Prescriptive Analytics for Call Center Satisfaction Measurement
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Solution Overview
Problem
Existing methods for measuring customer satisfaction in call centers, such as CSAT, are challenging due to low customer response rates and the time-consuming nature of quality metrics, making it difficult to manage teams and identify areas for improvement.
Innovation Solution
Implementing a prescriptive analytics system that allows agents to self-report their perception of customer satisfaction via an interface, which is then correlated with actual customer feedback to provide feedback and enhance data collection, enabling proactive corrective actions and identifying top performers for coaching roles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional CSAT surveys are used to measure customer satisfaction, then customer satisfaction data can be collected, but the response rate is very low and the process is time-consuming
Solution Approach 1:
The system enables agents to self-report their perception of customer satisfaction through a user interface during or after calls. Agents input their own assessment of customer satisfaction, eliminating the need for external survey collection systems. This self-service approach directly addresses the low response rate issue by having the data source (agent) provide the measurement themselves rather than waiting for customer surveys.
Solution Approach 2:
The system incorporates feedback mechanisms where agents receive real-time or near-real-time feedback about customer satisfaction perceptions. The system can provide feedback to agents about their performance metrics and customer satisfaction scores, creating a continuous feedback loop that improves both data collection accuracy and agent awareness of customer satisfaction levels.
2Measurement precision
If quality assurance officers manually evaluate recorded calls, then detailed quality metrics can be obtained, but the process requires significant time and expertise
Solution Approach 1:
Agents perform self-evaluation of their own calls by inputting satisfaction perceptions through the interface. This eliminates the need for QA officers to manually review every call, significantly reducing the time and expertise required for quality assessment while maintaining detailed quality metrics through the structured self-reporting process.
Solution Approach 2:
The system creates a digital record or copy of the quality assessment process through automated data capture. Instead of manual review, the system captures and stores satisfaction data electronically, creating a replicable record that can be analyzed without requiring continuous human evaluation of recorded calls.
3Loss of information
If sporadic customer satisfaction data is collected, then customer feedback can be gathered, but the data frequency is low and makes team management difficult
Solution Approach 1:
The system enables continuous collection of customer satisfaction data by having agents report satisfaction perceptions for each call as it occurs. Rather than sporadic survey collection, the system maintains continuous data flow through automated capture at the point of service, providing real-time information for team management and performance monitoring.
Solution Approach 2:
The system introduces an intermediary data capture mechanism between the customer interaction and management decision-making. The automated interface acts as a mediator that continuously collects, stores, and makes satisfaction data available to management systems, bridging the gap between service delivery and management oversight without requiring manual intervention.
Data Source
AI summary
Methods and systems determining customer satisfaction in a work environment via prescriptive analytics. Self-reported data related to the perception of an agent with respect to customer satisfaction in a work environment (e.g., a call center) can be collected via an interface (e.g., an agent dashboard) that allows the agent to enter the self-reported data regarding the customer satisfaction. The self-reported data can then be correlated with an actual customer satisfaction score associated with the agent to derive data indicative of an interaction between the agent and a customer(s). Feedback data can then be provided via the interface indicating a correctness of the self-reported data based on correlating the self-reported data with the actual customer satisfaction score.