Snapshot Contact Center QA Benchmark Study

GenAI enables detailed performance rankings, gap analysis, and actionable insights to enhance both customer experience and regulatory compliance. Importantly, we enforce a strict zero-transcription data retention policy, and no call data is used for training our AI models.

The QA Scoring Framework

Our approach utilizes a GenAI-driven architecture that delivers better-than-human-level QA scoring while mitigating bias and enhancing consistency across evaluations.

Step-by-Step Process:

1. Input Ingestion & Preprocessing

• Audio Transcription: Speech-to-text applied to call recordings
• Speaker Diarization: Differentiates between agent and customer
• Data Redaction: Sensitive data (e.g., names, credit card info, PHI) is removed
• Text Normalization: Structured transcripts prepared for analysis for GenAI analysis

2. Intent & Behavior Detection

• Natural Language Understanding (NLU): Identifies key intents (e.g., inquiry, resolution)
• Sentiment Analysis: Captures the emotional tone of both parties
• Behavioral Tagging: Detects QA-relevant behaviors like empathy, greeting, active listening

3. Scoring Methodologies

• Rule-Based Logic: Evaluates deterministic metrics (e.g., identity verification)
• Predictive Scoring Using ML/LLMs: Scores complex or subjective elements like empathy or confidence using models trained on thousands of human-scored QA interactions. (YOUR QA DATA IS NOT USED FOR MODELING)
• CSAT Prediction: Proprietary models forecast agent and call center satisfaction with up to 95% accuracy (we recommend all new clients conduct surveys to assess accuracy against QA scores and CSAT predictions).

4. Aggregation & Benchmarking

• Metric Consolidation: Grouped by categories (compliance, soft skills, CX)
• Composite QA Score: Weighted summary of all QA dimensions
• Benchmarking: Performance compared across peers, industry norms, and world class contact centers