Predictive Media Analytics Engine for Budget Optimization
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Solution Overview
Problem
Current media planning platforms lack the ability to provide intelligent insights and predictive analytics for future performance metrics of ongoing media plans, limiting the ability to optimize spending and modify plans mid-execution.
Innovation Solution
A media performance platform with a predictive engine that uses data analytics to forecast cost and performance metrics by adjusting baseline parameters with seasonality, inflation, and quality inputs, allowing for scenario planning and real-time adjustments to media plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data processing and reporting methods are used for media plan analytics, then implementation simplicity is maintained, but productivity and decision-making speed deteriorate
Solution Approach 1:
The patent replaces manual mechanical data processing with an automated predictive analytics platform that uses machine learning models and algorithms to automatically ingest media plan data, generate predictions, and provide insights without human intervention in the data processing pipeline
Solution Approach 2:
The platform enables self-service analytics by automatically performing data ingestion, processing, prediction generation, and insight delivery without requiring manual intervention, allowing users to simply input media plan parameters and receive automated predictive results
2Measurement precision
If baseline cost parameters are adjusted with multiple factors (seasonality, inflation, quality), then measurement precision of predictions improves, but device complexity increases
Solution Approach 1:
The patent segments the cost adjustment process into distinct modular components: seasonality adjustment module, inflation adjustment module, and quality adjustment module. Each module independently processes one specific factor and applies its adjustment to the baseline cost parameter, making the complex multi-factor adjustment process manageable and systematic
Solution Approach 2:
The system dynamically adjusts baseline cost parameters by applying multiple adjustment factors (seasonality indices, inflation rates, quality metrics) to transform historical baseline data into predictive cost estimates that reflect current and future conditions, enabling accurate predictions despite changing economic and market parameters
3Adaptability or versatility
If predictive analytics are implemented for future performance metrics, then adaptability of media plans improves, but loss of time for data processing increases
Solution Approach 1:
The patent performs preliminary predictive analytics during media plan development and approval stages, generating predictions about future performance metrics before execution begins. This allows stakeholders to evaluate potential outcomes and make informed decisions about plan modifications in advance, rather than waiting for post-execution analysis
Solution Approach 2:
The system enables dynamic media plan optimization by continuously monitoring actual performance against predictive benchmarks during execution and automatically generating recommendations for real-time plan adjustments, allowing the media plan to adapt dynamically to changing conditions without lengthy processing delays
Data Source
AI summary
A device obtains data, for a current media plan, that includes a cost adjustment factor, a duration of an unexecuted portion of the current media plan that is divisible into periods of time, and an unutilized budget, for the duration, that is divisible into budget portions based on the periods of time. The device generates a predictive baseline cost parameter by adjusting, by the cost adjustment factor, a baseline cost parameter of a previously implemented baseline media plan. The device predicts cost metrics for the current media plan using the predictive baseline cost parameter, and predicts performance metrics for the current media plan based on the cost metrics and predictive baseline cost parameter. The device determines target cost per point (CPP) values for the current media plan based on the cost metrics and performance metrics, and causes an action to be performed based on the target CPP values.