Audience Stability Modeling for Media Asset Ranking
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Solution Overview
Problem
Content providers face challenges in effectively monetizing advertising slots due to the variability in consumer demographics and the need for repeated exposure to promotional content to induce action, as existing systems struggle to consistently target and engage repeat viewers.
Innovation Solution
A media guidance application that analyzes viewer consistency by tracking user equipment data to identify and score media assets based on the number of consistent viewers, allowing for higher pricing of advertising slots during series with loyal viewer bases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content providers target advertisements to specific demographic groups, then advertisement targeting accuracy is improved, but advertisement effectiveness deteriorates because consumers are not repeatedly exposed to the same promotional content
Solution Approach 1:
The system tracks viewer behavior across multiple media assets and uses this feedback to identify consistent viewers. This feedback loop enables the system to distinguish between one-time viewers and repeat viewers, allowing for targeted re-exposure to the same promotional content to the same individuals, thereby improving advertisement effectiveness while maintaining demographic targeting accuracy
Solution Approach 2:
The system performs preliminary tracking and analysis of viewer behavior patterns before making advertising decisions. By pre-identifying consistent viewers through analysis of viewing history across multiple media assets, the system can proactively deliver repeated promotional content to the same individuals, ensuring advertisement effectiveness is maintained alongside targeting precision
2Quantity of substance
If content providers increase advertising slot prices based on demographic targeting, then revenue potential is improved, but advertisement action conversion deteriorates due to lack of repeated exposure to the same consumers
Solution Approach 1:
The system uses feedback from tracking viewer behavior to identify consistent viewers who are more likely to take action on promotional content. This enables content providers to prioritize repeated exposure to these specific individuals, thereby improving advertisement action conversion rates and justifying higher advertising slot prices based on demonstrated viewer consistency rather than just demographic characteristics
3Measurement precision
If systems track viewer behavior across multiple media assets, then viewer consistency identification is improved, but system complexity increases
Solution Approach 1:
The system employs a unified tracking framework that universally monitors viewer behavior across multiple types of media assets (linear programming, on-demand content, time-shifted content). This multi-functional approach consolidates tracking operations into a single system that handles diverse content types, improving viewer consistency identification while avoiding the complexity of separate tracking systems for each media type
Data Source
AI summary
Systems and methods are described for modeling the consistency of audiences viewing groups of media assets. For example, a media guidance application (e.g., executed on a server) may identify a first subset of user equipment that generated for display a first media asset (e.g., an episode of a series). The media guidance application may then identify a second subset of the first subset where the user equipment generated for display another media asset that is part of a group of media assets (e.g., another episode of the same series). Based on the number of user equipment in the second subset, the media guidance application may calculate a score for audience consistency for the group of media assets (e.g., the series) which can be used to rank the group of media assets among other groups of media assets.