How the New Engagement Metrics Can Impact Advertising Decisions

In my first two articles on engagement metrics, I discussed the need for a new approach to measuring Web data, to definining engagement, and developing a set of criteria for measuring the complexity of audience interaction. We began by looking at the "battle of engagement measurement methods" and showed that comScore and Nielsen methods fall short due to a fairly superficial approach to engagement. In part the panel approach is a technological limitation, as granular publisher data cannot be adequately analyzed. New firms, such as Quantcast, which seek to normalize publisher data through a checks and balances algorithm, offer the potential to look under the hood and describe engagement in a more insightful way.

But what is it anyway that we would hope to capture if we had the right technology and method? In my second article, I suggested that we need a definition of engagement that describes the complexity of audience interaction. We then turned to a model that Eric T. Peterson has been vetting on his blog for over a year. Peterson's approach acknowledges the complexity of engagement by looking at a set of engagement categories that supplement and complement each other.

In the last of this three-part series we will look at using this engagement model in a way that can guide online media strategies. Three questions come into play: how does this engagement model work in practice, who will produce and verify the data, and how can publishers and advertisers collaborate to yield actionable intelligence?

How it works in practice:

In my previous article we defined engagement as the nature of visitors' relationship with a site and how that is expressed in the full range of user interaction, involvement and connection. We then adapted Eric Peterson's model for indexing categories of engagement that effectively describe the kinds of engagement that would illuminate and differentiate Web sites for advertisers: loyalty, recency, click depth, interactivity, duration, and subscription. (Peterson also adds "feedback" and "brand" indices, which are, I believe, not as germane to the outbound advertising sales model). Here is a quick summary of how we can use the engagement index. Each category is indexed according to averages. The index points to the percentage of visitors exceeding the average. If we determine that the average visitor returns to health and medical content sites 4X per year, and 57% of WebMD's audience returns more than 4X per year, the WebMD Loyalty Index would be 57%. Using hypothetical examples of category averages, as we did with WebMD, let's take a look at the indices in action:

  • Click Depth (content clicked on): Percentage of visitors who exceed average page views in a given content category. If 26% of visitors exceed, say, 3 page views, C = 26%.
  • Loyalty (number of return visits over a longer period of time -- say 12 months): see WebMD example above: L = 57%.
  • Recency (number of return visits over a shorter period of time, say 1 month): If 5% of visitors return more than once a month, R = 5%.
  • Duration (time of session): If a category of content sites records a 4.6 minute average session time and 19% of a specific site's visitors spend more than 4.6 minutes, then D = 19% for that site.

The next two categories are not indexed against industry averages but are derived from the percentage of users performing specific actions:

  • Interactivity (defined actions taken with content-downloading, posting, attending a video or audiocast, etc): If 32% visitors take any one of these actions, I = 32%, during a specified time period.
  • Subscription (commitment of name and business or personal info): Measures the percentage of visitors who have given registration information. If 21% of a site's traffic can be identified by name and other submitted information, then S = 21%.

We can then also develop a Total Engagement Index by adding the values for each engagement category and dividing by 6. For the above fictitious example, then, the TE for this site would be 27%.

Would this be a useful way of measuring engagement? Comments are most welcome, of course. As I see it, the Peterson model enables us to acknowledge the complexity of engagement by showing both individual facets of the visitor relationships with a site and a metric for engagement in its entirety. Indexing allows for relational comparisons. We can compare a site to another site or see it in the context of a grouping of sites. And referring to each engagement category, as we noted last week, enables us to see the various dimensions of value that a site holds for its visitors.

If an advertiser knows that the click depth index is fairly low but that the interactivity index is relatively high, one might consider a type of advertising that plays into that strength -- say an audiocast. Or if the subscription rate is high, perhaps that means an opportunity for lead generation activity or newsletter sponsorships. For advertisers to gain the most from this kind of analysis, as I will discuss below, Web publishers will need to have the kind of deep knowledge of their visitors that will produce useful advertising insights.

Who will produce and verify the data?

"Probably not comScore or Nielsen," says Peterson. The problem is the panel approach. Projecting results from a small sample of users is increasingly controversial looking at simple metrics such as reach and composition. It will not work for engagement, as we have described it here. To get at the depth and complexity of this engagement model, we need to look at publisher data. As we discussed in the first article in this series, the startup firm Quantcast holds special appeal. Quantcast has advocated a methodology that normalizes direct publisher data through its "mass inference" algorithm. When I outlined this approach to CEO Konrad Feldman, who views the work of Eric Peterson "with the highest respect," Feldman said he believed that his company could in fact produce this kind of indexing. Are there other companies who can take the engagement model forward? I welcome all nominations.

How can publishers collaborate with advertisers to yield actionable intelligence?

"We've developed Web technology to the point where we have an astounding wealth of data about audiences. Publishers can tell us what content audiences are consuming and the share of content downloads among competing advertisers. All this has been great. But what does it all mean? How can we turn that information into something we can act on?"

Brandon Starkoff, Vice President/Global Director at Starcom Worldwide

Starkoff's point is critical to the whole point of seeking to establish a definition and a set of metrics for engagement. What does it matter if, as far as media companies are concerned, it doesn't produce better insights into what will make advertisers successful with even the most "engaged" audiences? The kind of audience knowledge Starkoff says he is seeking is "predictive intelligence-advice on what kinds of advertising will work with a particular audience or audience segment."

Advertisers right now think about engagement as a way to distinguish sites from each other. David Smith, CEO of Mediasmith, a San Francisco-based ad agency, is also a board member of Quantcast. When we looked at two of the largest sites in terms of traffic volume, Facebook and About, Smith pointed out how over 60% of Facebook's users are returning to the site more than 30 times per month, while just 2% of About's visitors come back with that kind of frequency. "That's why the entire advertising community is trying to figure out how to connect in with that level of engagemen," Smith says.

The Peterson model, I would argue, points us toward that kind of intelligence. We would be able to understand exactly what engagement means in terms of interactivity, content consumption, content generation, and loyalty. Publishers can show what kinds of content and what forms of activity make up those indices. Using analytics programs, such as WebTrends Visitor Intelligence and Score, for example, an automotive category publisher can provide detailed insights into the most engaged audience segments and show the likelihood visitors will respond to a newsletter on car audio accessories, a how-to video on hooking up MP3 players, or a Web site community blog.

As an industry, we are a country mile away from predictive intelligence. No doubt the tools are available and there are no real obstacles for individual publishers to take the entrepreneurial path of showing advertisers how to best target their audiences. I wouldn't be too surprised to hear that there are a good many taking steps in that direction. Yet there is a media industrywide opportunity to better understand engagement, establish a definition, and to agree upon standards. It is my hope that this series contributes toward that end. Please feel free to offer your ideas.

Tags: metrics
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