ML-Based Communication Channel Customization
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Solution Overview
Problem
Conventional systems used by enterprise organizations for communicating with users rely on generic communication methods and objective data, failing to account for subjective user passions and interests, leading to ineffective customization.
Innovation Solution
A machine learning model is trained using historical data from various sources to identify user-specific categories, allowing for customized communication schemes that include preferences in communication frequency, channel, terminology, and settings, which are continuously updated based on additional user data and feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic communication methods are used, then system complexity is reduced, but communication effectiveness and user satisfaction deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting user data from multiple sources (social media, enterprise systems, surveys) and training machine learning models in advance to predict user categories and communication preferences. This preparation enables personalized communication without adding complexity during actual communication operations.
Solution Approach 2:
Machine learning models serve as intermediaries between raw user data and communication customization. The models process and interpret diverse data sources, translating them into actionable user categories and preferences, thereby managing system complexity while improving communication effectiveness.
2Measurement precision
If objective data only is used for customization, then data reliability is improved, but customization accuracy deteriorates due to inability to capture subjective user passions
Solution Approach 1:
The system merges multiple data sources including objective enterprise data, public social media data, and subjective user self-reported data. This combination allows the machine learning models to capture both reliable objective information and subjective user passions, improving customization accuracy while maintaining data reliability through multi-source validation.
Solution Approach 2:
The system implements feedback mechanisms where user responses to communications and interactions with the enterprise are continuously collected and fed back into the machine learning models. This feedback loop refines user category predictions and preference accuracy over time, improving customization precision while maintaining reliability through iterative validation.
3Measurement precision
If machine learning models process multiple data sources, then customization accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary processing by collecting and pre-processing user data from multiple sources before actual communication needs arise. Machine learning models are trained in advance on historical data, enabling rapid inference and user categorization when communication customization is required, thereby reducing real-time processing time.
Solution Approach 2:
The system implements dynamic data processing where the depth and breadth of data analysis adapt based on communication context and user interaction stage. Not all data sources are processed equally at all times; the system dynamically adjusts processing intensity to balance accuracy with time and computational resource constraints.
Data Source
AI summary
Arrangements for communication channel customization are provided. In some aspects, historical data may be received from a plurality of data sources and used to train a machine learning model to generate recommended categories for association with users and customizations to communication schemes. Upon registering a user, user specific data may be received from data sources. The user specific data may be input to the machine learning model and, upon execution of the model, a recommended category for association with the user may be output. Based on the recommended category, a communication scheme may be retrieved and executed for the user. Subsequent user data may be received and used as inputs in the machine learning model. The model may be executed to output one or more customizations to the communication scheme. The one or more customizations may be transmitted to one or more computing systems and executed to further customize communications.