Posted by: Matt Shanahan

In my previous post, the lack of provenance for visit data was described as a big hole in web analytics tools such as Google Analytics and Yahoo! Web Analytics.  Provenance allows a publisher to enrich visit data with information from circulation, CRM, or other databases.  Provenance also enables visitor analysis to identify patterns in behavior.  These two capabilities help publishers maximize the lifetime value of visitors with improved targeting and prediction.

Improved targeting comes from visit data enrichment.  The easiest way to increase the potential pool of advertisers, is to create more targeted segments.  Visit data enrichment allows publishers to slice and dice their impression inventory in smaller chunks.  The smaller and more targeted the chunks, the more segments that can be packaged and the bigger the pool of potential advertisers.  The larger pool means increased demand for impressions and better CPM revenue.

Prediction comes from visitor analysis.  By analyzing an individual visitor, a publisher can identify impression patterns – not statistics – patterns.  Patterns from a visitor look at the historical behavior to determine frequency and reliability of impressions for each targeting parameter.  What sections does a visitor use?  Which topics in breaking headlines does the visitor respond to?  What day of the week does the visitor return?  Does the visitor come in during work or personal hours?  Understanding the frequency and reliability of impressions by targeting parameter allows a publisher to increase inventory forecasting accuracy.  Consequently, the quality of each insertion order goes up, and the level of make-good placements goes down.  With better utilization of inventory, the publisher has more inventory to sell and drive revenue.

Behavioral Analytics, Web Analytics Behavioral Analytics, Web Analytics

Posted by: Matt Shanahan

Provenance is one of the big holes in web analytics today.  Reporting and manipulating visit data  based on the source that generated the visit data isn’t exposed.  Some vendors offer expensive data mart add-ons to patch the hole, but this creates more headaches.  Consequently, the ability to understand individual visitor behavior isn’t possible with web analytics such as Omniture SiteCatalyst, Google Analytics, Webtrends Analytics, and others. 

Surprisingly, very few publishers are aware of this limitation and its impact on the revenue model.  For publishers, the lack of provenance puts real operational limits on monetizing subscribers or audience members.  I’ll cover these limitation in my next post.  This post looks mainly at the technical side.

So what is provenance?  Provenance is simply the ability to trace visit data back to the source.  In this case, a page view should be traceable back to the subscriber or audience member, device, network location, time and date that generated the page view.

What does provenance enable?  Visit data enrichment and visitor analysis are two important capabilities that come from provenance.  Data enrichment is the ability to annotate visit data with data from external systems.  An amazing amount of data regarding visitors and organizations is available in circulation databases, CRM systems, databases of other properties (e.g., vertical network), and publich databases (e.g., Hoovers, Facebook).  A page view can be enriched long after it is recorded if the provenance is in place to make the links to these other data sources.  With enriched data, a publisher has more ways to segment, analyze, and target subscribers and audience members.  Additionally, data enrichment on the back-end reduces cumbersome front-end tagging of a site.

Provenance also enables visitor analysis  Visitor analysis is the ability to look for behavioral patterns at an individual visitor level.  Visitor analysis creates new insights such as scoring visitor loyalty or understanding intent.  Visitor analysis essentially extends the segmentation and targeting schemes to become predictive.

Visit data enrichment and visitor analysis are capabilities that directly tie to monetization.  In the next post, I’ll explain the impact of provenance on targeting, prediction, and revenue.

Behavioral Analytics, Web Analytics Behavioral Analytics, Web Analytics

Posted by: Matt Shanahan

Web analytics fall short as a tool to monetize audiences.  Web analytics are designed primarily to provide statistics about the aggregate audience but not insights into individual members.  For instance, web analytics can tell you what the most popular page is for an audience, but it cannot tell you which member consumed the most pages.  To effectively sell audiences, publishers need tools that move beyond audience statistics to building individual profiles of members.

Reach and frequency are key metrics for selling audiences.  In the B2B media industry, selling audiences requires a publisher to know how many audience members belong to a specific industry (reach of a segment) as well as the frequency of visits for each member.  In B2C media, the same dynamics exist, but the segmentation parameters are different.  Armed with reach and frequency, a publisher can understand the inventory of their impressions and establish a stronger footing for negotiations regarding performance of campaigns against an audience demographic. 

Why don’t web analytics provide reach and frequency?  Because they are statistical tools and don’t describe individual members.  In audience selling, publishers are selling impressions of individuals and need prediction about individual members.  Does this member come hourly, daily, weekly?  Is the audience member a casual fly-by?  How many pages will be looked at by the member on average?  While there is always an exception to the rule, audience members are by and large predictable and behavioral analytics can be leveraged to create individual profiles that can be used to understand reach and frequency.

Publishers need to move beyond statistics to individual profiles.  It is the only way to truly understand reach and frequency, and the first step in audience selling.  The next step in audience selling is to improve the quality of targeting.  We’ll cover that in the next post.

Advertising, Audience Selling, Behavioral Analytics Advertising, Audience Selling, Behavioral Analytics

Posted by: Matt Shanahan

Okay, I’ll admit it—I’m a bit of an information junkie. I’m constantly digging for new information about the things I care about—fact, figures, and interesting new perspectives. I like to find and leverage information. At least half of the information that I use day-to-day, I get from the Internet.
 
And while I might process more information than the average person, I gather it in the standard way.  Information gathering (i.e., visits) pretty much comes in two flavors—routine and ad hoc—each offering its own opportunities to the publisher.
 
