Personalized Notification Channel Selection for User Engagement
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Solution Overview
Problem
Network-based services face challenges in engaging a large subscriber base effectively, as blanket marketing campaigns result in substantial bandwidth consumption and unwanted messages, with limited success.
Innovation Solution
A system and method to identify a selected set of users for notification based on engagement scores, determined by activity level, past behaviors, and likelihood of interaction, to optimize user engagement without broad-spectrum distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If blanket marketing campaigns are used to reach all subscribers, then user engagement coverage is improved, but bandwidth consumption and resource usage increase substantially
Solution Approach 1:
The patent segments the subscriber base into different groups based on their engagement levels and characteristics. Instead of treating all subscribers uniformly, the system divides them into segments such as highly engaged users, moderately engaged users, and inactive users, then applies targeted notification strategies to each segment. This segmentation allows the system to optimize resource usage by focusing efforts on the most responsive segments while reducing or eliminating notifications to less responsive segments.
Solution Approach 2:
The patent applies local quality by customizing notification content and delivery methods according to the specific characteristics of different user segments. Each segment receives notifications tailored to their preferences, behavior patterns, and engagement history. This localized approach ensures that each user receives relevant content in their preferred format and timing, thereby improving engagement effectiveness while reducing wasted bandwidth on irrelevant notifications.
2Productivity
If blanket marketing campaigns are sent to all subscribers, then notification coverage is improved, but the quality of desired content decreases due to spam perception
Solution Approach 1:
The system segments subscribers based on their engagement patterns and preferences, identifying which users are likely to find the notification valuable versus which users would perceive it as spam. By segmenting the audience, the system can selectively send notifications only to those who have demonstrated interest or engagement with similar content, thereby maintaining high notification coverage among relevant users while eliminating spam perception for others.
Solution Approach 2:
The patent changes key parameters of notification delivery including timing, frequency, content format, and channel selection based on user preferences and behavior data. By dynamically adjusting these parameters for each user segment, the system ensures notifications arrive at optimal times in preferred formats, significantly reducing the likelihood of being perceived as spam while maintaining comprehensive coverage among engaged users.
3Measurement precision
If engagement scores are calculated for all users, then notification targeting precision is improved, but computational complexity increases
Solution Approach 1:
The patent implements segmentation by dividing the user base into predefined groups based on basic engagement metrics before performing detailed engagement score calculations. This preliminary segmentation allows the system to focus computationally intensive calculations only on relevant segments rather than all users, thereby achieving high targeting precision for notification campaigns while significantly reducing overall computational complexity.
Solution Approach 2:
The system applies partial action by calculating detailed engagement scores only for a subset of users who meet certain criteria or belong to specific segments, rather than calculating scores for all users. This approach uses sufficient calculation depth to achieve accurate targeting for the relevant subset while avoiding the excessive computational burden of processing the entire user base, thus balancing precision with complexity.
Data Source
AI summary
Systems and methods for generating user notifications to a set of users of a social networking service is presented. For each user of a set of users of the social networking service, one or more machine learning models selects an optimal notification channel, an optimal notification template, and optimal personalization content for configurable elements of a selected notification template. Each of these determinations/selections is made according to and based on a likelihood of increased user engagement with the social networking service. Upon determining the notification channel, notification template, and personalizations to the template, the notification is generated and sent to the corresponding user.