Analyzing Marketing Campaign
by https-deeplearning-ai
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 Details
Repository Files
2 files in this skill directory
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.
Input Requirements
Expects campaign data in CSV format with these columns:
- date: Campaign date
- campaign_name: Campaign identifier
- channel: Marketing channel
- segment: Customer segment
- impressions: Ad impressions (empty for Email channel)
- clicks: Number of clicks
- conversions: Number of conversions
- spend: Marketing spend in dollars
- revenue: Revenue generated in dollars
- orders: Number of orders
Data Quality Check
- Check for missing values and empty cells (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 and provide brief interpretation for each channel. If benchmarks are not provided, use these historical values:
| 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 %)
- Unless user specifies different values, use:
- Shipping Cost: $8 per order
- Product Cost: 35% of revenue
Compare to user-provided targets. If not provided, use these 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 highlighting key insights and recommended actions.
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.
Related Skills
Xlsx
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Clickhouse Io
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
Clickhouse Io
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
Analyzing Financial Statements
This skill calculates key financial ratios and metrics from financial statement data for investment analysis
Data Storytelling
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
Kpi Dashboard Design
Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use when building business dashboards, selecting metrics, or designing data visualization layouts.
Dbt Transformation Patterns
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
Sql Optimization Patterns
Master SQL query optimization, indexing strategies, and EXPLAIN analysis to dramatically improve database performance and eliminate slow queries. Use when debugging slow queries, designing database schemas, or optimizing application performance.
Anndata
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.
Xlsx
Spreadsheet toolkit (.xlsx/.csv). Create/edit with formulas/formatting, analyze data, visualization, recalculate formulas, for spreadsheet processing and analysis.
