Know what to fix.
And exactly what it's worth.
Ranked fixes in $/year with confidence bands your team can defend. Reviews and survey in; causal estimates priced under assumptions you control.
- Input
- Reviews + survey
- Method
- Causal DML
- Output
- $/yr + band
$10M rev · +1pp sat ≈ +0.2% rev · assumptions set by your team
Product / experience quality
+11.0pp · 7% neg
~$221K
$210K to $232K
Service responsiveness
+9.3pp · 25% neg
~$185K
$174K to $197K
Digital experience
+9.0pp · 12% neg
~$179K
$167K to $191K
Environment & facilities
+8.9pp · 38% neg
~$178K
$166K to $189K
Environment is loudest in reviews; quality pays more per point fixed. Share ≠ worth.
Illustrative output
Example teardown
$10M rev · +1pp sat ≈ +0.2% rev · assumptions set by your team
| Fix | Lift | Worth / yrat your model |
|---|---|---|
QualityProduct / experience quality 7% neg | +11.0pp | ~$221K $210K to $232K |
ServiceService responsiveness 25% neg | +9.3pp | ~$185K $174K to $197K |
DigitalDigital experience 12% neg | +9.0pp | ~$179K $167K to $191K |
EnvironmentEnvironment & facilities 38% neg | +8.9pp | ~$178K $166K to $189K |
SupportStaff & support 29% neg | +7.7pp | ~$155K $141K to $169K |
OnboardingOnboarding / first use 24% neg | +6.8pp | ~$137K $124K to $149K |
Ready to scope a pilot for your unit?
Request early accessPilot snapshot
What an early teardown surfaces
Anonymized pattern from a multi-unit service brand pilot. Illustrative structure only — your ranked list is priced under assumptions your finance team sets.
Surprise
Environment ranked loudest in reviews but quality had the highest $/year per point fixed.
Decision
CX reordered Q3 initiatives using causal worth, not negative mention share.
Outcome
Finance signed off on one pilot fix with a documented confidence band and assumption sheet.
Composite anonymized pilot notes. Not a client endorsement.
Method
What runs under the teardown
Ingest
Reviews + driver-satisfaction survey for one brand unit. Validated CSV upload.
Tag & corroborate
Themes in open text, checked against survey items. Both must agree.
Estimate causally
Double machine learning estimates satisfaction lift if each issue is resolved.
Price under your model
You set revenue and how a satisfaction point maps to money.
Rank to fund
Ordered fix list with $/year and confidence band, ready to forward internally.
Typical VoC
“Theme X appears in 12% of negative feedback.” Volume does not say whether fixing X moves satisfaction or rides along with something else.
EvidenceOS
“Resolving service responsiveness lifts satisfaction +9.3pp, worth ~$185K/yr under your model, band $174K to $197K.”
Early access program
Onboarding multi-unit service brands for paid causal teardowns. First pilots include assumption workshops and deck-ready output for internal buy-in.
- CX / ops / finance buyers
- One unit → pilot
- Illustrative demo on site
One unit to start
Request a teardown
Three steps: request access here, send one unit's data when we reply, receive ranked fixes in $/year with confidence bands.
- 01
Request (this form)
Who you are, company, pilot context.
- 02
Send data (after we reply)
Reviews + satisfaction survey for one brand unit.
- 03
Get teardown
Ranked fixes in $/year under your assumptions.
Pilot, ongoing, and enterprise scopes. Pricing set with early customers.