Automated Directed Content Campaign Generation
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Solution Overview
Problem
Generating advertisement campaigns with satisfactory performance is impractical due to the complexity of attributes and the resource-intensive nature of manual inspection, where performance data does not effectively quantify the impact of individual attributes on campaign performance.
Innovation Solution
An automated system generates multiple variations of advertisement campaigns by altering attributes and allocates impression traffic based on performance metrics, using machine-learning models to iteratively update weights and rank campaign variants until a termination criterion is met, providing optimized campaign variants to advertisers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual inspection of advertisement attributes is performed, then advertisement performance can be optimized, but the process becomes resource-intensive and time-consuming
Solution Approach 1:
The system performs self-optimization by automatically generating multiple campaign variants, evaluating their performance, and iteratively improving them using machine learning models without requiring manual human intervention for each iteration
Solution Approach 2:
The patent replaces manual mechanical inspection processes with automated machine learning algorithms that can evaluate numerous attributes and campaign variants simultaneously, substituting human cognitive processes with computational models
2Reliability
If performance data is used to guide attribute exploration, then advertisement performance improves, but the exploration remains limited because performance data reflects group attributes rather than individual attribute effects
Solution Approach 1:
The system segments the holistic performance evaluation into individual attribute-level analyses by using machine learning models to decompose and quantify the specific impact of each attribute on campaign performance, rather than treating all attributes as a unified group
Solution Approach 2:
The patent changes the parameter of analysis from group-level performance metrics to individual attribute-level impacts by implementing machine learning models that can isolate and measure the contribution of specific attributes to overall campaign success
3Reliability
If extensive exploration of attribute space is conducted to generate numerous advertisements, then satisfactory performance can be achieved, but the process becomes costly in terms of time and human capital
Solution Approach 1:
The system performs preliminary actions by pre-generating multiple campaign variants with different attribute combinations before full deployment, allowing performance evaluation and optimization to occur in advance rather than through lengthy iterative manual processes
Solution Approach 2:
The patent transforms the time-consuming manual exploration process into an efficient automated parameter optimization process by implementing machine learning models that can rapidly evaluate and compare numerous attribute configurations simultaneously
Data Source
AI summary
Technologies are provided for automated generation of directed content campaigns. The generated campaign can be optimized for performance. In some embodiments, a group of variations of attributes that define a directed content campaign can be generated and allocated traffic weights for respective impressions of the directed content campaign in a media outlet channel. The traffic weights can then be iteratively updated until a termination criterion is satisfied. At each iteration, the traffic weight can be updated by applying a machine-learning model to current performance metric values of respective impressions corresponding to the traffic weights. After termination of the updates to the traffic weights, a particular set of variations having traffic weights exceeding a threshold can be selected as directed content campaign having satisfactory performance. Those variations can be supplied to a requestor device for subsequent utilization.