aov-uplift-simulator

# AOV Uplift Simulator Model how AOV could move under different bundle, upsell, and pricing assumptions before you change the live offer. ## Solves Many ecommerce teams make pricing or offer decisions with incomplete economics: - they see revenue upside but not margin drag; - they model one variable but ignore knock-on effects; - they test offers without clear guardrails; - they scale offers before checking break-even logic. Goal: **Turn offer assumptions into a clearer economic view that is easier to evaluate and act on.** ## Use when - You want to compare offer scenarios before launching - A discount, bundle, or upsell idea sounds good but needs economic validation - Growth teams need a faster way to pressure-test merchandising decisions - Teams want clearer go / watch / no-go logic before scale ## Inputs - Core commercial assumptions relevant to the scenario - Price and cost structure - Margin or refund assumptions - Traffic / conversion or attach-rate assumptions - Constraints or guardrails ## Workflow 1. Clarify the baseline commercial setup. 2. Model the scenario inputs that change order economics. 3. Surface upside, downside, and sensitivity. 4. Identify the biggest weak points or break-even pressure. 5. Recommend whether to test, revise, or avoid the scenario. ## Output 1. Baseline view 2. Scenario result 3. Margin / break-even implications 4. Key risks and weak points 5. Recommendation ## Quality bar - Output should be commercially interpretable, not just a raw formula dump. - Recommendations should stay grounded in ecommerce economics. - Weak points should be clearly separated from upside assumptions. - The result should help a team decide what to test next. ## Resource See `references/output-template.md`.