Machine Learning Channel Selection for Intelligent Communication Delivery
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Solution Overview
Problem
Existing customer communications management systems require manual, complex modeling of communication flows to reach individual recipients, which is inefficient and ineffective due to the need for explicit channel selection and error handling, lacking an automated approach to select the most effective delivery channel.
Innovation Solution
An automated self-learning system using AI to analyze recipient properties and communication effectiveness, selecting the best channel based on configurable customer data and context, and self-learning from previous delivery results to optimize future communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual modeling of communication flows is used to reach individual recipients, then communication delivery can be achieved, but the process becomes complex and inefficient due to explicit channel selection and error handling requirements
Solution Approach 1:
The system employs a self-learning machine learning engine that automatically selects output connectors based on context information and recipient information without requiring manual configuration. The engine learns from previous communication outcomes and autonomously optimizes delivery channels, eliminating the need for users to manually model complex communication flows with error handling and channel selection logic
Solution Approach 2:
The patent replaces the manual mechanical process of modeling communication flows with an automated intelligent system. The machine learning engine substitutes the manual configuration process, using algorithms to automatically determine optimal delivery channels based on learned patterns from historical data, thereby reducing both operational complexity and model complexity
2Productivity
If automated channel selection is implemented, then delivery efficiency is improved, but the system requires complex machine learning engines and data processing
Solution Approach 1:
The machine learning engine is designed as a universal component that handles multiple communication channels (email, SMS, voice, etc.) through a single unified system. The engine processes various types of context information and recipient information across different channels, consolidating what would otherwise require separate manual configurations for each channel into one multi-functional automated system
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning engine continuously learns from communication outcomes and delivery results. This feedback loop allows the system to automatically improve its channel selection accuracy over time, enhancing delivery efficiency while the complexity is managed through iterative learning rather than requiring overly complex initial system design
Data Source
AI summary
Systems, methods and products for intelligent delivery of communications, where a machine learning engine is trained to identify an output channel for delivery of a communication based on received context information and intended recipient information and to route the communication to the selected channel. An intelligent delivery task in a communication flow model is performed by the machine learning engine, which receives customer/recipient data such as age, region, gender, etc., and context data such as communication type, time of day, working hours, etc., and uses this data to determine which of a set of different channels is likely to be most effecting for sending the communication to the recipient. A user therefore does not have to build a complex static communication flow, but simply adds an intelligent delivery task to the flow. The output channel is dynamically selected and may vary for different recipients and communications.