Feature Promotion via Visual Callouts
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Solution Overview
Problem
Users often overlook features in applications due to unawareness or familiarity with existing methods, leading to underutilization of productivity-enhancing and enjoyment-increasing features.
Innovation Solution
A computing apparatus and method that promotes features based on popularity across different user groups by using visual callouts to direct attention to unpopular yet popular features among other users, determining feature popularity through usage data and thresholds, and displaying alerts on user devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users are provided with many features in an application, then the application functionality and productivity are improved, but users become overwhelmed and overlook many features due to unawareness or familiarity with existing methods
Solution Approach 1:
The system implements feedback by monitoring feature usage across multiple user groups and using this usage data to dynamically promote features. The feedback loop collects information about which features are popular among certain user groups and uses this information to target promotions to users who have not yet adopted those features, thereby increasing awareness without overwhelming users with all features simultaneously.
Solution Approach 2:
The system applies local quality by providing customized feature promotions to different user groups based on their specific characteristics and usage patterns. Instead of uniformly promoting all features to all users, the system identifies which features are relevant to which user groups and promotes only those features to the appropriate audiences, making the information more manageable and relevant for each user.
2Loss of information
If visual callouts are displayed to promote features to all users, then feature awareness is improved, but user attention is分散 and the callouts become less effective
Solution Approach 1:
The system applies local quality by tailoring visual callouts to specific user groups based on their characteristics and usage patterns. Each user group receives customized promotions for features that are most relevant to them, rather than receiving generic promotions for all features. This targeted approach makes the callouts more effective by presenting only the most relevant information to each user group.
Solution Approach 2:
The system segments users into different user groups based on their characteristics and behavior patterns, and then promotes features specifically to each segment. This segmentation allows the system to manage user attention efficiently by presenting feature promotions in a structured, organized manner rather than overwhelming all users with all possible promotions simultaneously.
3Measurement precision
If feature usage data is collected and analyzed across multiple user groups, then feature popularity determination is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system applies local quality by analyzing usage data separately for different user groups rather than aggregating all data into a single analysis. This allows the system to determine feature popularity within the context of specific user groups, providing more precise and meaningful insights. The localized analysis reduces the complexity of processing by breaking down the large-scale data problem into smaller, more manageable group-specific analyses.
Data Source
AI summary
According to an example, usage of a plurality of features of an application by users in each of a plurality of tenants may be accessed. Popularities of the plurality of features among the users in each of the plurality of tenants may be determined and a feature of the plurality of features to be promoted to the users in the first tenant may be identified based upon the determined popularities of the features. Additionally, a visual callout to the identified feature may be caused to be displayed to the users in the first tenant to promote the identified feature to the users in the first tenant.