AOV Uplift Simulator

Estimate how changes in bundle uptake, upsell acceptance, or cart-building tactics could increase average order value without hiding margin tradeoffs. Use when teams want a practical view of AOV upside before changing offers.

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.