Visitor Engagement Continued…. by Eric
Eric Peterson just published what I think is a comprehensive view of how to develop engagement metrics. I think basically this line (from his post) sums it up;
Engagement is an estimate of the degree and depth of visitor interaction on the site against a clearly defined set of goals.
He then goes onto explain the following formula he has developed for it;
While this looks complicated it basically represents a scoring system where each letter represents the following items;
- Click-Depth Index (Ci) is the percent of sessions having more than ’¬Εn’¬ page views divided by all sessions.
- Recency Index (Ri) is the percent of sessions having more than ’¬Εn’¬ page views that occurred in the past ’¬Εn’¬ weeks divided by all sessions. The Recency Index captures recent sessions that were also deep enough to be measured in the Click-Depth Index.
- Duration Index (Di) is the percent of sessions longer than ’¬Εn’¬ minutes divided by all sessions.
- Brand Index (Bi) is the percent of sessions that either begin directly (i.e., have no referring URL) or are initiated by an external search for a ’¬Εbranded’¬ term divided by all sessions
- Feedback Index (Fi) is the percent of sessions where the visitor gave direct feedback via a Voice of Customer technology like ForeSee Results or OpinionLab divided by all session
- Interaction Index (Ii) is the percent of sessions where the visitor completed one of any specific, tracked events divided by all sessions
In addition to the session-based indices, Eric added two small, binary weighting factors based on visitor behavior:
- Loyalty Index (Li) is scored as ’¬Ε1’¬³ if the visitor has come to the site more than ’¬Εn’¬ times during the time-frame under examination (and otherwise scored ’¬Ε0’¬³)
- Subscription Index (Si) is scored as ’¬Ε1’¬³ if the visitor is a known content subscriber (i.e., subscribed to my blog) during the time-frame under examination (and otherwise scored ’¬Ε0’¬³)
If a visitor falls into the category of each index he scores a point and then the total is divided by 8 and reported as the individuals engagement index percentage. Analysts need to know that this is possible. I also see that this engagement index pretty much requires that the analytics tool requires the ability to be able to track right down to the individual. This rules out using tools like Google Analytics, at least for the moment.
However as Eric suggests, tools that can measure (at least to some degree) the individual visitor session can be used. This means as Eric suggests tools like Unica, IndexTools, Visual Sciences and WebTrends or CRM tools like Eloqua and Salesforce combined with web analytics data could help in this measurement process. I can see how this calculation for instance could easily be established as a segment in Visual Sciences, applied across all visitors so that the most valuable visitors could be found as a snapshot of all visitors and specifically catered for on site.
This is especially useful in business to business operations.
Hummmmm, interesting, but like most measurements, this engagement index will only be useful if it actually leads to key actions of influences real business decisions. I like the fact that it tries to incorporate a number of key measures, but so many mixed together can kind of water down the end result and render this somewhat useless. I guess if you can track the “impact” of certain campaigns or pricing actions or new product introductions, etc., then maybe this index could be helpful. Otherwise, it could well become another interesting index that just kind of gets replaced by the next one someone comes up with. Any examples of how it has been implemented and influenced real business decisions?