Linear Reach Forecaster for Predictive Content Schedule Optimization
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Solution Overview
Problem
Existing linear content delivery mechanisms are reactive and lack predictive capabilities, leading to suboptimal utilization of advertising resources and difficulty in estimating the number of households that will be reached before content delivery.
Innovation Solution
The use of machine learning models to predict the effectiveness of a proposed linear content schedule, optimizing the delivery of linear content by maximizing the reach of households, and updating the schedule based on predicted effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional linear content delivery mechanisms are used, then the system is simple to operate, but the system lacks predictive capabilities and leads to suboptimal utilization of advertising resources
Solution Approach 1:
The system performs preliminary actions by predicting content schedule effectiveness before actual delivery using machine learning models. The linear reach forecaster estimates reach metrics and delivers predictions in advance, allowing optimization of content schedules before resources are committed, thus resolving the contradiction between adding predictive capability and maintaining system simplicity.
2Productivity
If machine learning models are introduced to predict content schedule effectiveness, then the reach and effectiveness of content delivery is improved, but the device complexity increases
Solution Approach 1:
The patent introduces a linear reach forecaster as an intermediary component that sits between the content scheduling system and the delivery mechanism. This forecaster uses machine learning models to predict effectiveness metrics, enabling improved content delivery productivity while isolating the complexity of ML models within a dedicated module, thus managing overall system complexity.
3Quantity of substance
If the number of delivery slots is increased to achieve target impressions, then the reach of content delivery is improved, but the loss of time for scheduling and optimization increases
Solution Approach 1:
The system performs preliminary prediction of reach metrics and optimal slot allocation before finalizing the content schedule. By using machine learning models to forecast effectiveness, the system can determine the required number of delivery slots in advance, reducing iterative scheduling time and enabling faster deployment of content schedules that meet target impression goals.
Data Source
AI summary
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for determining an number of slots for achieving a desired reach specified in a media content delivery schedule. The system, apparatus, article of manufacture, method, and/or computer program product aspects is designed with a simulation framework tuned to predict an estimate the needed number of slots based on a reach specified in the delivery schedule.