01 · CSAT coverage

CSAT for every single conversation.

Only about one customer in ten leaves a rating, and the ones who do skew to the extremes — the delighted and the furious. Supportman reads every closed conversation and predicts the rating the customer would have left, so every single conversation carries a satisfaction signal.

CSAT coverage · this week

71 conversations

Satisfaction signal100%

Rated by customers 11% AI-predicted 89%

71 of 71 conversations have a CSAT signal · avg 4.4 / 5

What you get

Real-time ratingsEvery real CSAT posts to Slack the second it lands — score, assignee, and the customer’s remark.

AI-predicted CSATA predicted rating for the ~90% of customers who never rate, on every closed conversation.

100% coverageA satisfaction signal on every single conversation — not a one-in-ten sample.

Grounded in your real ratingsPredictions sit alongside the ratings customers actually leave, so you can see how the two track.

Why sampled CSAT misleads

A 10% response rate doesn’t just make your CSAT noisy — it makes it biased. Customers with strong feelings rate; the quiet middle doesn’t. Teams end up steering quality decisions, agent reviews, and exec reporting off a skewed sliver of reality.

Coverage fixes the denominator. When every conversation carries a signal, a bad week looks bad and a good week looks good — no more wondering whether the number moved or the sample did.

Where the predictions show up

Predicted CSAT appears in the conversation’s Slack thread alongside the AI evaluation, rolls into the weekly team and per-agent reports, and feeds the dashboard trends — everywhere a real rating would.

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