Machine Learning User Communications for Adaptive Cohort Engagement
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Solution Overview
Problem
Software applications face challenges in optimizing user engagement, leading to inefficient use of random access memory resources and high user attrition rates, particularly in health-related applications, due to suboptimal communication strategies that do not adapt to changing user preferences and behaviors.
Innovation Solution
A customer engagement platform (CEP) and data analytics platform (DAP) system that uses machine learning models to identify and optimize communication configurations for user cohorts, balancing exploration and exploitation to enhance engagement by personalizing content and delivery methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If software applications continuously send communications to users to maintain engagement, then user engagement is improved, but random access memory resources are inefficiently used and user attrition increases
Solution Approach 1:
The system performs preliminary actions by predicting user engagement likelihood and pre-determining optimal communication timing and content before actual user interaction is needed. This allows the system to proactively engage users at the most effective moments rather than continuously pinging, thereby improving engagement while reducing memory resource consumption.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user engagement metrics and adjusting communication strategies accordingly. This closed-loop approach allows the system to learn from user behavior patterns and optimize future communications, improving engagement effectiveness while reducing unnecessary memory usage from unengaged communications.
2Adaptability or versatility
If communications are sent to all users to ensure engagement, then coverage is improved, but resource consumption increases and user attrition rises
Solution Approach 1:
The system applies local quality by tailoring communication strategies to individual users or specific user segments based on their unique engagement characteristics and preferences. Rather than treating all users uniformly, the system customizes communication content, timing, and frequency for each user segment, improving engagement effectiveness while reducing overall resource consumption by targeting only those most likely to engage.
Solution Approach 2:
The system segments users into distinct cohorts or segments based on predicted engagement likelihood and behavior patterns. This segmentation allows the system to allocate resources more efficiently by applying different communication strategies to different segments, improving overall coverage effectiveness while reducing resource waste on users unlikely to engage.
3Reliability
If communications are sent frequently to maintain user engagement, then user retention is improved, but memory resource allocation becomes inefficient
Solution Approach 1:
The system implements dynamics by making communication frequency and timing adaptive rather than static. The system continuously adjusts communication strategies based on real-time user engagement signals, moving from rigid scheduled communications to dynamic, need-based communications. This improves retention by communicating at optimal moments while reducing memory resource allocation to unnecessary frequent communications.
Data Source
AI summary
Certain aspects of the present disclosure relate to methods and systems for optimized delivery of communications including content to users of a software application. The method also includes obtaining, by a customer engagement platform (CEP), a set of cohort selection criteria for identifying a user cohort to deliver the content; identifying, by a data analytics platform (DAP), the user cohort to communicate with in accordance with the set of cohort selection criteria; identifying, by the DAP, one or more communication configurations for communicating with one or more sub-groups within the user cohort; and to each user of the user cohort, transmitting one or more communications based on the content and a corresponding communication configuration for a sub-group that may include the corresponding user; and measuring engagement outcomes associated with usage of the corresponding one or more communication configurations in communication with each of the sub-groups.