Customer Journey Prediction Using Discriminatory Sequence Patterns
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
As the number of customer interactions across various channels increases, businesses face challenges in understanding customer intent, motivations, and friction points, making it difficult to deliver consistent experiences and predict customer journeys effectively.
Innovation Solution
Implementing intelligent customer journey prediction and segmentation using machine learning operations to identify discriminatory sequence patterns, assign scores, and segment customers based on engagement patterns, thereby capturing motivations and intent, and identifying friction points that affect customer actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the number of customer interactions across various channels increases, then the quantity of customer data increases, but the difficulty of understanding customer intent and motivations increases
Solution Approach 1:
The patent introduces machine learning models and predictive analytics as intermediary systems that process raw customer interaction data and extract meaningful patterns, customer intent, and motivations. These intermediaries translate unstructured data from multiple channels into actionable insights, resolving the difficulty of understanding customer intent from increasing data quantities.
Solution Approach 2:
The patent replaces manual analysis methods with automated machine learning systems. Instead of human analysts manually examining customer interaction data, the system uses algorithms, neural networks, and predictive models to automatically identify patterns, segment customers, and forecast behaviors, thereby managing the complexity of increasing data volumes.
2Measurement precision
If machine learning operations are used to identify discriminatory sequence patterns, then customer journey prediction accuracy improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex machine learning system into distinct functional modules: data collection components, preprocessing modules, pattern recognition algorithms, customer segmentation engines, and prediction models. Each module performs a specific function, making the overall complex system more manageable and maintainable while achieving high prediction accuracy.
Data Source
AI summary
Embodiments for implementing intelligent customer journey prediction and customer segmentation of a processor in a computing environment. A response outcome of a customer journey for a user may be predicted according to an assigned score based on one or more discriminatory sequence patterns identified between one or more groups of customers using one or more machine learning operations.