Usage-Based Product Announcement Customization
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Solution Overview
Problem
The volume of product announcements for business or enterprise products is overwhelming, with most information being irrelevant to specific user usage, leading to critical updates being missed, resulting in potential loss of functionality and associated expenses.
Innovation Solution
A system that tracks the usage history of product components to prioritize and customize product announcements, filtering out irrelevant information and presenting a tailored version to users based on their specific usage patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If product announcements are provided in full volume to all users, then users receive complete information about all product changes, but users are overwhelmed by irrelevant information and may miss critical updates
Solution Approach 1:
The patent segments the complete set of product announcements into smaller, user-specific subsets based on individual usage patterns. The system divides announcements into categories (e.g., frequently used features, occasionally used features, deprecated features) and selectively presents only relevant segments to each user, thereby reducing information overload while ensuring critical updates are not missed.
Solution Approach 2:
The system performs preliminary analysis of user usage patterns before presenting announcements. By tracking and analyzing how users interact with product features in advance, the system pre-determines which announcements are most relevant to each user, allowing critical information to be prioritized and presented first, rather than requiring users to sift through all announcements.
2Loss of information
If all product announcements are presented to users, then complete information is provided, but users spend excessive time reviewing irrelevant announcements
Solution Approach 1:
The patent applies local quality by tailoring the information presentation to each user's specific needs and usage patterns. Instead of providing uniform announcements to all users, the system customizes the content, priority, and presentation format based on individual user characteristics, ensuring that each user receives high-quality, relevant information without wasting time on irrelevant content.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor user interactions with announcements and adjust future announcement delivery accordingly. By analyzing user behavior feedback (e.g., which announcements are read, which are acted upon, which are ignored), the system refines its prioritization algorithm to better predict which announcements are critical for each user, reducing review time while maintaining information completeness.
3Ease of operation
If product announcements are customized based on usage history, then relevant information is prioritized, but the system requires tracking and analyzing user behavior data
Solution Approach 1:
The patent implements self-service by having the system automatically track and analyze user usage patterns without requiring manual input or configuration from users. The tracking mechanism operates transparently in the background, collecting data on feature usage, announcement interactions, and product behavior, then automatically uses this data to personalize announcement delivery, eliminating the need for users to manually indicate their preferences or needs.
Data Source
AI summary
A computer-implemented method includes tracking usage history of a plurality of components of one or more products. An original set of announcements about the one or more products is received, where the original set of announcements includes a plurality of announcement records. The plurality of announcement records are prioritized based on the usage history of the plurality of components. A usage-based set of announcements is generated based on the prioritization of the plurality of announcement records.