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Σ(Ci + Di + Ri + Li) = Audience ENGAGEMENT

or

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.

source: webanalyticsdemystified

This week Eric Peterson, working together with comScore, announced (maybe released would be more appropriate) a new metric.

After months of work (that included collaboration with comScore’s Chief Research Officer Josh Chasin) they explain the calculations and showed resultant values for some well known sites.

The components of Audience Engagment formula are:

• Ci, Click Depth Index: Captures the contribution of page and event views
• Di, Duration Index: Captures the contribution of time spent on site
• Ri, Recency Index: Captures the visitor’s “visit velocity”—the rate at which visitors return to the web site over time
• Li, Loyalty Index: Captures the level of long-term interaction the visitor has with the brand, site, or product(s), in the case of comScore which has aggregated knowledge of the visitors activity this parameter is based in a share-of-visits figure.

These are a subset of the original Web Analytics Demystified’s Visitor Engagement calculation published during September.

Σ(Ci + Di + Ri + Li + Bi + Fi + Ii) = Visitor ENGAGEMENT

including additional terms:

• Bi, Brand Index: Captures the apparent awareness of the visitor of the brand, site, or product(s)
• Fi, Feedback Index: Captures qualitative information including propensity to solicit additional information or supply direct feedback
• Ii, 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)

 The details on how each of this terms can be calculated can be found in this white paper. But so you can make an idea of how it works I will explain the first, Ci, Click Depth Index.

You take all visits that viewed (or clicked/triggered events) more that a certain threshold and divide them by the total number of visits and that’s it. To be honest not all of them are that straightforward and things can get messy with some of them. The advice from the co-writer, mathematician and cultural anthropologist Joseph Carrabis, is not to mix your units and be very careful with the time frames you use to calculate each term.

Now we can jump into the really fun part, taking a peek at the results for one of their sites categories, News.

We have 2 different things here, on the left hand the individual terms and the averaged Audience Engagement Index, on the right a distribution of highly and poorly engaged audience (visitors or visits?, the original post says visitors).

I really find this engagement distribution breakdown awesome. Unfortunately it requires web analytics tool with advanced segmentation capabilities in order to define criteria that describes "highly engaged" and "poorly engaged" traffic.

Back to the engagement scores. Although I agree that the average can be the simplest method to aggregate the terms both to compute AE and to be understood by media buyers. I don’t  think this is will be the best way to go in most of the Visitor Engagement for each site.

Maybe be using the minimum value is an alternative or grouping terms that are related and applying an operator and the aggregate again on the results.

I will keep thinking about this and I’ll post again if something comes to me.

 LAST MINUTE UPDATE: WITH THE NEWLY ANNOUNCED CUSTOM SEGMENTS FEATURE IN GOOGLE ANALYTIC YOU CAN PROBABLY CALCULATE HIGHLY AND POORLY ENGAGED AUDIENCE SEGMENTS!