Generative AI Deep Link Messaging for User Retention
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Solution Overview
Problem
Businesses face challenges in re-engaging inactive users who have shown initial interest in products or services, leading to user churn, making it costly to acquire new customers and maintain a healthy customer base.
Innovation Solution
A system and method utilizing generative artificial intelligence to analyze user and contextual data, generating personalized pre-filled messages through a dynamic-link initiated approach, integrating LSTM-based models with attention mechanisms to provide timely and relevant interactions within messaging applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional user re-engagement methods are used, then implementation is simple, but user engagement and retention effectiveness is low
Solution Approach 1:
The patent introduces an AI intermediary system that sits between the user data and the re-engagement messaging. This AI intermediary analyzes user behavior patterns, preferences, and contextual information to generate personalized re-engagement strategies, thereby improving retention effectiveness while managing system complexity through a dedicated intermediate layer
Solution Approach 2:
The patent replaces traditional rule-based or manually-designed re-engagement systems with machine learning models that automatically analyze user data and generate personalized messages. This substitution of mechanical/rules-based systems with intelligent algorithms improves engagement effectiveness while the automation helps manage complexity
2Reliability
If personalized follow-up messages are implemented, then user engagement improves, but data processing requirements increase
Solution Approach 1:
The patent extracts only the most relevant features and data points needed for personalization from the vast user data repository. By selecting and extracting key attributes such as user preferences, behavior patterns, and contextual information rather than processing all available data, the system achieves effective personalization while reducing data processing volume
Solution Approach 2:
The patent performs preliminary data processing and feature extraction during user interaction sessions, preparing and storing processed user profiles and preferences in advance. This preliminary action reduces the data processing burden during actual re-engagement campaigns, as the AI model can directly query pre-processed user data rather than analyzing raw data in real-time
3Loss of time
If real-time message generation is implemented, then response timeliness improves, but computational resources increase
Solution Approach 1:
The patent pre-processes user data and maintains ready-to-use user profiles with key attributes during initial user interactions. When a re-engagement opportunity arises, the system can quickly retrieve and personalize messages using pre-computed user features rather than performing full data analysis in real-time, thus reducing both response time and real-time computational resource consumption
Solution Approach 2:
The patent applies different levels of processing intensity to different aspects of message generation. Critical personalization elements are pre-computed and stored, while only specific message components require real-time generation. This local differentiation of quality and processing intensity optimizes the balance between response timeliness and computational resource usage
Data Source
AI summary
A system and methods for dynamic-link initiated user engagement and retention utilizing generative artificial intelligence. The system integrates a generalized generative AI, personalized to each user's context, generating pre-filled message to be displayed within a messaging application on a mobile device. It utilizes user profiles, interaction history, and deep link context to dynamically generate contextually relevant pre-filled messages. The system may employ an LSTM-based model with attention mechanisms for both timing and content prediction. It interfaces with the messaging app through an API, extracting deep link context and triggering AI-generated suggestions. This enables seamless, personalized follow-up messages accompanying deep links, fostering customer engagement and retention by providing timely and valuable interactions. Continuous monitoring and model updates ensure optimal performance and alignment with user preferences, ultimately enhancing user experience and long-term app engagement.