Customer Satisfaction Score (CSAT)

Customer Satisfaction Score — commonly abbreviated as CSAT — is one of the most widely used and directly meaningful metrics available to any business. At its core, CSAT asks customers a simple question: “How satisfied were you with your experience?” Responses are typically collected on a 1–5 or 1–10 scale, with the final score expressed as a percentage of customers who gave a positive rating.

The formula is straightforward: divide the number of satisfied customers by the total number of respondents and multiply by 100. A score of 80% means 80 out of every 100 customers left an interaction feeling positive. Simple to understand — but powerful in its implications.

In a competitive marketplace, the cost of losing a customer far exceeds the cost of keeping one. Research consistently shows that acquiring a new customer can cost five times more than retaining an existing one. CSAT sits at the heart of this equation — it tells you, in near real-time, whether your customers are likely to stay, return, and recommend you to others.

Beyond retention, CSAT data informs almost every department within a business. For operations teams, it highlights process failures. For product teams, it exposes friction in the user journey. For HR, a declining CSAT in customer-facing roles can signal training needs or staffing gaps. When treated as a strategic metric rather than a box-ticking exercise, CSAT becomes a shared language that connects the voice of the customer to every corner of the business.

One of the most common discoveries when working with businesses on their customer data is how much relevant information already exists — completely untapped. CSAT-relevant data doesn’t only live in post-transaction surveys. It accumulates across every customer touchpoint: support ticketing systems, email response times, online reviews, live chat logs, CRM records, social media mentions, NPS responses, warranty claims, and even time-to-resolution data in field service systems.

Many organisations capture this data as a matter of operational necessity — logging support tickets, recording call outcomes, noting complaint categories — without ever connecting the dots. The result is a fragmented picture: islands of data that individually seem unremarkable, but together paint a detailed and actionable portrait of customer sentiment across the entire journey.