Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component...
npx skills add ashish47/claude-agent-skills --skill "analyzing-marketing-campaign"
Install specific skill from multi-skill repository
# Description
Analyze weekly marketing campaign performance data across channels. Use when analyzing multi-channel digital marketing data to calculate funnel metrics (CTR, CVR) and compare to benchmarks, compute cost and revenue efficiency metrics (ROAS, CPA, Net Profit), or get budget reallocation recommendations based on performance rules.
# SKILL.md
name: analyzing-marketing-campaign
description: Analyze weekly marketing campaign performance data across channels. Use when analyzing multi-channel digital marketing data to calculate funnel metrics (CTR, CVR) and compare to benchmarks, compute cost and revenue efficiency metrics (ROAS, CPA, Net Profit), or get budget reallocation recommendations based on performance rules.
Marketing Campaign Analysis
Automated analysis of multi-channel marketing campaign data from BigQuery.
Data Source
Query data from BigQuery using the bigquery:execute_sql tool.
Location: marketing-analytics-483823.marketing.campaign_performance
Schema:
| Column | Type | Description |
|--------|------|-------------|
| date | DATE | Campaign date |
| campaign_name | STRING | Campaign identifier |
| channel | STRING | Marketing channel |
| segment | STRING | Customer segment |
| impressions | INTEGER | Ad impressions (NULL for Email) |
| clicks | INTEGER | Number of clicks |
| conversions | INTEGER | Number of conversions |
| spend | FLOAT | Marketing spend in dollars |
| revenue | FLOAT | Revenue generated in dollars |
| orders | INTEGER | Number of orders |
Required Input
The user must specify a week to analyze. Accept formats like:
- "Dec 9-15" or "December 9-15, 2024"
- "2024-12-09 to 2024-12-15"
- "week of Dec 9" or "last week"
If the date range is ambiguous, ask the user to clarify before querying.
Querying Data
Always filter by date range—never pull the entire table. Example query structure:
SELECT
channel,
SUM(impressions) as impressions,
SUM(clicks) as clicks,
SUM(conversions) as conversions,
SUM(spend) as spend,
SUM(revenue) as revenue,
SUM(orders) as orders
FROM `marketing-analytics-483823.marketing.campaign_performance`
WHERE date BETWEEN '2024-12-09' AND '2024-12-15'
GROUP BY channel
Adjust the query as needed for the specific analysis (e.g., group by segment, include daily breakdown).
Data Quality Check
- Check for NULL values (Email channel won't have impressions)
- Verify no negative values in numeric columns
- Flag anomalies (e.g., conversions without clicks)
Funnel Analysis
Calculate per channel:
- Click Through Rate (CTR) = clicks / impressions × 100
- Conversion Rate (CVR) = conversions / clicks × 100
Compare to user-provided benchmarks, report difference in percentage points. If benchmarks not provided, use:
| Channel | CTR | CVR |
|---|---|---|
| Facebook_Ads | 2.5% | 3.8% |
| Google_Ads | 5.0% | 4.5% |
| TikTok_Ads | 2.0% | 0.9% |
| 15.0% | 2.1% |
Efficiency Analysis
Calculate per channel:
- Return On Ad Spend (ROAS) = revenue / spend
- Cost Per Acquisition (CPA) = spend / conversions
- Net Profit = revenue - Total Costs
- Total Costs = spend + (orders × Shipping Cost) + (revenue × Product Cost %)
- Defaults: Shipping Cost = $8/order, Product Cost = 35% of revenue
Compare to user-provided targets. Defaults:
- Target ROAS: 4.0x minimum
- Max CPA: $50
Output Format
Present results as tables with status indicators:
Funnel Analysis Table:
| Channel | CTR Actual | CTR Benchmark | CTR Diff | CVR Actual | CVR Benchmark | CVR Diff |
Efficiency Analysis Table:
| Channel | ROAS | Status | CPA | Status | Net Profit | Status |
Status indicators:
- ROAS: "[OK] Above" if >= target, "[X] Below" if < target
- CPA: "[OK] Below" if <= max, "[X] Above" if > max
- Net Profit: "[OK] Positive" if > 0, "[X] Negative" if <= 0
Follow each table with brief channel-by-channel interpretation.
Budget Reallocation
If user asks about budget reallocation, read references/budget_reallocation_rules.md for the complete decision framework including eligibility rules, performance-based actions, and constraints.
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.