Use cases by industry

Operational problems we solve, in the language your team already uses

Customer case study

American Bath Group: one intelligence layer across email, voice, and surveys.

Read the case study

B2B SaaS

Common challenges

Churn rate rising but root cause is a debate every QBR

Support escalations up, no clear attribution to a product or process cause

Repeat contacts on onboarding failures eating handle time

CS team is large and growing but retention isn't improving

How StatQuestions helps

Churn root cause from support tickets

Export 12 months of tickets from your helpdesk. Issues are classified and joined to churn data. You see which failure patterns statistically correlate to cancellations, and which ones are just noise. The output is a prioritized fix list with ARR impact attached.

Key metrics:Repeat contact rateChurn correlationEscalation driversARR at risk

Onboarding failure analysis

Customers who churn in the first 90 days leave a paper trail in your tickets. Upload their contacts separately. The pattern that caused early churn is almost always identifiable, and almost always fixable.

Key metrics:Time-to-first-contactOnboarding failure themesRepeat contacts on setup issues

Repeat contact rate by tier

Join ticket data to account tier or ARR segment. Find out whether your enterprise accounts have to come back more often than SMB, and what issue types are driving the gap.

Key metrics:Repeat contact rate by tierRework cost per segmentSLA breach causation

Industry benchmarks

Published third-party research on this industry, context for what StatQuestions looks for, not StatQuestions results.

20–40%
Typical repeat contact rate
of inbound volume, most of it traceable to 3–5 unresolved root causes.
Source: SQM Group
$6–14
Cost of rework
per ticket in a well-run organization; repeat contacts roughly double that.
Source: Forrester
33%
Churn from service failure
of B2B churn is attributable to service experience rather than product.
Source: Gartner

Statistical methods that drive results

T-tests
Compare segments with statistical significance (e.g. West BU vs. East BU escalation rate)
Correlation analysis
Identify which factors actually drive outcomes (e.g. handle time vs. repeat contact rate)
Driver analysis
Rank importance of multiple factors to prioritize improvements
Effect sizes
Measure practical significance, not just statistical (e.g. small vs. large impact)

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