Driving Vertical Content Recommendations

One of the recurring patterns I’ve run into with web content, illustrated here with the upside down triangle, is the inverse relationship between traffic and value.   The areas of a site where traffic is high, value tends to be low and vice versa.  The pattern reflects a basic dynamic: high traffic generally indicates breadth of content appeal while high value generally indicates specificity of audience that advertisers are willing to pay a premium for.  A more specific audience….tends to generate lower traffic.

The inverted relationship is not invariably true; there is high traffic, high value content (and unfortunately low traffic, low value).   But it is a recurring tendency.   And the more that content narrows within a site, from a broad overall positioning to specific topic verticals, the more the triangle visual will apply.

This concept relates to another, the importance of maximizing monetization within each site visit.  In a previous post, Is Increasing Visit Frequency a Realistic Goal? I’ve laid out some public domain Quantcast data to show that the visits per visitor dimension of loyalty has less variability across sites than the pages per visit dimension.    Visits per visitor tend to fall into a more narrow range most likely because of the mind-bogglingly enormous set of possible destinations on the web.    It’s just hard to squeeze another visit per month from a site visitor; getting another page or two per visit is a lower hurdle.   To monetize most effectively you’ve got to monetize them when you’ve got them.

The logic and mathematics of recommendation algorithms are beyond me.  But based on the theories above I believe they can be key to driving increased loyalty along the path of least resistance, getting another page or two from the visitor on the occasions when he/she happens to visit.

Furthermore, recommendations that drive the visitor deeper into the triangle, into content verticals that appeal specifically to a visitor’s profile and command higher value, are worth more than recommendations that drive visitors across the top, to other broadly appealing but lower value content.   The more recommendation algorithms can achieve this goal the more effectively they can monetize for the publisher.

Along these lines, here is a link to a cool visualization from the site Online Behavior that shows how a page can be personalized for a specific visitor:

“A visitor arrives to a website from a search engine using search keywords including “health”.  Why not display a personalized version of the page based on the visitor’s interest?”

Not a recommendation algorithm per se but an example of how a specific visitor could be driven to higher value content that specifically appeals to his/her profile.