Publishing Audience Segmentation by Interests & Topic Affinities
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An Analytics tool can auto-tags stories with topics and measures quality of engagement, and provide much more reliable Audience Interest based segmentation using sophisticated algorithms in Machine Learning.
Benefits for the company
For editorial and audience development teams, the ability to clusterize and view audiences by their topic affinities which is currently done using a mix of demographic indicators such as age, sex, location and session attributes such as Facebook audience, repeat user, visited politics category, isn’t very reliable and time consuming.
Feasability
High
Type of expertise/ AI domain
Text Mining, Classification, Clustering
Internal data required
Audience Interests, interaction, Click Report, Content Consumption, Audience Segments
This segmentation can be used in the following ways:
– For prioritization of news-worthy topics
– Understand overlapping topic affinities to improve reader engagement
– Identify similar audiences and the traffic sources that work best to identify high-engagement readers
– Digital marketers can compare campaign results by engagement, instead of just traffic volumes