Routine visits are by definition, pattern-based. Routine visits involve specific topics and content on a regular basis. Routine visits might be what you read daily over your morning cup of coffee, what you stay updated on weekly as a part of your job, or content that you need when you close your monthly books.  It is specific, time-based usage. 
 
Ad-hoc visits are very different.  Ad-hoc visits are event driven.  The visit is for a specific reason—maybe the visit is to support a short-term project, because of a breaking story, or result from some research.
 
Routine visits are important to publishers because its usage that they can count on.  Routine visits represent the money that’s in the bank so to speak.  It’s the ad-hoc visits that provide the growth opportunity.  Ad-hoc visits represent the potential for incremental revenue over the routine by converting the visit into an action, a lead, or a routine visitor.
 
The trick for content providers is to detect the type of visit—routine or ad hoc—and then adjust the presented editorial or offer to match the interest of the visitor.  Behavioral analytics does just that—provides a lens to determine whether the visit is a one-time deal or is part of a larger pattern.  Once this is known, publishers can start serving up highly targeted offers to ad-hoc visitors that demonstrates the deeper value of the site and drives the top line.

Audience Development, Behavioral Analytics Audience Development, Behavioral Analytics

Posted by: Matt Shanahan

Publishers spend lots of time and money on complex web analytics, but they’re reporting the wrong thing – unique visitors, visits, and page views. What should be reported is visitor loyalty and what contributes to increasing loyalty.

Visitor loyalty is the basis for a business model on the web.  Whether the business model is based on ad revenue, pay-per-view, or subscription revenue, the real foundation for success is a large and loyal set of visitors (a.k.a., large and loyal audience).  To constantly improve revenues, a publisher need to understand who are the loyal visitors and what makes them loyal.  Unfortunately, the Visitor Loyalty reports in web analytics packages don’t help answer those questions, but rather report how many visits were nth visits to the site (e.g., Description of GA Visitor Loyalty Report).

The long-tail problem of visitor loyalty makes visits and page views less valuable.  Drive-bys make up the largest set of unique visitors, visits, and page views, especially if you are good at SEO, but skew all the information about your most loyal visitors.  Using web analytics reports, a publisher might decide to scrap editorial or content used by loyal visitors only because it’s not the most popular.  I am writing a more detailed article about how to report on visitor loyalty for eMedia Vitals on the topic right now.

Advertising, Behavioral Analytics, Visitor Loyalty Advertising, Behavioral Analytics, Visitor Loyalty

Posted by: Pete Horadan

I talked earlier about the insufficiencies of current tracking approaches and their impact on behavioral analytics.  Let’s now talk about how it should be done—how we do it here at Scout Analytics.  Basically, our technology is centered around a unique signature concept.  We analyze dozens of unique attributes and utilize patent-pending algorithms to derive a unique signature for the device, network, even the individual user (biometric). 

You can think of signatures sort of like a genome—while there are overlapping characteristics—each carries a unique and identifiable pattern and sequence.

Of course, this isn’t easy or everyone would be doing it.  As you can imagine, machines, networks and even people change in different environments and over time—you install a new version of Flash, or change to Silverlight.  The cast on your broken right hand changes how you use the computer.  You change network carriers.  The trick is to have algorithms that take into account enough factors and have enough intelligence to sense the change as a progression of a previous signature, rather than a different signature.

We have algorithms for recording and tracking a Devices Signature, a Network Signature and a Biometric Signature.  Each of these unique signatures works to improve the accuracy of tracking and context during analysis.  So next time I’ll give a little more insight into each.  The development of these techniques is something that truly sets us apart, and it’s what makes Demand Rating™ work.

Behavioral Analytics, Metrics Accuracy, Behavioral Analytics, Metrics

Posted by: Pete Horadan

Last week, we looked at the serious acurracy issues associated with using cookies to track visitor behavior and engagement.  This week we’ll explore the accuracy of another often-used factor, IP address. In short, IP address provides even less information about users than a cookie. 

The accuracy issue starts with the fact that one user may have many IP addresses.   For an example, let’s look at me. I access different sites (e.g., TechCrunch) from home through my personal broadband, while at my coffeehouse through their broadband, or through the network at my corporate office. When I’m on the road, I broaden my access points to include hotels and airports. When using IP address to count visitors, the information collected for me might look something like this:

IP Address                    Domain                         Location
24.16.13.198             comcast.net                Woodinville, WA  
208.54.4.23               tmobile.net                   Issaquah, WA
70.103.221.189    scoutanalytics.com              Issaquah, WA
12.204.178.67          marriott.com                  New York, NY

Of course this is incorrect. What appears to be four unique visitors, is really only me. 

The flip side is true as well, namely one IP address may have many visitors.  The common example is found when visitors behind an office firewall may all appear to come from the same address. In this case, paid-content providers have no visibility into individual visitors or their different behaviors. So, when trying to analyze unique visitor counts from IP addresses, the problem is almost intractable.  Does a given IP address represent one visitor, many visitors, or just a fraction of a visitor?

On their own, IP addresses have serious limitations in terms of establishing unique visitors or tracking behavior, seriously over- or under-estimating the true picture. Improperly used, they can lead to faulty analysis.  IP addresses are best used to provide hints which I’ll discuss more in future posts.

Behavioral Analytics, Metrics Accuracy, Behavioral Analytics, Metrics