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Glossary

CSAT (Customer Satisfaction Score)

CSAT (Customer Satisfaction Score) is a metric that measures how satisfied customers are with a specific interaction, product, or experience. It is typically measured by asking customers to rate their experience on a scale (1-5 or 1-10) immediately after an interaction.

How it works

CSAT is calculated as:

CSAT = (Number of satisfied responses / Total responses) x 100

Typically, responses of 4-5 (on a 5-point scale) are counted as "satisfied." A CSAT score of 80% means 80% of respondents rated their experience 4 or 5 out of 5.

CSAT is measured at the individual interaction level, making it different from NPS (which measures overall brand loyalty) or CES (which measures effort). It is the most common metric for evaluating customer support quality.

Operational Review

In practice, csat (customer satisfaction score) should be evaluated by what it changes in the support workflow. Ask whether it improves answer accuracy, reduces repeated agent work, clarifies handoff decisions, or makes reporting easier. If the answer is only "it sounds modern," the concept is not yet operational.

A concrete example is post-chat survey for ai interactions: After an AI chatbot resolves a shipping question, the customer is asked "How helpful was this response?" on a 1-5 scale. A rating of 4 or 5 counts as satisfied. The aggregated score reveals the AI achieves 87% CSAT, on par with human agents.

The simplest takeaway is: CSAT measures satisfaction with a specific interaction, typically on a 1-5 or 1-10 scale

Why it matters

CSAT directly reflects whether customers feel their support issues were resolved effectively. High CSAT correlates with customer retention, positive reviews, and revenue growth. For AI chatbots, CSAT is the key metric for validating that AI resolution quality matches or exceeds human agent quality.

How Chatsy uses csat (customer satisfaction score)

Chatsy includes message-level feedback (thumbs up/down) on AI responses, which functions as a CSAT signal. This feedback helps identify where the AI performs well and where it needs improvement, enabling continuous accuracy refinement.

Real-world examples

Post-chat survey for AI interactions

After an AI chatbot resolves a shipping question, the customer is asked "How helpful was this response?" on a 1-5 scale. A rating of 4 or 5 counts as satisfied. The aggregated score reveals the AI achieves 87% CSAT, on par with human agents.

Comparing AI vs human CSAT

A support team tracks CSAT separately for AI-resolved and human-resolved conversations. They discover AI scores 82% on billing questions but only 65% on technical troubleshooting, signaling where the knowledge base needs improvement.

Real-time CSAT monitoring dashboard

A support manager monitors a live CSAT dashboard that flags any topic dropping below 75% satisfaction. When "password reset" CSAT drops to 68%, they update the knowledge base article and see the score recover to 85% within a week.

Key takeaways

  • CSAT measures satisfaction with a specific interaction, typically on a 1-5 or 1-10 scale

  • Scores above 80% are generally considered good; best-in-class teams achieve 90%+

  • CSAT is transactional (per-interaction) unlike NPS which measures overall brand loyalty

  • For AI chatbots, CSAT validates that automated resolution quality matches human agent quality

  • Message-level feedback (thumbs up/down) provides more granular CSAT data than post-conversation surveys

When csat (customer satisfaction score) does not apply

  • You have under 50 conversations per month. Sample sizes are too small for stable averages.
  • Your customers will not respond to satisfaction prompts and the response rate is below 5%.

Frequently asked questions

What is a good CSAT score?

A CSAT score above 80% is generally considered good. Best-in-class support teams achieve 90%+. For AI chatbots, aim for CSAT parity with human agents (typically 80-85%) as a baseline.

How is CSAT different from NPS?

CSAT measures satisfaction with a specific interaction (transactional). NPS measures overall brand loyalty and likelihood to recommend (relational). Both are useful: CSAT for support quality, NPS for overall customer relationship health.

How often should I measure CSAT?

Measure after every support interaction for the most accurate data. Keep the survey short: a single 1-5 rating question with an optional comment field achieves the highest response rates (typically 20-30% for chat, 5-10% for email).

Can AI chatbots achieve the same CSAT as human agents?

Yes, for common questions. Studies show AI chatbots achieve 80-90% CSAT on straightforward queries (FAQs, order status, policy questions), matching or exceeding human agents. CSAT drops for complex or emotional issues, which is why human handoff remains essential.

What does CSAT stand for?

CSAT stands for Customer Satisfaction Score. It is a transactional metric: customers rate a specific interaction (a support chat, a purchase, a delivery) on a short scale, usually 1-5 or 1-10, and the score is reported as the percentage who chose the top one or two options.

Does CSAT mean the same thing in healthcare?

In healthcare, "CSAT" usually still refers to Customer Satisfaction Score applied to patient experience. Note that in clinical contexts, CSAT can also be an unrelated abbreviation for Center for Substance Abuse Treatment, which is a US government agency, not a metric.

Related terms

First Response Time (FRT)

First Response Time (FRT) is a customer support metric that measures the elapsed time between when a customer submits a ...

Ticket Deflection

Ticket deflection occurs when a customer resolves their question or issue without creating a support ticket or contactin...

Human Handoff

Human handoff (also called human takeover, agent escalation, or live agent transfer) is the process of transferring an o...

Further reading

Measuring Customer SatisfactionChatbot Metrics To Track

Related Resources

Customer Support BlogSee Chatsy Features

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Conversational AIRetrieval-Augmented Generation (RAG)Vector SearchChatbotHuman HandoffFirst Response Time (FRT)Ticket DeflectionNatural Language Processing (NLP)EmbeddingKnowledge BaseLive ChatSentiment AnalysisHybrid SearchLarge Language Model (LLM)AI HallucinationPrompt EngineeringAgentic AIAI AgentFine-TuningIntent ClassificationTokenContext WindowOmnichannel SupportSLA (Service Level Agreement)NPS (Net Promoter Score)Average Handle Time (AHT)First Contact Resolution (FCR)WebhookSemantic Search

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