and then buying products. If you are a B2B company, your funnel may be higher-level like acquisition, research, trial and then form completion.

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10 Tips for Building a Dashboard in Excel
Tim Wilson, Partner

This post has an unintentionally link bait-y post title, I realize. But, I did a quick thought experiment a few weeks ago after walking a client through the structure of a dashboard I'd built for them to see if I could come up with ten discrete tips that I'd put to use when I built it. Turns out…I can!

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Exploring Optimal Post Timing ... Redux
Tim Wilson, Partner

Back in 2012, I developed an Excel worksheet that would take post-level data exported from Facebook Insights and do a little pivot tabling on it to generate some simple heat maps that would provide a visual way to explore when, for a given page, the optimal times of day and days of the week are for posting.

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What I Love: Adobe and Google Analytics*
Tim Wilson, Partner

While in Atlanta last week for ACCELERATE, I got into the age-old discussion of "Adobe Analytics vs. Google Analytics." I'm up to my elbows in both of them, and they're both gunning for each other, so this list is a lot shorter than it would have been a couple of years ago.

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Top 5 Metrics You're Measuring Incorrectly ... or Not
Eric T. Peterson, Senior Partner

Last night as I was casually perusing the days digital analytics news - yes, yes I really do that - I came across a headline and article that got my attention. While the article's title ("Top 5 Metrics You're Measuring Incorrectly") is the sort I am used to seeing in our Buzzfeed-ified world of pithy "made you click" headlines, it was the article's author that got my attention.

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Bulletproof Business Requirements
John Lovett, Senior Partner

As a digital analytics professional, you've probably been tasked with collecting business requirements for measuring a new website/app/feature/etc. This seems like a task that's easy enough, but all too often people get wrapped around the axle and fail to capture what's truly important from a business users' perspective. The result is typically a great deal of wasted time, frustrated business users, and a deep-seated distrust for analytics data.

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Welcome to Team Demystified: Nancy Koons and Elizabeth Eckels!
Eric T. Peterson, Senior Partner

I am delighted to announce that our Team Demystified business unit is continuing to expand with the addition of Nancy Koons and Elizabeth "Smalls" Eckels. Our Team Demystified efforts are exceeding all expectation and are allowing Web Analytics Demystified to provide truly world-class services to our Enterprise-class clients at an entirely new scale.

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When to Use Variables vs SAINT in Adobe Analytics
Adam Greco, Senior Partner

In one of my recent Adobe SiteCatalyst (Analytics) "Top Gun" training classes, a student asked me the following question: When should you use a variable (i.e. eVar or sProp) vs. using SAINT Classifications? This is an interesting question that comes up often, so I thought I would share my thoughts on this and my rules of thumb on the topic.

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5 Tips for #ACCELERATE Exceptionalism
Tim Wilson, Partner

Next month's ACCELERATE conference in Atlanta on September 18th will be the fifth - FIFTH!!! - one. I wish I could say I'd attended every one, but, sadly, I missed Boston due to a recent job change at the time. I was there in San Francisco in 2010, I made a day trip to Chicago in 2011, and I personally scheduled fantastic weather for Columbus in 2013.

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I've Become Aware that Awareness Is a #measure Bugaboo
Tim Wilson, Partner

A Big Question that social and digital media marketers grapple with constantly, whether they realize it or not: Is "awareness" a valid objective for marketing activity?

I've gotten into more than a few heated debates that, at their core, center around this question. Some of those debates have been with myself (those are the ones where I most need a skilled moderator!).

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Advanced Conversion Syntax Merchandising
Adam Greco, Senior Partner

As I have mentioned in the past, one of the Adobe SiteCatalyst (Analytics) topics I loathe talking about is Product Merchandising. Product Merchandising is complicated and often leaves people scratching their heads in my "Top Gun" training classes. However, many people have mentioned to me that my previous post on Product Merchandising eVars helped them a lot so I am going to continue sharing information on this topic.

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Team Demystified Update from Wendy Greco
Eric T. Peterson, Senior Partner

When Eric Peterson asked me to lead Team Demystified a year ago, I couldn't say no! Having seen how hard all of the Web Analytics Demystified partners work and that they are still not able to keep up with the demand of clients for their services, it made sense for Web Analytics Demystified to find another way to scale their services. Since the Demystified team knows all of the best people in our industry and has tons of great clients, it is not surprising that our new Team Demystified venture has taken off as quickly as it has.

