Audio Ad CTA Selection Using Conversion Feedback
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Solution Overview
Problem
Current audio ad delivery platforms lack creative optimization, leading to suboptimal call-to-action (CTA) selection and reduced conversion rates due to the inability to tailor audio ads to individual listener preferences.
Innovation Solution
An audio ad optimization system (AAOS) uses machine learning to analyze audio ads, extract metadata, and detect conversion patterns, enabling optimized play selection and creative content adjustments based on listener behavior and context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If audio ads are played in even rotation without optimization, then all ad versions receive equal exposure, but conversion rates remain suboptimal due to inability to tailor ads to listener preferences
Solution Approach 1:
The patent implements dynamic ad selection that changes based on real-time listener context, behavior patterns, and conversion feedback. The system transitions from static even-rotation playback to dynamic selection where the 'best' ad version is chosen based on current listener state, similar to how a dynamic system adapts its properties during operation.
Solution Approach 2:
The system incorporates feedback loops where conversion results from played ads are analyzed and used to refine future ad selection decisions. The machine learning models continuously learn from conversion data to improve their predictions of which ad versions will perform best for specific listener segments, creating a self-optimizing system.
2Adaptability or versatility
If multiple CTA types are used in audio ads, then advertiser can target different user actions, but advertiser has no easy way to change CTA based on conversion results
Solution Approach 1:
The system enables self-service optimization where the ad delivery platform automatically selects and adjusts CTA types based on conversion feedback without requiring manual advertiser intervention. The machine learning models autonomously determine which CTA types perform best for different listener segments and dynamically adjust ad content accordingly.
Solution Approach 2:
The patent replaces manual CTA selection and adjustment mechanisms with automated machine learning-based decision systems. Instead of advertisers manually guessing and adjusting CTAs based on intuition, the system uses computational models that analyze conversion data and automatically optimize CTA selection, substituting mechanical/manual processes with intelligent automation.
3Productivity
If machine learning optimization is implemented for audio ads, then conversion rates improve through tailored ad selection, but system complexity increases
Solution Approach 1:
The system segments listeners into different groups based on behavior patterns, context, and conversion history. By dividing the listener population into segments, the machine learning model can apply different optimization strategies to each segment rather than attempting to optimize for all listeners uniformly, reducing the computational complexity while maintaining high conversion rates.
Solution Approach 2:
The system performs preliminary analysis of listener behavior and context before ad playback to pre-determine the optimal ad version and CTA type. By doing the optimization work in advance based on predicted listener responses, the system avoids more complex real-time decision-making during ad delivery, reducing operational complexity.
Data Source
AI summary
Embodiments of an audio advertising optimization system are disclosed to enable optimization of audio ad play selection and audio ad content creation using machine learning techniques. In embodiments, the system uses audio processing model(s) to extract metadata about audio ads that it receives from advertisers, such as speaker voice characteristics, music characteristics, and types of call-to-action (CTA) used. As the ads are played to users by ad servers, conversion results associated with the ad plays are recorded. Machine learning model(s) are built based on the ad metadata, user metadata, listening context data, and the user conversion results to learn conversion patterns of the ads. The conversion patterns may be used to optimize the play selection of ad servers to improve conversion rates. In embodiments, the conversion patterns may be made available to ad production systems, which may use the data to optimize audio ad content.