- name
- discount-impact-calculator
- description
- Calculate how discounts affect revenue, margin, conversion assumptions, and allowable acquisition cost so teams can see whether a promotion is actually worth running.
Discount Impact Calculator
See the real commercial effect of a discount before launching it.
先交互，再计算
开始时先问：
- 这次 discount 是什么形式？
- 直接打折 - coupon - 满减 - second-unit discount
- 你们平时 discount impact 怎么算？
- 这次要不要把 conversion uplift 假设一起算进去？
- 是否要一起考虑退款率、AOV 变化、CAC 容忍度？
- 你想沿用现有逻辑，还是让我给推荐分析框架？
如果用户没有成型口径，先给推荐框架，再确认后计算。
Python script guidance
当用户给出结构化数字后：
- 生成 Python 脚本做 discount scenario 分析
- 输出 baseline vs discount scenario
- 返回 break-even / margin / CAC 变化
- 最后附上可复用脚本
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 discount 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.
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.
Resource
See references/output-template.md.