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ecom-promo-roi电子商务促销投资回报率

Agent Skill

ecom-promo-roi 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

392

周安装

16

GitHub Stars

125

下载量

125
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ecom-promo-roi(电子商务促销投资回报率)
来源仓库:https://github.com/asgard-ai-platform/skills
仓库路径:skills/ecom-promo-roi
安装命令:
npx skills add https://github.com/asgard-ai-platform/skills --skill ecom-promo-roi
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/asgard-ai-platform/skills --skill ecom-promo-roi

简介

促销投资回报率分析框架,聚焦增量销售额而非总营收,规避虚假增长陷阱。

  • 适用于满减、折扣等营销活动的财务评估,需区分自然销量与促销拉动部分。
  • 核心公式:Promo ROI = (Incremental Revenue × Margin - Promo Cost) / Promo Cost。
  • 需准确估算 baseline 销量与边际成本,避免因数据偏差导致策略误判。
  • ecom-promo-roi 属于待分类类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

Promotion ROI Analysis

Framework

IRON LAW: Measure INCREMENTAL Sales, Not Total Sales During Promo

A promotion that generates $100K in revenue during a sale week looks great —
until you realize $80K would have happened anyway (baseline). The incremental
lift is only $20K. If the discount cost $25K in margin, the promo LOST money.

Promo ROI = (Incremental Revenue × Margin - Promo Cost) / Promo Cost

Key Metrics

MetricFormula
Incremental RevenuePromo period revenue - Baseline revenue (what would have happened without promo)
Promo CostDiscount amount + marketing spend + operational cost
Promo ROI(Incremental Gross Profit - Promo Cost) / Promo Cost
Cannibalization Rate% of promo sales that would have occurred at full price
Pull-Forward Rate% of promo sales that are just earlier purchases (customers would have bought next week anyway)

Promo Type Comparison

TypeMechanismProsCons
% Discount20% off everythingSimple, high trafficErodes brand, trains discount-waiting
$ OffNT$200 off orders >NT$1000Drives AOV upLess exciting for low-AOV customers
BOGOBuy one get one freeMoves inventory fast50% margin hit, attracts deal-seekers
Gift with purchaseFree item with min spendProtects price perceptionCost of gift, may not drive incremental
Flash saleTime-limited deep discountUrgency, high engagementOperational stress, cannibalization
Loyalty pointsEarn/redeem pointsBuilds retention, deferred costComplex to manage, redemption liability

Analysis Steps

Phase 1: Establish Baseline

  • What would revenue have been without the promo? (prior period, prior year same period, or control group)
  • Account for seasonality, day-of-week effects, and trend

Phase 2: Calculate Incremental Impact

  • Total promo revenue - baseline = incremental
  • Subtract cannibalization and pull-forward estimates
  • Calculate gross profit on incremental (not revenue)

Phase 3: Calculate Full Cost

  • Discount dollars given up
  • Marketing spend (ads, email, creative production)
  • Operational cost (warehouse overtime, customer service spike)

Phase 4: Compute ROI and Decide

  • ROI > 0: Promo was profitable
  • ROI < 0 but strategic (new customer acquisition, inventory clearance): May still be justified
  • ROI < 0 with no strategic rationale: Don't repeat

Output Format

# Promo ROI Report: {Promotion Name}

## Promo Summary
- Type: {discount type}
- Period: {dates}
- Offer: {details}

## Results
| Metric | Value |
|--------|-------|
| Total Revenue (promo period) | ${X} |
| Baseline Revenue (estimated) | ${X} |
| **Incremental Revenue** | **${X}** |
| Incremental Gross Profit | ${X} |
| Promo Cost (discount + marketing + ops) | ${X} |
| **Promo ROI** | **{X%}** |

## Profitability Assessment
{Profitable / Unprofitable — with context}

## Recommendation
{Repeat / Modify / Discontinue — with rationale}

Gotchas

  • Baseline estimation is the hardest part: The accuracy of your ROI depends on how well you estimate what would have happened without the promo. Use prior year same period as a starting point, adjust for trend.
  • Post-promo dip is real: Sales often drop BELOW baseline after a promotion (customers pulled purchases forward). Include the dip period in your analysis.
  • Discounts are addictive: Customers learn to wait for sales. Track the % of revenue sold at full price over time — if it's declining, you have a discount dependency problem.
  • New vs existing customer mix matters: A promo that acquires 500 new customers at negative ROI may be worth it if their LTV justifies the acquisition cost. Track separately.

References

  • For A/B testing promotional offers, see references/promo-testing.md

适合场景

01

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02

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03

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能力概览

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能力 2

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能力 3

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能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.46%
按下载量换算47

Claude

32.98%
按下载量换算41

Cursor

18.27%
按下载量换算23

Gemini CLI

9.28%
按下载量换算12

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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