The selection of content involves a strongly subjective component (i.e. Art versus Science).
Achieving the optimal mix of content is one of the most important tasks to ensure long-term ratings success.
It is difficult to consider multiple variables (+10) to explain content success using experience alone.
It is possible to predict how content will perform given a set of content attributes (i.e. High and low rating).
Provide additional inputs to the content selection and programming teams on the effect of key attributes on content performance through 2 mathematical models.
Explanatory: Identify the attributes that historically explain the rating.
Predictive: Given certain attributes, predict how certain content will perform.
By teaching the “Machine” how to identify the most important content attributes that maximize Rating (or Share) for a particular time, day or feed.
What is Metadata?
All the variables that could explain the KPI we want to maximize. For example:
Analytic strategy for explanatory model (example Decision Tree)