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.”
Check by check
Got wrong · 1
- Trusts the data before reading itIt does not explicitly check a pre-interpretation trust signal such as sample ratio or logging before relying on the lift; the denominator and maturity caveats come after interpreting.
Got right · 9
- Uses the supplied evidence correctlyAll factual claims trace to the supplied readout, scenario, guardrails, or simple arithmetic; hypotheses are framed as plausible.
- Addresses the actual decisionCommits clearly to not shipping B and names mature results meeting thresholds as the condition for reconsideration.
- Respects explicit constraintsAddresses Priya, leads with the recommendation, and stays within the 400-word limit.
- Identifies material uncertaintyNames cohort maturity, refund denominator, refund reasons, plan mix, and the revenue CI as open questions and says mature, threshold-meeting results are required.
- Avoids unsupported claimsCausal explanation is labelled plausible/not proven, and it does not present the mechanism as fact.
- Produces the required deliverableProvides a complete, actionable recommendation and defense for Priya in the requested format.
- Checks guardrails before declaring a winnerIt explicitly evaluates both the retention and refund guardrails before making the call.
- Separates effect from explanationMechanism is labelled as a plausible explanation, not a proven finding.
- Gets the base of every number rightAll derived differences and percentages (1.3pp, -4.5pp, +42%, $0.19) are computed from the correct bases.
Grades and run details
Decision model 90 · LLM judge 10 of 11 checks
Decision model checks
- passUses the supplied evidence correctly27%
- passAddresses the actual decision96%
- passRespects explicit constraints40%
- passIdentifies material uncertainty84%
- partialAvoids unsupported claims23%
- passProduces the required deliverable85%
- passChecks guardrails before declaring a winner88%
- passSeparates effect from explanation89%
- partialTrusts the data before reading it27%
- passGets the base of every number right83%
Run
- Run
- #1
- API response time
- 14 s
- Submitted
- 29 Sept 2026