# 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.

- Skill: `leooooooow/aov-uplift-simulator` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add leooooooow/aov-uplift-simulator`
- Raw SKILL.md: https://api.skillmd.com/api/skills/leooooooow/aov-uplift-simulator/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: Leooooooow (https://skillmd.com/u/leooooooow)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/leooooooow/aov-uplift-simulator

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# 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`.