AI in Contact Center Analytics

Intents/sentiment analysis, transcription services, agent prompts, and quality dashboards are used to boost FCR/NPS and reduce AHT.

AI assists with Intents via ASR/NLP and Agent Assist, alongside Quality dashboards.

Quality Assessment & Scripts, Risk Detection

Dashboards visualizing call topics/reasons and their effectiveness are crucial for analysis.

Key metrics include AHT, FCR, CSAT/NPS, and QA score measurements.

live demo · related simulation● LIVE

Escalations & Repeat Calls

Privacy concerns (PII) require masking, access controls, and DPIA assessments.

Multilingual support, accents, and channel integration are essential for comprehensive coverage.

Frequently asked questions

What level of ASR accuracy is required? Is a WER of 85-95% sufficient?

ASR accuracy requirements vary depending on the domain; post-editing critical segments is crucial for achieving high accuracy.

How can FCR be improved? Are better articles/prompts key?

Improving FCR involves optimizing knowledge base content and prompts, analyzing reasons for repeat calls, and leveraging RAG (Retrieval-Augmented Generation) responses.

Is real-time processing possible? Is low latency important?

Low latency is critical for sensitive scenarios, often requiring caching mechanisms or on-device processing.

How is quality control handled? Is manual QA still necessary?

A combination of selective human QA and automated checks provides a robust quality assurance process.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation

AI in Contact Center Analytics – Intents, Sentiment, and Efficiency