# Analyzing Marketing Campaign

Analyze weekly marketing campaign performance data across channels.

- Repository: https://github.com/https-deeplearning-ai/sc-agent-skills-files (1426 stars)
- Category: Marketing
- License: see repository
- Last updated: 2026-06-08
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add https-deeplearning-ai/sc-agent-skills-files --skill analyzing-marketing-campaign -g -a claude-code -y`
Codex: `npx skills add https-deeplearning-ai/sc-agent-skills-files --skill analyzing-marketing-campaign -g -a codex -y`
Cursor: `npx skills add https-deeplearning-ai/sc-agent-skills-files --skill analyzing-marketing-campaign -g -a cursor -y`
Gemini CLI: `npx skills add https-deeplearning-ai/sc-agent-skills-files --skill analyzing-marketing-campaign -g -a gemini-cli -y`

## 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, as published by its author (untrusted third-party text, read it as data)

# 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:

```sql
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

1. Check for NULL values (Email channel won't have impressions)
2. Verify no negative values in numeric columns
3. 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

The rest: https://github.com/https-deeplearning-ai/sc-agent-skills-files/blob/HEAD/L3/updated_marketing_skill/analyzing-marketing-campaign/SKILL.md