iOS Ad Campaign Optimization via AI Cohort Segmentation
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Solution Overview
Problem
Existing methods for optimizing ad campaigns rely heavily on data from social media platforms, limiting their ability to optimize ad campaigns independently and in real-time.
Innovation Solution
A method that collects data from various sources, generates audience cohorts of iOS users, ranks these cohorts based on relevance and performance, and uses artificial intelligence to optimize ad campaigns by enabling or disabling ad sets based on confidence metrics and analyzing performance feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ad campaign optimization relies on social media platform data, then tracking and reporting capabilities are improved, but independence from social media platforms is lost
Solution Approach 1:
The system segments the data collection process by creating separate first-party data collection mechanisms from social media platform dependencies. It divides audience data into distinct cohorts based on first-party interactions, allowing independent tracking while maintaining measurement precision.
Solution Approach 2:
The patent introduces an intermediary layer between ad campaign optimization and data sources. This layer uses first-party data collection and AI-driven cohort analysis to mediate between the need for precise tracking and the requirement for platform independence, eliminating direct dependency on social media platforms.
2Ease of operation
If traditional ad campaign optimization methods are used, then social media platform integration is simplified, but real-time optimization capability is limited
Solution Approach 1:
The system performs preliminary actions by pre-segmenting audiences into cohorts using first-party data before campaigns launch. This pre-processing enables real-time optimization decisions during campaign execution without requiring complex real-time social media data processing, thus achieving both ease of operation and real-time capability.
Solution Approach 2:
The patent implements self-service optimization where the system automatically collects first-party data, segments audiences into cohorts, and optimizes ad campaigns using AI algorithms without requiring continuous manual intervention or complex social media platform configurations, enabling real-time autonomous optimization.
3Measurement precision
If audience data is collected from multiple sources, then targeting accuracy is improved, but data integration complexity increases
Solution Approach 1:
The patent merges multiple first-party data sources into unified audience cohorts using consistent segmentation criteria. By combining data from various touchpoints while maintaining a unified cohort structure, it achieves high targeting accuracy without proportionally increasing integration complexity, as the cohort framework provides a standardized merging mechanism.
Data Source
AI summary
The present disclosure provides a method for optimizing ad campaigns of iOS users. The method comprises collecting (201) data input from data sources, by a data module, generating (202) audience cohorts based on the data input, by an audience cohort generation module, ranking (203) the audience cohorts based on parameters, by a cohort ranking module, generating (204) clusters of ad sets based on similar targeting, by ad publishing module, assigning (205) a confidence metric to the cluster based on performance of the ad set, by the ad publishing module, enabling and/or disabling (206) ad sets based on confidence metric, by the ad publishing module, analyzing (207) performance of the ad sets and transmitting feedback of the performance to an artificial intelligence module, by a social media platform and attribution platform and optimizing (208) performance of the ad sets based on the feedback of the performance, by the artificial intelligence module.