Unified AI Model for Customer Value Prediction
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Solution Overview
Problem
Existing data modeling techniques, particularly in big data environments, are not optimized for accurately predicting user behavior and outcomes, leading to inefficiencies in resource allocation and productivity in transactional systems.
Innovation Solution
The use of densely connected neural networks, unified AI models, and structured convolutional neural networks to predict customer value and its sub-variables directly from raw transactional data, enabling more robust and accurate predictions even for users with limited transaction history.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing data modeling techniques are used to predict user behavior, then resource allocation can be performed, but prediction accuracy is insufficient leading to inefficiencies in productivity and resource utilization
Solution Approach 1:
The patent combines multiple prediction models (customer value prediction, churn prediction, lifetime value prediction) into a unified machine learning framework that processes transactional data simultaneously. This integration allows the system to generate comprehensive predictions across multiple dimensions, improving both accuracy and resource allocation efficiency by eliminating the need for separate modeling processes.
Solution Approach 2:
The patent transforms traditional modeling approaches by changing the parameters from separate single-variable models to multi-variable predictive models. The system uses techniques such as gradient boosting, random forests, and neural networks with multiple output nodes to predict several customer metrics simultaneously, thereby improving prediction precision while maintaining computational efficiency.
2Measurement precision
If machine learning techniques are applied to improve prediction, then prediction capability can be enhanced, but the techniques are not well optimized leading to computational inefficiencies
Solution Approach 1:
The patent implements preliminary data processing and feature engineering steps before model training, including data cleaning, normalization, and feature selection. The system pre-processes transactional data to extract relevant patterns and relationships, which reduces the computational burden during actual prediction and improves both accuracy and efficiency.
Solution Approach 2:
The patent creates optimized copies of the machine learning models for different prediction tasks. Instead of running multiple separate models, the system uses a unified model architecture with multiple output layers that can generate predictions for customer value, churn probability, and lifetime value simultaneously, reducing computational time and resource consumption.
Data Source
AI summary
A unified model for a neural network can be used to predict a particular value, such as a customer value. In various instances, customer value may have particular sub-components. Taking advantage of this fact, a specific learning architecture can be used to predict not just customer value (e.g. a final objective) but also the sub-components of customer value. This allows improved accuracy and reduced error in various embodiments.