Ad Management System for Streaming Groups
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Solution Overview
Problem
Existing advertising systems face challenges in targeting advertisements to specific individuals within a predefined group during streaming media, as they struggle to provide timely and relevant ads, leading to inefficiencies in scaling and relevance.
Innovation Solution
An ad management system that identifies close-knit groups of users sharing a concurrent streaming account, tailoring advertisements based on age profiles and user engagement, to increase relevance and interest in products or services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advertisements are targeted to specific individuals within a predefined group during streaming media, then advertisement relevance to users is improved, but system complexity and difficulty of scaling increases
Solution Approach 1:
The system segments users into close-knit groups based on shared streaming accounts, allowing targeted advertising at the group level rather than requiring individual-level tracking for each user. This segmentation enables personalized advertising approaches while simplifying the underlying system architecture.
Solution Approach 2:
The patent introduces an intermediary layer that detects user engagements within close-knit groups and uses this information to trigger targeted advertisements to other members of the same group. This intermediary mechanism bridges the gap between individual user behavior and group-level advertising without requiring direct individualized requests from each user.
2Reliability
If advertisements are individually targeted to users in real-time based on current interests, then advertisement effectiveness is improved, but timing and responsiveness deteriorates due to system load
Solution Approach 1:
The system performs preliminary actions by detecting and recording user engagements with advertisements in real-time, storing this engagement data for later use. When a user in a close-knit group engages with an advertisement, the system has already prepared the engagement information, enabling rapid dissemination to other group members without real-time processing delays.
Solution Approach 2:
The system implements a feedback mechanism where user engagement with advertisements is detected and used to trigger subsequent targeted advertisements to other users in the same close-knit group. This feedback loop creates a responsive advertising system that adapts to user interests while maintaining timing efficiency through automated group-based triggering.
3Productivity
If advertisements are tailored to individual users in a predefined group, then user interest and engagement is improved, but difficulty of detecting and measuring user characteristics increases
Solution Approach 1:
The system uses a universal approach by leveraging the shared streaming account as a common identifier for all users in a close-knit group. This universal marker enables the system to detect and measure group membership and user engagements without requiring individualized tracking mechanisms for each user's characteristics.
Solution Approach 2:
The system copies engagement detection mechanisms from individual user interactions and applies them at the group level. By detecting advertisement engagements from any user in the close-knit group and copying this engagement signal to trigger advertisements for other group members, the system simplifies characteristic detection while maintaining individualized advertising effectiveness.
Data Source
AI summary
The present disclosure is directed toward targeting advertisements to a close-knit group of users. Methods and systems of the present disclosure identify a close-knit group of users or devices based on the use of a concurrent streaming account. The methods and system further include providing an advertisement in conjunction with the content streaming to the devices of the close-knit group to increase the likelihood of a conversion. Optionally, the methods and systems tailor these advertisements based on an age segment of targeted users or based on features engaged by another user of the close-knit group.