Agent Skills
› gooseworks-ai/goose-skills
› analyze-performance-revenue-join
analyze-performance-revenue-join
GitHub将清洗后的商店或CRM数据与广告支出合并，构建业务事实表，计算MER、新客成本及超额索赔比率，用于分析性能报告。
触发场景
需要合并销售/CRM数据与广告花费以生成业务事实表
计算营销投资回报率(ROI)和新客户获取成本
安装
npx skills add gooseworks-ai/goose-skills --skill analyze-performance-revenue-join -g -y
SKILL.md
Frontmatter
{
"name": "analyze-performance-revenue-join",
"version": "0.1.1",
"description": "Step of the analyze-performance playbook: joins cleaned store or CRM numbers with ad spend to build the business-truth table, MER, new-customer cost and the overclaim ratio. Run analyze-performance instead; not for direct use."
}
Revenue join (atom)
Load: ../analyze-performance/references/revenue-join.md.
Inputs: clean store/CRM numbers; total spend per platform; platform claims; break-even MER and allowable CAC (campaign brief economics box / measurement contract); last week's report (the story the marketer picked).
Do:
- Business-truth table: this week, last week, 4-week average, same week last year.
- MER, new-customer MER, nCAC, contribution after ads — each vs break-even / allowable / target.
- Overclaim ratio, like-for-like as in the reference (claimed purchases ÷ store orders, or claimed revenue ÷ store gross; a floor when a claimant is missing), with its 4-week trend.
- Marginal: Δ new-customer revenue ÷ Δ spend vs last week and vs the 4-week average.
- If the platform and blended stories point different ways: write both at the same grain, each ending "if true, you'd…", a recommendation, and the cheapest test that would settle it. Carry forward the story the marketer picked last time unless a test changed it.
- B2B: pipeline chain and cost per qualified lead / opportunity instead of revenue.
Output: the report's Business truth, overclaim line and Two stories sections.
版本历史
- 1add09b 当前 2026-11-02 21:49
