- name
- offer-profitability-checker
- description
- Check whether an ecommerce offer is commercially viable after discounts, costs, refunds, and traffic economics are included. Use when teams need a fast profitability reality check before launch or scale.
Offer Profitability Checker
A quick commercial reality check for offers that look good on the surface but may not hold up economically.
先交互，再分析
开始时先问清楚：
- 这次你想评估的 offer 是什么？
- 直降 - bundle - upsell - 满减 - 包邮
- 你们平时怎么判断一个 offer “可做”？
- 看净利？ - 看 contribution margin？ - 看 CAC 容忍度？
- 你希望沿用现有口径，还是让我给一套推荐的电商 profitability 框架？
- 这次最关心的是：利润、放量空间、转化假设，还是风险边界？
如果用户没有明确口径，先给推荐分析框架，再让用户确认。
Python script guidance
当用户提供结构化数字后：
- 生成 Python 脚本建模
- 先展示假设与公式
- 再输出 baseline / scenario / sensitivity
- 最后返回可复用脚本
如果关键数据缺失，不要直接假装精确；继续追问或给推荐默认值并等待确认。
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
- Clarify the baseline commercial setup and evaluation logic.
- Model the scenario inputs that change order economics.
- Surface upside, downside, and sensitivity.
- Identify the biggest weak points or break-even pressure.
- Recommend whether to test, revise, or avoid the scenario.
- Return reusable Python script when structured inputs exist.
Output
- Baseline view
- Scenario result
- Margin / break-even implications
- Key risks and weak points
- Recommendation
- Python script
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.
- Do not pretend precision before assumptions are confirmed.
Resource
See references/output-template.md.