Content Optimization

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).

Key Objectives:

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:

Objective Metadata

Production House

Number of episodes

Cast, ethnicity

Accent

Screenwriter

Broadcast time

Premiere date

Subjective Metadata

Subjective opinions

Romantic, Dramatic, Sexual, Religious.

Analytic strategy for explanatory model (example Decision Tree)

We seek to determine:

•The most important characteristics of a production in determining the level of success.

•The trail of attributes that lead a production to be {good, average or bad}.

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Analytic strategy for predictive model

 

•We seek to determine if the proposed content will reflect a high or low rating through a classification model for a feed.

•Likelihood

    - High

    - Low

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