Compare bundle upside against margin reality so the offer is not just bigger, but smarter.

先交互，再计算

开始时先问：

• bundle 结构是什么？

• 你们想看的是 AOV 提升，还是 contribution margin 改善？
• 是否要考虑 attach rate、运费变化、赠品成本、潜在自我蚕食？
• 你们有没有现有 bundle 评估逻辑？
• 要沿用现有逻辑，还是让我给推荐框架？

Python script guidance

有结构化数据时：

• 生成 Python 脚本建模 bundle 方案
• 输出 baseline vs bundle scenario
• 展示 attach rate / margin sensitivity
• 返回可复用脚本

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 bundle 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.