Satisfaction Score
Definition
Customer Satisfaction (CSAT) score is calculated from post-conversation survey responses. After a chatbot interaction ends, the user is asked: 'How satisfied were you with this interaction?' and rates on a scale (1-5, 1-10, or thumbs up/down). CSAT is expressed as a percentage of positive responses (typically 4 or 5 out of 5). For chatbots, CSAT measures the user's overall experience — whether the bot resolved their issue, was easy to use, and felt helpful. CSAT is tracked over time and segmented by intent type, channel, and user segment to identify where the bot is performing well or poorly.
Why It Matters
CSAT is the gold-standard measure of chatbot value from the user's perspective. Internal metrics like containment rate and fallback rate tell you about bot capability; CSAT tells you whether users actually felt helped. A chatbot with a 95% containment rate but a 2.5/5 CSAT score is technically resolving queries but doing so in a way users find unsatisfying. CSAT is also the metric most comparable to human agent benchmarks, making it useful for ROI justification and stakeholder reporting.
How It Works
Post-conversation CSAT is triggered by the goodbye flow or a timed prompt after session end. The survey is presented in the chat widget or via a follow-up email/SMS for async channels. User responses are logged with the session ID, enabling correlation with conversation length, intents, topics, and fallback events. CSAT dashboards show rolling averages, trend lines, and breakdowns by segment. Low-CSAT conversations are flagged for review.
Real-World Example
A chatbot team tracks CSAT weekly. They notice CSAT drops from 4.2 to 3.8 after a knowledge base update. Reviewing low-CSAT conversations, they find that a recently updated refund policy article now contains incorrect information that the bot is retrieving and presenting to users. They fix the article and CSAT returns to 4.2 within a week.
Common Mistakes
- ✕Collecting CSAT only for resolved conversations — users who escalated or abandoned are equally important signals.
- ✕Using CSAT as the sole measure of bot performance — pair it with containment rate, FCR, and fallback rate for a complete picture.
- ✕Not acting on low-CSAT conversation reviews promptly — every low-rated conversation is a specific, actionable improvement opportunity.
Related Terms
Chatbot Feedback
Chatbot feedback is the collection and analysis of user opinions about their chatbot experience — typically through thumbs up/down ratings, star ratings, or short surveys. It provides direct user signal on response quality, helping teams identify failures and prioritize improvements.
A/B Testing for Chatbots
A/B testing for chatbots involves running two or more versions of a chatbot response, flow, or prompt simultaneously and measuring which performs better on key metrics like resolution rate, user satisfaction, or conversion. It enables data-driven optimization of chatbot design rather than relying on intuition or guesswork.
Chatbot Analytics
Chatbot analytics is the measurement and analysis of chatbot performance — tracking metrics like conversation volume, resolution rate, fallback rate, escalation rate, and user satisfaction. These insights reveal how well the bot is performing and where to focus improvement efforts.
Escalation to Human
Escalation to human is the process by which a chatbot transfers a conversation to a live human agent when it cannot resolve the user's issue. Effective escalation passes the full conversation context to the agent, ensuring the user doesn't have to repeat themselves and the agent can immediately continue where the bot left off.
Chatbot Testing
Chatbot testing is the process of evaluating a chatbot's performance before and after deployment — verifying that intents are correctly recognized, flows execute as designed, edge cases are handled gracefully, and responses meet quality standards. Regular testing prevents regressions and ensures the bot delivers a reliable user experience.
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