Beyond Targeting: Understanding Content Attractors To Improve Content Relevance
Up to now, most effort to improve the relevance of content has centered on targeting.The premise of targeting is that if the content provider can predict what content a user wants to see, it can then offer that content.Targeting involves tracking and collecting user data, a seemingly never-ending quest.
While targeting is powerful for content intended to drive a particular action, it is less helpful for recreational and general interest content, a big category of content that includes news, features, discussions, and many kinds of content marketing.
Rather than focus on predicting the user, it can be more effective to develop better data on the properties of your content and how it is valued.To improve content recommendations, we need to know what will attract different users to different content, and why.We need extend our toolset beyond topic-oriented taxonomies and conventional usage analytics.We need a structural understanding of content attractors.
To make use of content attractors, I will introduce a framework to classify content that based on content experiences, rather than content topics.I will outline how to characterize your content attractors, to improve the effectiveness of recommendations.The framework also will help content publishers understand the multiple roles an audience segment may have and understand how their content needs to address those varied needs.I will challenge participants to think hard about what makes their content distinctive, and to stop making assumptions about their audiences, and actually talk to them.
Michael Andrews, Story Needle