Machine Learning System Identifying Significant Customer Journey Attributes
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Solution Overview
Problem
Customer data processing systems lack the ability to identify significant attributes associated with record-related events, which are crucial for understanding customer experiences and optimizing interactions.
Innovation Solution
A system utilizing machine learning to process attributes of customer journey records, identifying significant attributes that impact outcomes and enabling the display and leveraging of these insights to adjust future customer interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional customer data processing systems are used, then basic data tracking is achieved, but the ability to identify significant attributes associated with customer experiences is lost
Solution Approach 1:
The patent introduces machine learning models as intermediary components between raw customer data and analysis outcomes. These models process and transform unstructured customer journey data into structured insights, enabling identification of significant attributes without requiring complex manual analysis systems.
Solution Approach 2:
The patent replaces traditional mechanical data processing methods with machine learning-based automated analysis. Instead of using rule-based systems or manual analysis to identify significant attributes, the system employs trained machine learning models that automatically detect patterns and significance in customer journey data.
2Loss of information
If machine learning is applied to process customer journey attributes, then identification of significant attributes is enabled, but processing time and computational resources increase
Solution Approach 1:
The patent applies machine learning models in advance to customer journey data to pre-identify significant attributes and patterns. By performing this analysis beforehand, the system prepares structured insights that can be quickly retrieved and applied to real-time customer interactions, reducing processing time during actual customer engagements.
3Measurement precision
If comprehensive customer journey data is analyzed, then deeper insights into customer experiences are achieved, but data processing complexity and resource requirements increase
Solution Approach 1:
The patent extracts and focuses analysis on specifically significant attributes within customer journey data rather than processing all data uniformly. The machine learning models identify and extract only the most relevant features and patterns that drive customer experience outcomes, filtering out noise and less important information to reduce processing complexity.
Data Source
AI summary
A system, method, and computer program product are provided for identifying significant attributes of records (e.g. journeys, etc.). A plurality of records are stored, including a plurality of events with a plurality of attributes. Further, the attributes of the events are processed, utilizing machine learning. To this end, at least one of the attributes are identified as being significant, based on the processing. Such identified at least one attribute may then be displayed, etc.