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.