Non-linear Reach Optimization for Advertising Campaigns
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Solution Overview
Problem
Traditional reach optimization systems rely on simplistic linear models, leading to inaccurate data that hinders users' ability to make informed decisions about advertising placements to maximize audience reach.
Innovation Solution
The implementation of non-linear reach optimization methods, which involve retrieving available media spots, computing probabilities of user access, determining demographic weights, and calculating reach values using non-linear functions to identify optimal advertising opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional linear models are used for reach optimization, then the system complexity is low and ease of operation is high, but the measurement precision of reach values is inaccurate
Solution Approach 1:
The patent transforms the linear reach calculation model into a non-linear probabilistic model by introducing probability parameters and non-linear functions. This allows the system to capture complex user behavior patterns and media consumption dynamics that linear models cannot represent, thereby improving reach value accuracy despite increased computational complexity
Solution Approach 2:
The patent introduces probability as an intermediary concept between user characteristics and reach values. By computing probabilities of user exposure to media content based on demographic weights and consumption patterns, the system achieves more accurate reach measurements without directly modeling every complex interaction
2Measurement precision
If non-linear models are used for reach optimization, then the measurement precision of reach values is improved, but the device complexity increases
Solution Approach 1:
The patent implements automated computation of probabilities and reach values through the non-linear model, eliminating the need for manual adjustments or complex user configurations. The system self-adjusts to user behaviors and media patterns, maintaining ease of operation despite the sophisticated underlying mathematics
3Productivity
If non-linear reach optimization is implemented, then the productivity of advertising decision-making is improved, but the loss of time for computation increases
Solution Approach 1:
The patent pre-computes probability values and demographic weights based on historical user data and media consumption patterns. By preparing these probabilistic parameters in advance, the system enables rapid reach calculations when advertisers need to make decisions, reducing real-time computational burden while maintaining high accuracy
Data Source
AI summary
Methods and systems for performing non-linear optimization of reach are described herein. The methods and systems may be used to compute the reach associated with different advertisement campaigns involving different combinations of spots. The method includes computing probabilities that a user will access a certain spot. The method includes retrieving a weight associated with the targeted user demographic. The method includes computing a first reach value using a first non-linear function, and a second reach value using a second non-linear function. The method includes comparing the two reach values to each other and to a predetermined reach threshold. The higher reach value larger than the predetermined reach threshold is selected and information associated with the selected reach value is provided to the user.