Audience Delivery Optimization via Monte Carlo Simulation
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Solution Overview
Problem
Content providers underutilize their advertising inventory by not optimally scheduling advertising content segments, leading to missed revenue opportunities and liabilities due to complex scheduling constraints and vast combinations of possible slot placements.
Innovation Solution
An audience delivery optimization system that determines permissible advertisement slot swaps using initial pruning criteria, evaluates lift scores, and applies Monte Carlo simulations to optimize advertisement log placement, ensuring net gains and desirable swaps that align with performance goals of advertisement campaigns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual scheduling methods are used for advertisement slots, then scheduling simplicity is maintained, but revenue optimization and inventory utilization are insufficient
Solution Approach 1:
The patent segments the advertisement scheduling problem into distinct components: initial pruning of infeasible slots based on basic constraints, lift score calculation for potential swaps, and Monte Carlo simulation for optimization. This segmentation allows the system to handle complexity in manageable stages while achieving revenue optimization.
Solution Approach 2:
The system performs preliminary actions by pre-calculating permissible swaps using initial pruning criteria before the actual optimization process. This preliminary filtering eliminates infeasible options early, reducing the search space for subsequent Monte Carlo simulations and improving overall computational efficiency.
2Manufacturing precision
If exhaustive evaluation of all possible slot placements is performed, then optimal scheduling is achieved, but computational time and resources are excessive
Solution Approach 1:
The patent extracts and evaluates only the most promising swaps by calculating lift scores for permissible swaps and using Monte Carlo simulation to identify optimal configurations. This selective approach extracts the essential optimization needed without exhaustively evaluating all possible slot placements, significantly reducing computational time while maintaining optimization accuracy.
Solution Approach 2:
The system changes parameters dynamically during optimization by adjusting swap evaluations based on lift scores and simulation results. This allows the system to focus computational resources on high-impact changes rather than uniformly evaluating all possible parameter combinations, achieving precision efficiently.
3Productivity
If advertisement slots are filled without optimization, then scheduling simplicity is maintained, but revenue lift and inventory utilization are suboptimal
Solution Approach 1:
The patent implements a dynamic optimization system that adaptively evaluates swaps based on lift scores and simulates multiple scenarios using Monte Carlo methods. This dynamic approach allows the system to respond to changing conditions and maximize revenue lift while managing complexity through structured evaluation criteria.
Solution Approach 2:
The system incorporates feedback mechanisms by evaluating the impact of potential swaps on campaign performance and using simulation results to guide subsequent optimization decisions. This feedback loop ensures that each scheduling decision contributes to overall revenue optimization while maintaining system manageability.
Data Source
AI summary
Systems, methods and articles for providing optimized scheduling of a log of spots to be delivered to consumers. The optimized scheduling allows content providers to autonomously satisfy contracts and increase revenue. An audience delivery optimizer system receives an initial log, generates an optimized log, and returns the optimized log so that content can be delivered to consumers according to the optimized log. The audience delivery optimizer system may use historical ratings data and may implement an algorithm to accurately project future delivery. The audience delivery optimizer system may evaluate and optimize a log that spans a particular period of time, such as a day, a week, a month, etc. The audience delivery optimizer system may evaluate over-performing contracts and under-performing contracts and may then optimize the placement of spots based on such evaluations. The audience delivery optimizer system may track liability or other metrics over determined periods (e.g., quarterly).