Tag-Based Contact Center Framework for Real-Time Data Analysis
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Solution Overview
Problem
Contact centers face challenges in efficiently collecting and utilizing customer interaction data in real-time to enhance operations and customer experience, as they often lack a structured framework for data interpretation across various communication channels.
Innovation Solution
A tag-based operational framework that identifies keywords from machine-readable text, updates keyword groups, and invokes actions such as routing calls or generating statistics, allowing for real-time data analysis and reporting, enabling deeper insights into customer interactions and business operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If contact centers use ad-hoc strategies for interpreting customer needs, then they can handle various communication channels, but they cannot effectively collect and derive information in real-time
Solution Approach 1:
The patent segments customer interaction data into discrete taggable units (keywords, phrases, concepts) that can be independently identified, categorized, and analyzed. This segmentation enables the system to process diverse communication channels through a unified tagging framework, resolving the contradiction between handling channel diversity and achieving real-time information extraction.
Solution Approach 2:
The patent introduces tags as an intermediary layer between raw communication data and business insights. These tags serve as mediators that standardize and structure information from various communication channels, enabling real-time analysis without losing the adaptability to handle different channel formats and structures.
2Loss of information
If contact centers implement comprehensive data collection, then they can deepen business insights, but they increase operational complexity
Solution Approach 1:
The patent extracts only the most relevant and actionable information from customer interactions by identifying and tagging key keywords, phrases, and concepts. This extraction approach enables deep business insights without requiring comprehensive processing of all interaction data, thereby reducing operational complexity while maintaining information depth.
Solution Approach 2:
The patent transforms unstructured communication data into structured tagged data, changing the parameter state from raw text to categorized information. This parameter transformation simplifies subsequent analysis operations while preserving comprehensive business insights, resolving the contradiction between insight depth and operational complexity.
3Adaptability or versatility
If contact centers process data in various formats, then they can accommodate multiple channels, but they cannot inherently structure data for real-time use
Solution Approach 1:
The patent creates a universal tagging framework that can process and structure data from multiple communication channels and formats. This multi-functional tagging system applies consistent structural rules across diverse data types, enabling real-time data readiness while maintaining adaptability to various input formats and channels.
Data Source
AI summary
An apparatus includes a processor and a memory. The memory stores instructions that when executed by the processor cause the processor to: identify a keyword from machine-readable text; identify a contact center resource based on the identified keyword; update a first group of keywords associated with the contact center resource based on the identified keyword; invoke an action based on analysis of the keyword associated with the contact center resource; monitor the action and report results in response; and update a second group of keywords according to analysis of the results.