Customer Persona Classification for Targeted Content Delivery
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Solution Overview
Problem
Existing systems fail to provide personalized experiences for customers by effectively segmenting and targeting content based on customer attributes, leading to inefficiencies in service delivery and customer satisfaction.
Innovation Solution
A system and method for persona generation using K-means clustering and decision tree classifiers to identify customer personas, allowing for targeted content delivery based on attribute data, including numerical and categorical information, and dynamically adjusting service parameters based on location-specific RAN attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional customer segmentation methods are used, then customer grouping is achieved, but personalized content targeting and service delivery remain ineffective
Solution Approach 1:
The patent segments customers into distinct personas using unsupervised learning (K-means clustering) on attribute data, then applies supervised learning (decision tree classifiers) to classify customers into specific personas. This two-stage segmentation approach enables personalized content targeting by creating granular customer groups based on multiple attributes including demographic, device, and behavioral characteristics.
Solution Approach 2:
The system transforms raw customer attribute data into meaningful persona classifications by changing the parameter representation from individual attributes to composite persona labels. The decision tree classifier generates human-readable segmentation rules that translate complex multi-attribute data into actionable persona categories, enabling personalized service delivery.
2Measurement precision
If detailed customer attribute data is processed, then accurate persona identification is achieved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary clustering using K-means algorithm to group similar customers before applying the decision tree classifier. This preliminary action reduces the computational burden of the subsequent classification stage by pre-organizing data into coherent clusters, thereby maintaining high persona identification accuracy while reducing overall computational complexity.
Solution Approach 2:
The system replaces traditional mechanical or rule-based customer segmentation methods with machine learning algorithms (K-means clustering and decision tree classifiers). This substitution enables accurate processing of detailed customer attribute data without proportionally increasing computational complexity, as the algorithms automatically learn patterns and relationships from the data.
3Reliability
If personalized service delivery is implemented, then customer satisfaction improves, but system operational efficiency decreases
Solution Approach 1:
The system changes the parameter representation from raw customer attributes to compressed persona labels that capture essential customer characteristics. These persona labels serve as efficient keys for retrieving and delivering personalized content, maintaining high customer satisfaction while improving service delivery efficiency through faster content retrieval and targeted delivery.
Solution Approach 2:
The patent creates compressed representations of customer personas that capture the essential characteristics needed for personalized service delivery. These persona copies enable efficient content targeting without requiring processing of the full original customer attribute datasets, thereby maintaining personalization quality while improving operational efficiency.
Data Source
AI summary
One or more computing devices, systems, and/or methods are provided. In an example, a method comprises receiving, by a device, attribute data for a customer, determining, by the device, a cluster label for the customer based on the attribute data, determining, by the device, a first customer persona for the customer using a decision tree classifier model based on the cluster label and the attribute data, and providing, by the device, targeted content based on the first customer persona. Embodiments described herein also include a system comprising a processor configured to execute instructions to facilitate performance of operations comprising receiving attribute data for a customer, determining a cluster label for the customer based on the attribute data, determining a first customer persona for the customer using a decision tree classifier model based on the cluster label and the attribute data, and providing targeted content based on first customer persona.