Caller Identification Metadata System for Agent Skill Matching
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Solution Overview
Problem
Current call center systems lack the ability to effectively utilize comprehensive metadata for caller ID selection and agent skill matching, leading to suboptimal call outcomes due to limited data collection and analysis.
Innovation Solution
A system that gathers and utilizes extensive metadata, including geographic, demographic, and call-related information, to enhance caller ID selection and agent skill matching by populating databases and applying predisposed logic pathways for real-time decision-making during inbound and outbound calls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive metadata is collected from multiple sources, then caller identification accuracy and agent skill matching improve, but system complexity and data collection difficulty increase
Solution Approach 1:
The system divides metadata collection into multiple independent modules, each responsible for specific data types (geographic, demographic, call-related). This segmentation allows comprehensive data gathering while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The system employs a universal metadata collection framework that can gather various types of data from multiple sources through standardized interfaces. This multi-functional approach enables comprehensive caller identification without proportionally increasing system complexity.
2Productivity
If extensive metadata is gathered and analyzed in real-time, then call effectiveness and agent matching improve, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing metadata in optimized databases before calls occur. This allows rapid retrieval and analysis during actual calls, improving call effectiveness while minimizing real-time processing delays.
Solution Approach 2:
The system dynamically adjusts the depth and scope of metadata analysis based on call priorities and available resources. This dynamic processing approach ensures comprehensive analysis for high-value calls while using reduced processing for routine calls, optimizing both effectiveness and time efficiency.
Data Source
AI summary
A system and method for increasing meta data with a call center system to provide increased caller ID and agent skill matching. A system may include pre-populated meta data from a given phone number. A processor may include pre-programmed logic sequences for increasing meta data for the system by providing signal outputs to other sources and vendors to request the missing meta data. The information obtained from the other sources is input into the memory of the system for another logic sequence to act upon to provide the phone number or a call to the best possible and available agent based on agent skills and other information. Agents are able to access all the meta data prior to, during or after the call. Inbound and outbound calls may each utilize the system.