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SiteCatalyst Unannounced Features
Adam Greco, Senior Partner

Lately, Adobe has been sneaking in some cool new features into the SiteCatalyst product and doing it without much fanfare. While I am sure these are buried somewhere in release notes, I thought I'd call out two of them that I really like, so you know that they are there.

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Hello. I'm a Radical Analytics Pragmatist
Tim Wilson, Partner

I was reading a post last week by one of the Big Names in web analytics…and it royally pissed me off. I started to comment and then thought, "Why pick a fight?" We've had more than enough of those for our little industry over the past few years. So I let it go.

Except I didn't let it go.

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Competitor Pricing Analysis
Adam Greco, Senior Partner

One of my newest clients is in a highly competitive business in which they sell similar products as other retailers. These days, many online retailers have a hunch that they are being "Amazon-ed," which they define as visitors finding products on their website and then going to see if they can get it cheaper/faster on Amazon.com. This client was attempting to use time spent on page as a way to tell if/when visitors were leaving their site to go price shopping.

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How to Deliver Better Recommendations: Forecast the Impact!
Michele Kiss, Partner

One of the most valuable ways to be sure your recommendations are heard is to forecast the impact of your proposal. Consider what is more likely to be heard: "I think we should do X ..." vs "I think we should do X, and with a 2% increase in conversion, that would drive a $1MM increase in revenue ..."

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ACCELERATE 2014 "Advanced Analytics Education" Classes Posted
Eric T. Peterson, Senior Partner

I am delighted to share the news that our 2014 "Advanced Analytics Education" classes have been posted and are available for registration. We expanded our offering this year and will be offering four concurrent analytics and optimization training sessions from all of the Web Analytics Demystified Partners and Senior Partners on September 16th and 17th at the Cobb Galaria in Atlanta, Georgia.

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Product Cart Addition Sequence
Adam Greco, Senior Partner

In working with a client recently, an interesting question arose around cart additions. This client wanted to know the order in which visitors were adding products to the shopping cart. Which products tended to be added first, second third, etc.? They also wanted to know which products were added after a specific product was added to the cart (i.e. if a visitor adds product A, what is the next product they tend to add?). Finally, they wondered which cart add product combinations most often lead to orders.

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7 Tips For Delivering Better Analytics Recommendations
Michele Kiss, Partner

As an analyst, your value is not just in the data you deliver, but in the insight and recommendations you can provide. But what is an analyst to do when those recommendations seem to fall on deaf ears?

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Overcoming The Analyst Curse: DON'T Show Your Math!
Michele Kiss, Partner

If I could give one piece of advice to an aspiring analyst, it would be this: Stop showing your "math". A tendency towards "TMI deliverables" is common, especially in newer analysts. However, while analysts typically do this in an attempt to demonstrate credibility ("See? I used all the right data and methods!") they do so at the expense of actually being heard.

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Making Tables of Numbers Comprehensible
Tim Wilson, Partner

I'm always amazed (read: dismayed) when I see the results of an analysis presented with a key set of the results delivered as a raw table of numbers. It is impossible to instantly comprehend a data table that has more than 3 or 4 rows and 3 or 4 columns. And, "instant comprehension" should be the goal of any presentation of information - it's the hook that gets your audience's brain wrapped around the material and ready to ponder it more deeply.

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Automating the Cleanup of Facebook Insights Exports
Tim Wilson, Partner

This post (the download, really - it's not much of a post) is about dealing with exports from Facebook Insights. If that's not something you do, skip it. Go back to Facebook and watch some cat videos. If you are in a situation where you get data about your Facebook page by exporting .csv or .xls files from the Facebook Insights web interface, then you probably sometimes think you need a 52" monitor to manage the horizontal scrolling.

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The Recent Forrester Wave on Web Analytics ... is Wrong
Eric T. Peterson, Senior Partner

Having worked as an industry analyst back in the day I still find myself interested in what the analyst community has to say about web analytics, especially when it comes to vendor evaluation. The evaluations are interesting because of the sheer amount of work that goes into them in an attempt to distill entire companies down into simple infographics, tables, and single paragraph summaries.

