ML Communication Channel Selection for User Responsiveness
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Solution Overview
Problem
Organizations face inefficiencies in determining the most effective communication channels for interacting with users due to the use of simple rule-based heuristics that lack personalization and quantitative measures, leading to ineffective communication and resource waste.
Innovation Solution
A machine learning-based system that generates feature vectors from user profiles and time series data to predict user responsiveness across various communication channels, using models like gradient boosted decision trees to select the optimal channel for user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based heuristics are used to determine communication channels, then the system is simple to implement, but the communication effectiveness and user response rate deteriorate
Solution Approach 1:
The patent replaces rule-based heuristics (mechanical system) with a machine learning model that uses gradient boosted decision trees to predict user responsiveness. The system processes user profile data, communication time series, and event time series through computational algorithms to determine optimal communication channels, substituting simple if-then rules with sophisticated predictive analytics that adapt to individual user behavior patterns.
Solution Approach 2:
The system changes from static rule-based parameters to dynamic learned parameters. The machine learning model learns optimal communication channel selections based on user responses to different channels over time, adjusting predictions as user behavior changes. This allows the system to adapt to evolving user preferences rather than relying on fixed rules that become outdated.
2Device complexity
If rule-based heuristics with broad user categorizations are used, then the system complexity is reduced, but the personalization and accuracy of communication channel selection deteriorate
Solution Approach 1:
The system segments users into highly granular groups based on their individual behavior patterns rather than broad categories. Each user's communication history, response patterns, and preferences are analyzed separately to create personalized predictions. This fine-grained segmentation allows the system to treat each user as a unique segment with distinct communication preferences.
Solution Approach 2:
The system creates detailed digital copies of user behavior patterns through comprehensive data collection and modeling. By copying and analyzing individual user interactions across multiple channels and time periods, the system builds accurate digital representations of user preferences that enable highly personalized communication channel selection without requiring complex manual configuration.
3Ease of operation
If rule-based techniques without quantitative measures are used, then the system is easier to operate, but the ability to adapt to changing user behavior deteriorates
Solution Approach 1:
The system implements continuous feedback loops where user responses to communications are measured and fed back into the machine learning model. The model quantifies user responsiveness to different channels and uses this feedback to refine future predictions. This automated feedback mechanism enables the system to adapt to changing user behavior without manual intervention, maintaining ease of operation while dramatically improving adaptability.
Solution Approach 2:
The system transitions from static rule-based operations to dynamic adaptive operations. The machine learning model continuously updates its predictions based on new data, allowing the system to respond dynamically to changing user preferences and behaviors. This dynamic approach maintains ease of operation through automation while enabling real-time adaptation to evolving user patterns.
4Loss of energy
If ineffective communication mechanisms are used, then resource consumption is reduced, but user response rate and organizational goal achievement deteriorate
Solution Approach 1:
The system performs preliminary analysis of user profiles and behavior patterns before selecting communication channels. By predicting user responsiveness in advance using machine learning models, the system pre-identifies the most effective channels for each user, avoiding wasted resources on ineffective communication attempts. This preliminary action ensures that resources are allocated to high-probability successful communications.
Solution Approach 2:
The system changes communication channel selection from random or rule-based parameters to optimized parameters based on predicted user responsiveness. By adjusting channel selection parameters according to learned user preferences and response patterns, the system maximizes user response rates while minimizing resource consumption on low-probability communication attempts.
Data Source
AI summary
A system uses a machine learning based model to select a channel for communicating with users. The system generates a feature vector based on a user profile of the user. The user profile data includes time series data describing past communications to users and past user actions. The system executes one or more machine learning based models, each machine learning based model configured to receive a feature vector describing a particular user and predict a likelihood of the particular user performing an expected user action responsive to a communication sent via the communication channel. The system selects a communication channel based on the results of the machine learning based models and sends a communication to the user via the selected communication channel.