Linear Log Optimization for Cross-Platform Ad Pacing
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Solution Overview
Problem
Traditional linear and digital supplemental content campaigns are executed separately, leading to inefficiencies in cross-platform optimization, unpredictable pricing and pacing, and excessive maintenance costs, especially when targeting specific demographics, and there is a need for flexible cross-platform forecasting and ad placement.
Innovation Solution
A cross-platform decisioning engine employs pre-flight and in-flight optimization to pace and place supplemental content across linear and digital streaming endpoints, using a budget split technique for capacity balancing and a linear log optimizer for supplemental content pacing and placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional separate execution of linear and digital supplemental content campaigns is maintained, then campaign execution simplicity is preserved, but cross-platform optimization efficiency deteriorates
Solution Approach 1:
The patent combines separate linear and digital supplemental content campaigns into a unified cross-platform campaign structure. The system integrates linear broadcasting infrastructure with digital streaming platforms, allowing supplemental content to be placed across both platforms simultaneously under a single campaign framework. This merging enables shared audience targeting, consolidated optimization algorithms, and unified performance measurement, directly improving cross-platform optimization efficiency while accepting increased system complexity as a necessary trade-off.
Solution Approach 2:
The patent creates a universal campaign execution system that can operate across multiple platform types (linear and digital) with different technical characteristics. The supplemental content placement system is designed to function universally across platforms with varying audience sizes, content slot structures, and delivery mechanisms. This multi-functionality allows the same optimization engine to efficiently allocate impressions across diverse platforms, resolving the contradiction between execution simplicity and optimization efficiency.
2Reliability
If traditional separate campaign execution is used, then system complexity is reduced, but impression guarantee reliability deteriorates
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors actual impression delivery across linear and digital platforms, compares it against guaranteed targets, and dynamically adjusts placement strategies. The optimization engine receives real-time data on audience availability, content slot utilization, and impression delivery status, then recalculates allocation to ensure guarantees are met. This closed-loop feedback system significantly improves impression guarantee reliability by detecting and correcting deviations before they result in non-compliance.
Solution Approach 2:
The system performs preliminary forecasting and capacity planning before campaign execution begins. It pre-calculates the distribution of guaranteed impressions across linear and digital platforms based on historical audience data, forecasted viewership, and platform capacity. This advance planning allows the system to proactively allocate impressions to ensure guarantees are met, rather than reacting to shortfalls after they occur. The preliminary action includes setting aside reserved capacity on each platform to buffer against variability in actual delivery.
3Adaptability or versatility
If traditional linear forecasting with fixed content slots is used, then content placement predictability is improved, but cross-platform flexibility deteriorates
Solution Approach 1:
The patent transitions from static, pre-scheduled content slot placement to dynamic, real-time optimization. The system continuously adjusts supplemental content placement decisions based on current audience availability, platform performance, and campaign objectives. Instead of fixing placements in advance based on predetermined slots, the optimization engine makes dynamic allocation decisions that adapt to changing conditions across linear and digital platforms. This dynamic approach maximizes cross-platform flexibility while using predictive algorithms to maintain placement predictability through probabilistic forecasting.
Solution Approach 2:
The system changes key parameters from fixed values to variable ranges. Instead of treating content slot positions, timing, and platform allocation as fixed parameters, the optimization engine treats them as variable parameters with probability distributions. It uses forecasted audience availability and platform capacity as variable inputs to determine optimal placement parameters. This parameter transformation allows the system to adapt to different platform characteristics and conditions while maintaining predictable outcomes through statistical modeling and optimization algorithms.
4Measurement precision
If supplemental content is targeted to specific demographic segments, then audience targeting precision is improved, but campaign execution complexity deteriorates
Solution Approach 1:
The patent divides the overall audience into distinct demographic segments (e.g., age groups, geographic regions, viewing behavior clusters) and executes separate optimization processes for each segment. The system creates segment-specific placement strategies that target supplemental content to the appropriate audience groups across linear and digital platforms. This segmentation approach improves targeting precision by tailoring content delivery to specific demographic characteristics while managing complexity through modular, segment-by-segment optimization rather than attempting to optimize all audiences simultaneously.
Data Source
AI summary
Systems and methods for efficient and reliable supplemental content scheduling within primary content of a linear platform are provided. Specifically, a placement context is used to identify a suitable placement model from a plurality of placement models. The scheduling of future placements of the supplemental content via the linear platform is determined by applying historical linear impressions to the suitable placement model.