A 90% CSAT score. And still the escalations kept coming.
Five million support conversations a year. Surveys collected from eight to ten percent of them. A score that looked strong. A business that kept getting escalations from the ninety percent nobody was measuring.
5M
Conversations measured
Not just the 8–10% survey sample
90%+
Model accuracy
At deployment, up from 60% at start
↓
Escalations
Proactive outreach replaced reactive firefighting
The CSAT score was tracking at 89 to 90 percent. By any benchmark, that's strong. But the Senior VP of Global Customer Support had a problem he couldn't solve with that number: escalations kept arriving from customers who had never filled in a survey. Nine out of ten conversations — more than four million a year — generated no feedback at all. The business had no idea how they went.
The insight
“The transition from entry state to exit state is the real measure of support quality. Not whether the customer fills in a form — but whether they left the conversation better or worse than they arrived.”
A machine learning sentiment model trained on conversation transcripts — voice and chat — that measures the emotional arc of every interaction from beginning to end
A training approach built on the conversations that already had CSAT responses, giving the model ground truth to learn from before being applied to the full five million
A personalised agent development layer — using the same conversation-level data to identify which agents were strong at which problem types, and building learning paths targeted to actual gaps
The support team could identify — in near real time — which interactions had gone poorly and reach out proactively before frustration became a complaint. The overall satisfaction score held and improved directionally. Agent development became personalised by conversation-level performance data rather than generic training curricula.
“What made this project work wasn't the machine learning. It was starting from first principles: why does anyone contact support, what state are they in when they do, and what does a good outcome actually look like? The technical solution followed from that thinking.”
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