Optimization System for Non-Linear Audience Reach
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Solution Overview
Problem
Traditional methods for creating advertisement campaigns often result in inefficient resource allocation, as they do not account for diminishing returns, leading to suboptimal allocation of resources across media assets and excessive spending to reach the intended audience.
Innovation Solution
An optimization system that recommends resource allocation based on diminishing rates of return, using graphical relationships and threshold rates to determine the most efficient amount of resources needed to reach a target audience, thereby reducing unnecessary spending and maximizing audience reach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional methods allocate resources to reach maximum audience, then audience reach is improved, but resource efficiency deteriorates due to diminishing returns
Solution Approach 1:
The system determines an optimal resource allocation that achieves sufficient audience reach without over-allocation. By identifying the point where additional resources yield diminishing returns, the system applies partial action - using just enough resources to achieve the desired effect without excess, thereby resolving the contradiction between maximizing reach and maintaining resource efficiency
Solution Approach 2:
The system dynamically adjusts resource allocation parameters based on media asset characteristics, historical performance data, and diminishing return thresholds. By changing the allocation parameters optimally for each specific context rather than using fixed traditional methods, the system achieves both improved audience reach and better resource efficiency
2Adaptability or versatility
If campaign managers manually allocate resources between media assets, then allocation flexibility is improved, but allocation optimality deteriorates due to lack of efficiency analysis
Solution Approach 1:
The system incorporates feedback loops that continuously analyze campaign performance data, audience reach metrics, and resource consumption patterns. This feedback mechanism enables the system to learn from past allocations and automatically adjust future resource distribution to optimize efficiency while maintaining the flexibility needed for different campaign objectives and media asset types
Solution Approach 2:
The system acts as an intermediary between campaign managers and resource allocation decisions. It provides data-driven recommendations that preserve manager flexibility in setting campaign goals while optimizing the actual resource distribution across media assets, combining human strategic input with computational optimization efficiency
Data Source
AI summary
Methods and systems are disclosed for an optimization system that recommends amounts of resources for use in reaching viewers during a media asset. The optimization system, using control circuitry, receives a user input of a first value of a resource and determines a first rate at which a number of unique household viewers reached during a first media asset changes for the first value. The optimization system compares the first rate to a threshold rate and determines whether the first rate equals or exceeds the threshold rate. The optimization system, in response to determining that the first rate does not equal or exceed the threshold rate, recommends a second value to the user.