Customer Satisfaction Score (CSAT)

Customer satisfaction has always been critical to marketing success, but it's historically been hard to measure at scale. CSAT solves this by asking customers a simple question—usually 'How satisfied are you?' on a 1-5 or 1-10 scale—and aggregating responses into a single, trackable number. Think of it like a report card for your customer experience.

The marketing relevance is straightforward: your campaigns promise value, but CSAT tells you whether customers actually received it. If your email campaign drives sign-ups but CSAT drops 10 points post-purchase, you've created a gap between expectation and reality. That gap becomes expensive—churn increases, word-of-mouth turns negative, and acquisition costs rise because you're fighting reputation damage.

In practice, AI tools now automate CSAT collection and analysis. Instead of manually sending surveys and reading responses, AI can trigger CSAT questions at optimal moments (after a support interaction, post-purchase, end of trial), analyze sentiment in open-ended feedback, and flag patterns—like 'customers who use Feature X report 20% higher satisfaction.' Some platforms use AI to predict which customers are at risk of low CSAT before they respond, allowing proactive intervention.

For CMOs evaluating marketing technology, CSAT becomes a key input to AI-driven personalization. If you know which customer segments report low satisfaction, you can adjust messaging, targeting, or product positioning before the next campaign. It also helps justify marketing spend: campaigns that improve CSAT by 15 points typically reduce churn and increase lifetime value far more than campaigns optimized for clicks alone.

The practical implication: CSAT isn't just a support metric anymore—it's a marketing intelligence signal. When selecting AI tools, ask whether they integrate CSAT data into campaign optimization, predictive models, and audience segmentation.