Video Ad Targeting via User Interaction Data
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Solution Overview
Problem
Conventional in-stream video advertising lacks targeting precision, failing to ensure that advertisements reach the intended audience and does not provide users with control over the ads they watch, leading to a suboptimal viewing experience for both advertisers and users.
Innovation Solution
A computer-implemented method that generates a user interface for displaying videos with controllable advertisement slots, maps videos to categories, and associates targeted advertisements with the content, allowing user interaction data to influence ad placement and bidding, ensuring ads are relevant and engaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional in-stream video advertising is used, then advertisers can reach users through video content, but the targeting precision is poor and advertisements do not reach the intended audience
Solution Approach 1:
The system performs preliminary actions by collecting user interaction data (clicks, views, skips) and video category information before ad selection. This advance preparation enables precise targeting when ads are delivered, resolving the contradiction between reaching users and ensuring relevance.
Solution Approach 2:
The system implements feedback loops where user interactions with ads (clicks, views, skips) are continuously collected and used to refine future ad selections. This feedback mechanism improves targeting precision over time while maintaining audience relevance.
2Ease of operation
If conventional pre-programmed advertisement viewing is used, then advertisers can deliver ads through video streams, but users lack control over the ads they watch
Solution Approach 1:
The system enables self-service by allowing users to express preferences through interactions (skipping ads, clicking links) which automatically influence future ad selections. Users effectively control their own ad experience without direct intervention, resolving the contradiction between user control and system complexity.
3Reliability
If in-stream video advertisements are delivered without user interaction data, then ads can be displayed quickly, but ad effectiveness and user satisfaction are suboptimal
Solution Approach 1:
The system performs preliminary data collection during video playback, gathering user interaction information in advance of ad selection decisions. This allows ads to be selected with high effectiveness while minimizing the time delay between interaction and ad delivery.
Solution Approach 2:
The system maintains continuous data collection during video playback, ensuring that ad effectiveness is improved through ongoing user interaction monitoring without interrupting the viewing experience or causing significant delays.
Data Source
AI summary
At a client, a video is received. The video includes one or more advertisement slots. The video is played back to a user. During the playback of the video, an impending advertisement slot is detected. One or more advertisements are requested for placement in the advertisement slot. The one or more advertisements are received and placed in the advertisement slot.