Analyzing the front page content recommendations that came out of the Visual Revenue Platform, I found that Gossip and Entertainment stories do not always rank the highest. News and Sports stories, as an example are extremely valuable, and easily assure that Lindsay Lohan doesn’t always win!

Some editors will use the Click-through Rate (CTR) of an Article Box* on the front page as a gauge for whether a piece of content deserves the exposure it’s being given. CTR is certainly better than not using any data at all, and surely more optimal than looking at how many readers are viewing my Article right now. I would like, however, to introduce two problems that discount CTR as the only gauge for success – whilst adding to the complexity of our data modeling, it’s also something that we’ve solved when predicting which Article to place where on the front page. Let me elaborate.

Any decent Web Analytics tool enables you to assign each page on your website a unique document name for the purposes of reporting. This is an override function and you are usually not forced to use it, as if you do not assign unique names to each page, the tool tend to apply the HTML title tag as the name of the page viewed – and with that an opportunity to report on this grouped entity. But what do you do when the Title changes continuously ?

Maximizing performance by setting the perfect front page 150 times a day is a nigh impossible task without a little hand holding from technology :-) – and it seems like an unfair ask of the front page editor without empowering her with any predictive support

We recently finished more than 150 hours of real-time “real-world’ recommendations with our Beta Publishers, so it is most likely you’ve read – without knowing it – the front page of a news media destination somewhere in the world that was set based on our content recommendation technology! The outcome, a phenomenal increase in Front Page CTR

If you want to provide recommendations on how well content is going to perform in the future, you have to accept that you have very little data to model on initially and that the recommendation itself expires (decrease in value) rapidly. Knowing which Article should have been pushed on the Front Page of the New York Times Yesterday has little or no value today!