Post-Call Customer Satisfaction Prediction Model

Proprietary Post-Call Customer Satisfaction Prediction Model Based on AI and Regression Analysis to Predict Agent CSat Score Derived from an Auto QA Evaluation

ID, UNITED STATES, November 27, 2024 /EINPresswire.com/ -- Benchmarkable CSat Prediction

Our proprietary post-call CSat prediction model uses AI, machine learning, natural language processing, and regression analysis techniques to benchmark and predict CSat. The CSat prediction is typically based on various factors related to the call, such as agent behavior and how a typical customer would rate the call. SQM's Agent QA CSat Score Prediction Matching to Survey Agent CSat is ˃90%. Our capability to determine QA and predict CSat accurately on every call is what truly sets us apart from our competitors.

Purpose of Predicting Customer Satisfaction

1. Enhance Customer Experience:
• Call centers can improve the customer experience by identifying areas to improve.

2. Improve Agent Performance:
• Insights from the model can highlight areas where agents can improve, such as communication skills or efficiency.

3. Optimize Processes:
• Understand operational bottlenecks like long hold times or unresolved issues that lead to dissatisfaction.

4. Reduce Customer Churn:
• Address dissatisfaction proactively to improve retention and loyalty.

SQM Marketing
SQM Group Inc.
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