Relationship Model for User Retention Prediction

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Solution Overview

Problem

Current methods for user retention and duration prediction in relationships between entities and users are costly and ineffective, with high rates of user defection across various industries, resulting in significant financial losses.

Innovation Solution

A system and method that utilize a dataset analysis framework, including an information collection module, processing module, and relationship module, to identify potential users by generating a relationship model that predicts desired characteristics such as user duration, using machine learning algorithms and data from multiple sources like mobile devices and IoT devices.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional user retention strategies are implemented (incentives, retention programs), then user retention may improve slightly, but implementation costs increase significantly

Engineering Contradiction:

Improveuser retentionVSAvoidimplementation cost

Core Design Contradiction:

ReliabilityVSLoss of energy

Solution Approach 1:

The system performs preliminary analysis of user behavior patterns and relationship dynamics before retention issues arise. By using machine learning models to predict relationship stability and identify at-risk users in advance, the system enables proactive retention strategies rather than reactive expensive interventions.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent replaces traditional mechanical retention mechanisms (incentives, discounts, retention programs) with an intelligent information processing system. The system uses data analysis, machine learning algorithms, and relationship modeling to automatically identify retention opportunities and predict user behavior, substituting expensive operational interventions with computational analysis.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If comprehensive data collection and analysis systems are deployed to predict user behavior, then prediction accuracy improves, but system complexity increases

Engineering Contradiction:

Improveprediction accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments the complex prediction task into distinct functional modules: data collection module, data processing module, relationship model generation module, and prediction module. Each module handles specific aspects of the analysis, making the overall system more manageable and maintainable while achieving high prediction accuracy through coordinated operation of specialized components.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces relationship models as intermediary representations that bridge raw user data and prediction outcomes. These models serve as intermediate artifacts that capture relationship dynamics and patterns, enabling the system to translate complex multi-source data into actionable predictions without requiring the entire system to directly process all raw data simultaneously.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11900396B2Systems and methods for generating a relationship among a plurality of datasets to generate a desired attribute value

Publication Date: 2024.02.13 JACKSON JAMES R

AI summary

A system or method for identifying a plurality of entities in a first dataset that satisfy a predetermined target attribute by deploying on the first dataset a relationship model generated from a second dataset having a plurality of entities not in the first dataset.