CSM, onboarding, renewals

Hiring customer success? Here's how we evaluate.

We score how candidates own outcomes under pressure — not whether they can recite the right talking points. The model runs the conversation; the rubric is ours.

See the rubric

What we score

The dimensions, not the playbook.

We don't publish the exact criteria, weights, or sub-probes — that's how candidates would game the rubric. Here's what every customer success candidate is scored against.

Situational judgment
When a churn-risk account, a missed SLA, and an exec escalation all land in the same morning, what do they do first — and why? We score the reasoning, not the answer.
Ownership of outcomes
Whether they take responsibility for the customer's result or hand off to someone else. We probe what they would have done differently, and whether they would have flagged the risk earlier.
Communication in escalation
How they de-escalate without over-promising. We score whether they hold the line on what's actually possible, or whether they cave to keep the call peaceful.
Business empathy
Whether they understand the customer's business — not just the product they bought. CSMs who can't connect product usage to customer outcomes get surfaced.

Sample scenarios

What candidates actually face.

Two illustrative scenario types — the actual prompts vary per session and stay private to your tenant.

Scenario 1
Walk through a churn-risk account.
The AI presents an account with declining usage, an unresponsive sponsor, and a renewal in 60 days. We score how the candidate prioritizes, what they ask, and which lever they pull first. There's no single right answer — but there are bad ones.
Scenario 2
Onboarding hand-off scenario.
An account is being handed from sales to CS with mismatched expectations. We score whether the candidate names the gap to the customer directly or papers over it — and what they do to course-correct in the first 30 days.

Integrity signals

What we watch for — and what stays private.

We name the signals we capture, but not how we weight or threshold them. That's the part that breaks if we publish it.

  • Every session is recorded — audio, video, and full transcript — and retained per your tenant policy.
  • Every score ships with an ML confidence band. Low-confidence scores are flagged for human review before the candidate is decided on.
  • Response timing relative to a tenant baseline is tracked; rehearsed-sounding, perfectly-timed answers get surfaced for review.
  • Admin labeling lets your CS leaders flag interviews where the AI's read missed nuance a senior CSM would catch.
  • We never train shared models on your candidate data.

What we measure

The outcome you can defend.

Situational-judgment score, communication-in-escalation score, and ownership score for every candidate — plus a confidence band on each. We measure how our 'strong hire' candidates perform on retention metrics in their first 90 days, and recalibrate when the rubric and the outcome diverge.

We frame these as what we measure, not as customer-attributed metrics.

Want to see how this rubric scores a real candidate?

An expert will walk you through a live customer success interview transcript — including how the integrity signals played out — in 15 minutes.

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Other domains:EngineeringSales
SOC 2 Type II — In progressGDPR-readyTenant-isolated infrastructureData residency: USQuarterly bias auditsNo training on your candidate data