{"id":32209,"date":"2026-07-01T17:44:49","date_gmt":"2026-07-01T16:44:49","guid":{"rendered":"https:\/\/www.intercom.com\/blog\/?p=32209"},"modified":"2026-07-01T17:45:55","modified_gmt":"2026-07-01T16:45:55","slug":"how-to-measure-customer-experience-as-ai-scales","status":"publish","type":"post","link":"https:\/\/www.intercom.com\/blog\/how-to-measure-customer-experience-as-ai-scales\/","title":{"rendered":"How to measure the customer experience as AI scales"},"content":{"rendered":"

CSAT captures less than 10% of your conversations<\/a>, and the responses you do get skew toward extremes \u2013 the delighted and the furious.<\/p>\n

The vast majority of customers say nothing at all, especially when they\u2019re busy and would rather move on.<\/p>\n

That silence is a blind spot. You’re reporting to leadership and coaching your team based on a sample that doesn’t represent most of your customers. No amount of CSAT tuning fixes a response rate that low.<\/p>\n

\"Chart:<\/p>\n

Coverage isn\u2019t the only limitation, either. CSAT also compresses multiple problems into a single score. A negative rating could be driven by the product, policy frustration, or poor service quality. It alone won\u2019t tell you which. You end up spending more time questioning the data than acting on any of it, and it hides the friction that actually impacts the customer experience: repeated explanations, multiple handoffs, or technically correct answers that still felt frustrating.<\/p>\n

As AI handles more conversations end-to-end, the gap only gets bigger. A larger share of your customer experience sits outside direct human review, while CSAT still only reflects the subset of conversations that generate responses.<\/p>\n

Teams need to understand what happened in every conversation, rather than just the ones where someone chose to fill in a survey.<\/p>\n

What full coverage looks like<\/h2>\n

AI enables support teams to move from sampling to scoring every conversation, so instead of waiting for customers to tell you how it went, you can automatically evaluate each interaction. When every conversation is visible like this, you get a complete picture of what your customers are actually experiencing across service quality, resolution, and customer effort \u2013 the things a single survey was never able to show.<\/p>\n

If you’re using Fin, CX Score<\/a> provides this coverage. It evaluates every interaction (both AI and human), assigns a score from 1\u20135, and surfaces the reasons behind each score. For most teams, this results in roughly five times more conversation coverage than CSAT alone. If you’re using another solution, the principle is the same: you need AI-powered visibility into every conversation.<\/p>\n

\"Fin<\/p>\n

CSAT still has a role as an open door for customers who want to share their experience directly, but it’s the scored view of every conversation that tells you what’s really happening across your entire customer experience, which is especially important as AI scales.<\/p>\n

How to set targets<\/h3>\n

You can\u2019t map old CSAT targets onto a new metric. The coverage is different, so you need to build targets from the data itself.<\/p>\n

At Fin, we started by correlating CX Score with operational metrics like first response time and time to close. That gave us useful targets for human support, but we wanted to take a deeper look at how our Agent Fin was performing.<\/p>\n

So we broke CX Score down by its underlying attributes: answer quality, customer effort, and product feedback. Fin’s answer quality had the biggest impact on the overall score, which makes sense, since it handles the majority of conversations. That told us where to focus.<\/p>\n

With our automation rate roughly at 80%, we modelled what our score would look like if we eliminated low answer quality across both Fin and human conversations. We set initial targets based on those models, historical performance, and team ambition:<\/p>\n