Media Plan Engine Balancing Historical and Predictive TV Ad Buying
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Solution Overview
Problem
The planning of advertising for linear and non-linear television is complex due to the specialized domain knowledge required for effective purchasing of remnant advertising slots, especially in the context of content delivery networks, making it difficult for entities to ascertain effective channels and time slots for advertising.
Innovation Solution
A media plan engine that automatically generates optimized media plans for linear and non-linear TV advertising, using a core plan based on historical data and a test plan based on predictive algorithms, balancing proven effective advertising with predictive metrics to allow entities to easily A/B test their strategies within their risk profile and budget.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual planning of remnant advertising slots is used, then specialized domain knowledge can be applied, but the complexity of determining effective channels and time slots increases significantly
Solution Approach 1:
The system enables automatic generation of media plans through the media plan engine that autonomously processes spot airing data, historical data, and predictive metrics to generate optimized advertising schedules without requiring manual intervention or specialized domain knowledge from users
Solution Approach 2:
The patent replaces manual mechanical planning processes with an automated computational system that uses machine learning models, historical data analysis, and predictive algorithms to determine optimal advertising placements, substituting human expertise with algorithmic decision-making
2Reliability
If historical data only is used for media planning, then proven effective advertising can be selected, but the ability to discover new effective advertising options is limited
Solution Approach 1:
The system performs preliminary testing of predictive models on historical data before deployment, and generates test media plans that allow advertisers to experiment with new strategies in a controlled manner before full implementation, enabling safe exploration of novel advertising approaches
Solution Approach 2:
The media plan engine dynamically balances between historical performance data and predictive metrics, allowing the system to adaptively adjust the weight given to proven effective advertising versus predictive opportunities, enabling flexibility in strategy selection based on risk tolerance and objectives
3Productivity
If automated media plan generation is implemented, then efficiency is improved, but the requirement for specialized domain knowledge and data processing increases
Solution Approach 1:
The media plan engine is designed as a universal system that handles multiple data types (spot airing data, historical performance data, predictive metrics), generates different types of media plans (core plans, test plans), and serves multiple advertising objectives through a single integrated platform
Solution Approach 2:
The system introduces a media plan engine as an intermediary layer between raw data sources and final advertising decisions, which processes and synthesizes complex data from multiple sources into actionable media plans, shielding users from the underlying data processing complexity
Data Source
AI summary
Media plan generation systems and methods for TV advertising are disclosed. Embodiments of these systems and methods are adapted to generate a media plan comprising a core plan and a test plan. The core plan may be produced based on historical models regarding historical performance of an entity's media while the test plan may be produced based on a predictive model of media performance.