Support quality, written down.

Auto QA is the automated scoring of customer conversations against a quality scorecard, replacing manual sampling with full, continuous coverage.

CES measures how much effort a customer spent to get an issue resolved. Here is how it is asked, how it is scored, and why low effort predicts loyalty.

Conversation analytics analyzes customer conversations at scale to surface topics, trends, quality and outcomes. Here is how it works and where it fits.

Conversation intelligence is software that automatically analyzes customer chat, email and voice conversations to surface quality and insight at scale.

The standard CSAT survey question, the formula with a worked example, typical benchmarks and the limits to keep in mind when you report it.

First Contact Resolution (FCR) is the share of issues solved in one interaction. Here is the formula, benchmarks, and why it drives CSAT and cost.

First Response Time (FRT) is how long a customer waits for the first reply. Here is how it is measured across channels, benchmarks, and why it shapes CSAT.

Interaction analytics analyzes customer interactions across voice, chat and email to surface quality, sentiment and trends at scale. Here is how it works.

LLM-as-a-judge uses a large language model to grade text or conversations against defined criteria. Here is how it works and why evidence matters.

NPS measures customer loyalty on a 0 to 10 scale. Here is the formula, what promoters and detractors mean, benchmarks, and how it links to QA.

QA calibration is when reviewers score the same conversation to align on standards, so scores stay consistent. Here is why it matters and how to run it.

Quality monitoring is the ongoing review of customer conversations against a scorecard to measure quality. Manual vs automated coverage explained.