Usable with a quick edit9 of 10 checks passed
The recommendation, evidence, and guardrail analysis are sound and directly usable. A quick edit could add an explicit sample-ratio or novelty check, but the core reasoning would not change.
Output
To: Priya Nair, Head of Growth Recommendation: Do not ship B to 100% on Monday. Keep A as the default, stop new exposure to B, and retest a clearer pricing design.
B delivers a credible conversion lift, but it fails both pre-agreed guardrails. We should not override those thresholds because the headline metric improved.
- Conversion improved: Trial-to-paid rose from 11.8% to 13.1%, a +1.3 percentage-point lift (95% CI: +0.6 to +2.0).
- Retention deteriorated: Day-30 paid retention fell 4.5 points, from 84.0% to 79.5%, versus an allowed decline of no more than 2 points. The confidence interval excludes zero, although it does not establish that the true decline necessarily exceeds 2 points.
- Refunds breached the threshold: Requests increased from 212 to 301—42%, versus the permitted 10%. Confirm the rate using the agreed denominator, but the reported result is a clear warning under approximately equal exposure.
- Revenue does not justify the trade-off: Day-30 revenue per trial start increased only $0.19, and its confidence interval crosses zero. We have not established a revenue benefit.
The design provides a plausible explanation, not proven causation: leading with “$9.99/month” while hiding the monthly option may obscure that customers must pay $119.99 upfront. Higher conversion could therefore reflect misunderstanding rather than stronger purchase intent.
Next steps: Validate cohort maturity—the experiment ran 21 days, so we need to understand how day-30 outcomes were obtained—and check refund reasons, plan mix, and billing-related complaints. Then test a version that prominently states “$119.99 billed annually ($9.99/month equivalent)” and keeps the $14.99 monthly option visible.
Monday’s defensible position: “B increased conversion, but failed our customer-outcome guardrails without demonstrating a revenue gain. We’ll preserve the learning, fix pricing clarity, and require mature results that meet the agreed thresholds before scaling.”
Grades and run details
Decision model 95 · LLM judge 10 of 11 checks
Decision model checks
- passUses the supplied evidence correctly21%
- passAddresses the actual decision96%
- passRespects explicit constraints42%
- passIdentifies material uncertainty85%
- partialAvoids unsupported claims28%
- passProduces the required deliverable85%
- passChecks guardrails before declaring a winner84%
- passSeparates effect from explanation89%
- passTrusts the data before reading it45%
- passGets the base of every number right83%
Run
- Run
- #1
- API response time
- 14 s
- Submitted
- 29 Sept 2026