What sentiment analysis can't see: Measuring whether customers were helped, and what went wrong, across 70,000 support conversations
Most companies read their customer support data at scale using sentiment analysis, which measures how customers sound rather than whether they were satisfied with the result. We tested a richer alternative on 70,450 support conversations from a leading online fundraising platform: alongside tone, we used GPT-5.4 to estimate each customer's satisfaction and to flag whether they reported a concrete problem, then validated all three readings against the 1-to-5 ratings customers left on the conversations they rated. The satisfaction estimate tracked those ratings far better than sentiment did, correlating at 0.47 against 0.36 and flagging unhappy customers with far fewer false alarms. The str...
| Authors | Jason Potteiger |
|---|---|
| Primary category | cs.CL |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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