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Funnel Visualizations That Make Sense
Tim Wilson, Partner

Funnels, as a concept, make some sense (although someone once made a good argument that they make no sense, since, when the concept is applied by marketers, the funnel is really more a "very, very leaky funnel," which would be a worthless funnel - real-world funnels get all of a liquid from a wide opening through a smaller spout; but, let's not quibble).

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Reenergizing Your Web Analytics Program & Implementation
Adam Greco, Senior Partner

Those of you who have read my blog posts (and book) over the years, know that I have lots of opinions when it comes to web analytics, web analytics implementations and especially those using Adobe Analytics. Whenever possible, I try to impart lessons I have learned during my web analytics career so you can improve things at your organization.

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Registration for ACCELERATE 2014 is now open
Eric T. Peterson, Senior Partner

I am excited to announce that registration for ACCELERATE 2014 on September 18th in Atlanta, Georgia is now open. You can learn more about the event and our unique "Ten Tips in Twenty Minutes" format on our ACCELERATE mini-site, and we plan to have registration open for our Advanced Analytics Education pre-ACCELERATE training sessions in the coming weeks.

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Current Order Value
Adam Greco, Senior Partner

I recently had a client pose an interesting question related to their shopping cart. They wanted to know the distribution of money its visitors were bringing with them to each step of the shopping cart funnel.

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A Guide to Segment Sharing in Adobe Analytics
Tim Wilson, Partner

Over the past year, I've run into situations multiple times where I wanted an Adobe Analytics segment to be available in multiple Adobe Analytics platforms. It turns out…that's not as easy as it sounds. I actually went multiple rounds with Client Care once trying to get it figured out. And, I've found "the answer" on more than one occasion, only to later realize that that answer was a bit misguided.

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Currencies & Exchange Rates
Adam Greco, Senior Partner

If your web analytics work covers websites or apps that span different countries, there are some important aspects of Adobe SiteCatalyst (Analytics) that you must know. In this post, I will share some of the things I have learned over the years related to currencies and exchange rates in SiteCatalyst.

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Linking Authenticated Visitors Across Devices
Adam Greco, Senior Partner

In the last few years, people have become accustomed to using multiple digital devices simultaneously. While watching the recent winter Olympics, consumers might be on the Olympics website, while also using native mobile or tablet apps. As a result, some of my clients have asked me whether it is possible to link visits and paths across these devices so they can see cross-device paths and other behaviors.

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The 80/20 Rule for Analytics Teams
Eric T. Peterson, Senior Partner

I had the pleasure last week of visiting with one of Web Analytics Demystified's longest-standing and, at least from a digital analytical perspective, most successful clients. The team has grown tremendously over the years in terms of size and, more importantly, stature within the broader multi-channel business and has become one of the most productive and mature digital analytics groups that I personally am aware of across the industry.

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Ten Things You Should ALWAYS Do (or Not Do) in Excel
Tim Wilson, Partner

Last week I was surprised by the Twitter conversation a fairly innocuous vent-via-Twitter tweet started, with several people noting that they had no idea you could simple turn off the gridlines.

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Omni Man (and Team Demystified) Needs You!
Adam Greco, Senior Partner

As someone in the web analytics field, you probably hear how lucky you are due to the fact that there are always web analytics jobs available. When the rest of the country is looking for work and you get daily calls from recruiters, it isn't a bad position to be in! At Web Analytics Demystified, we have more than doubled in the past year and still cannot keep up with the demand, so I am reaching out to you ...

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A Useful Framework for Social Media "Engagements"
Tim Wilson, Partner

Whether you have a single toe dipped in the waters of social media analytics or are fully submerged and drowning, you've almost certainly grappled with "engagement." This post isn't going to answer the question "Is engagement ROI?" ...

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It's not about "Big Data", it's about the "RIGHT data"
Michele Kiss, Partner

Unless you've been living under a rock, you have heard (and perhaps grown tired) of the buzzword "big data." But in attempts to chase the "next shiny thing", companies may focus too much on "big data" rather than the "right data."

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