User Engagement System Using Profile-Based Segmentation
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Solution Overview
Problem
E-mail marketing campaigns often fail to target users intelligently, resulting in poor click-through rates due to the lack of personalized engagement messages based on user activity and behavior.
Innovation Solution
A machine-implemented method that determines user engagement by analyzing user profiles, ranks engagement types based on previous interactions, selects optimal engagement types, generates customized messages, and transmits them through various communication channels, such as email or push notifications, to provide a tailored experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If mass e-mails are sent to users, then the coverage of marketing campaigns is improved, but the click-through rate deteriorates due to lack of intelligent targeting
Solution Approach 1:
The patent segments users into different groups based on their engagement history, activity levels, and preferences. Instead of sending uniform mass e-mails to all users, the system divides the user base into segments (e.g., active users, inactive users, high-value users) and sends targeted engagement messages to each segment, thereby improving click-through rates while maintaining broad coverage
Solution Approach 2:
The patent applies local quality by customizing engagement messages according to individual user characteristics, preferences, and behavior patterns. Each user receives a tailored message with content, timing, and channel optimized for their specific profile, rather than a generic mass e-mail approach
2Reliability
If personalized engagement messages are created for each user, then the click-through rate is improved, but the complexity of the system deteriorates
Solution Approach 1:
The patent implements preliminary action by pre-calculating and storing user profiles, engagement histories, and message templates in advance. The system prepares engagement strategies beforehand based on user behavior patterns, so that when a message needs to be sent, the personalization can be quickly applied without complex real-time processing
Solution Approach 2:
The patent uses copying by creating templates for engagement messages that can be replicated and customized for different users. Instead of manually crafting unique messages for each user, the system uses template copies with dynamic fields that are automatically filled based on user data, reducing the complexity of message creation
3Productivity
If automated engagement scheduling is implemented, then the productivity of marketing campaigns is improved, but the need for human-defined campaigns deteriorates
Solution Approach 1:
The patent applies self-service by enabling the system to automatically generate, schedule, and optimize engagement campaigns without human intervention. The system uses machine learning algorithms to analyze user behavior, determine optimal messaging strategies, and execute campaigns autonomously, thereby improving productivity while reducing the need for human-defined campaigns
Data Source
AI summary
The subject technology discloses configurations, for a set of unique users, processing application usage logs to determine a set of features of an application accessed by each user. A respective profile of each user is then updated based on the determined set of features accessed by the set of unique users. The subject technology determines a set of users that have lapsed in usage of an application based on a respective profile of each user. One or more previous engagement messages sent to the determined set of users are determined. The subject technology ranks a set of engagement types for each user of the determined set of users based on a set of criteria including the determined previous engagement messages. A new engagement message is generated based on a selected engagement type and then transmitted to each user of the determined set of users.