User Cohort Classification for Assistant Content Delivery
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Solution Overview
Problem
Current automated assistant systems indiscriminately render user suggestions, wasting resources by providing content to both expert and non-expert users, and failing to tailor suggestions based on user proficiency and engagement.
Innovation Solution
Implementing a user interaction classification system that assigns users to specific cohorts based on their interaction data, allowing for tailored suggestions and resource conservation by limiting unnecessary content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If assistant suggestions are provided to all users indiscriminately, then user engagement and feature adoption may improve, but network bandwidth and processing resources are wasted on expert users who do not need suggestions
Solution Approach 1:
The system segments the user population into different cohorts based on interaction frequency and recency metrics. Users are classified as expert users (high frequency, recent activity) or non-expert users (low frequency, inactive), allowing the system to deliver suggestions selectively to non-expert users only, thereby reducing network bandwidth consumption while maintaining feature adoption effectiveness
Solution Approach 2:
The system applies different quality levels of content delivery to different user segments. Expert users receive minimal or no suggestions (low quality delivery), while non-expert users receive comprehensive suggestions with detailed explanations (high quality delivery). This local differentiation optimizes resource allocation by matching content delivery intensity to user needs
2Ease of operation
If assistant suggestions are provided to all users indiscriminately, then non-expert users may benefit from learning features, but expert users experience unnecessary content delivery
Solution Approach 1:
The system divides users into expert and non-expert cohorts using interaction data analysis. Non-expert users are identified by low interaction frequency or extended inactivity periods, while expert users show high engagement. This segmentation enables targeted content delivery that provides educational suggestions only to users who need them, reducing overall content delivery volume while improving ease of operation for the intended audience
Solution Approach 2:
The system applies partial action by delivering suggestions to only the subset of users who need them (non-experts), rather than providing excessive content to all users. This selective delivery approach reduces the quantity of substance (content volume) while maintaining adequate support for users who require it
3Loss of energy
If cohort-based classification is implemented, then resource allocation is optimized, but system complexity increases due to interaction data processing
Solution Approach 1:
The system implements self-service by automatically collecting interaction data from user sessions and performing cohort classification without manual intervention. The classification logic processes interaction frequency and recency metrics autonomously, assigning users to appropriate cohorts based on predefined thresholds. This automation reduces the need for complex manual configuration while optimizing resource allocation through data-driven user segmentation
Data Source
AI summary
Implementations set forth herein relate to assigning users to cohorts and generating assistant content for presenting to the user based on their respectively assigned cohort and to reduce user churn out. Each cohort can belong to a plurality of cohorts that vary according to the level of experience, proficiency, and/or engagement that a user has historically exhibited with respect to a particular application and/or feature. A user can be assigned to multiple cohorts in circumstances in which a user may be proficient with respect to certain features of an application but not other features. When a user is estimated to be churning out or otherwise disengaging with respect to a particular feature, assistant content associated with that particular feature can be generated and rendered at a particular time that may not distract the user and may result in further engagement with the particular feature.