All glossary terms

Definition

CSAT (Customer Satisfaction Score)

CSAT, or Customer Satisfaction Score, is a survey-based metric that measures how satisfied a customer was with a specific support interaction. It is typically collected immediately after a ticket closes by asking the customer to rate their experience on a 1–5 or 1–10 scale. CSAT is the most widely used metric for measuring support quality at the individual conversation level.

How it works

CSAT is calculated by dividing the number of positive responses (usually 4–5 stars or 8–10 out of 10) by the total number of responses, then multiplying by 100. A CSAT score of 85% means 85% of responding customers rated their experience positively.

Response rates for CSAT surveys typically range from 10–30%. Low response rates introduce selection bias — customers who had strongly positive or negative experiences respond disproportionately, while average interactions go unmeasured.

CSAT differs from NPS (Net Promoter Score), which measures overall brand sentiment, and CES (Customer Effort Score), which measures how hard the customer had to work to get their issue resolved. CSAT is interaction-specific; NPS and CES are relationship-level.

For AI-handled tickets, CSAT is especially important because deflection rate alone doesn't confirm quality — a ticket can be 'deflected' by closing without resolution. CSAT validates that deflection was genuine.

When you'll encounter it

CSAT appears in every support platform's analytics dashboard, in vendor SLAs, and in quarterly business reviews. When evaluating an AI support tool, always ask for CSAT data alongside deflection rate — a vendor claiming 80% deflection with no CSAT data may be counting abandoned conversations as deflections.

How SupportSyndicate handles this

SupportSyndicate sends automatic CSAT surveys via the same channel used for the conversation (chat, email, or WhatsApp) immediately after ticket closure. Scores sync to your connected CRM or Klaviyo profile as a contact property, enabling post-support email flows and at-risk customer segmentation based on satisfaction signals.

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