Contact Center Dialogue Analysis for High Customer Effort Detection
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Solution Overview
Problem
Existing contact center systems struggle to identify and address interactions associated with high customer effort, leading to customer dissatisfaction and inefficient resource utilization due to systemic and representative-related issues, which are often not adequately detected in random sampling reviews.
Innovation Solution
A contact center system that generates dialogue data, identifies communication sessions with high customer effort through keyword analysis, and displays user-selectable records, enabling comprehensive review and coaching opportunities for representatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If random sampling reviews are used to evaluate customer interactions, then review complexity is reduced, but detection precision of high customer effort interactions deteriorates
Solution Approach 1:
The patent replaces manual random sampling review with an automated keyword analysis system. The contact center system automatically generates dialogue data, identifies keywords associated with high customer effort, and flags relevant communication sessions without requiring manual review of random samples. This substitution of mechanical/manual processes with automated computational analysis resolves the contradiction by achieving both low complexity and high precision simultaneously.
2Measurement precision
If comprehensive review of all communication sessions is conducted, then detection precision improves, but productivity decreases
Solution Approach 1:
The patent extracts only the most relevant communication sessions for review by identifying keywords associated with high customer effort. Instead of reviewing all communication sessions or random samples, the system extracts and flags specifically those sessions containing keywords that indicate high customer effort perceptions. This extraction approach achieves high detection precision while maintaining productivity by focusing review resources only on relevant cases.
3Productivity
If keyword analysis is implemented to identify high effort interactions, then resource utilization improves, but device complexity increases
Solution Approach 1:
The contact center system performs self-service by automatically generating dialogue data, identifying keywords, and flagging high customer effort interactions without requiring external manual analysis. The system uses its own computational resources to analyze communication sessions and identify patterns, eliminating the need for extensive external review resources. This self-service capability improves resource utilization while the automation actually reduces overall system complexity compared to manual processes.
Data Source
AI summary
A contact center system can track and evaluate dialogue data, telephony data, and/or application usage data associated with communication sessions between customers and representatives. The contact center system can identify communication sessions that have dialogue data containing keywords of one or more keyword categories, such as keyword categories associated with perceptions of high customer effort, and/or based on values of other key performance indicators. Users can use the contact center system to investigate the dialogue data, telephony data, and/or application usage data for identified communication sessions, for example to identify opportunities to train representatives to use alternate language during communication sessions, revise procedures in the contact center, or otherwise reduce perceptions of customer effort.