DTC Campaign Performance Analyzer
Analyze DTC cosmetics campaign performance and optimize CAC across all channels
What You Can Do
You can systematically evaluate multi-channel DTC cosmetics campaigns to identify optimization opportunities, calculate true customer acquisition costs (CAC) vs. customer lifetime value (CLV) ratios, and pinpoint underperforming assets. This skill helps you move beyond vanity metrics to diagnose root causes of underperformance, segment performance by customer cohort, and recommend specific optimizations that increase ROAS and profitability across paid social, email, search, and influencer channels.
Features
evaluate paid social, email, search, influencer, and organic data in one coherent framework
account for creative costs, platform fees, and blended acquisition across traffic sources
identify where drop-off occurs and which channels drive highest-intent traffic
determine which products and categories drive true margin vs. volume
compare performance by customer type (new vs. repeat), acquisition source, and product category
interpret A/B test results and identify winning asset patterns
recognize when paid channels show engagement decay and ROI erosion signals
transform raw data into executive-ready insights with prioritized action items
Example Output
Example 1: Multi-Channel CAC Analysis
Input: October campaign data across TikTok Ads, email, Google Search, and influencer partnerships with spend, conversions, and AOV by channel.
Output:
- TikTok Ads: $18 CAC, 3.2x ROAS (highest intent, but audience saturation evident)
- Google Search: $12 CAC, 4.8x ROAS (most efficient channel, opportunity to increase budget)
- Email: $2.40 CAC, 6.1x ROAS (strong repeat customer activation)
- Influencer: $45 CAC, 1.9x ROAS (brand awareness driver, not efficient for direct conversion)
Recommendation: Shift $5K from TikTok to Google Search; test influencer partnerships as awareness funnel feeding email list rather than direct conversion.
Example 2: Product-Level Profitability Breakdown
Input: Campaign performance data segmented by product category (lip products, skincare sets, brushes) with COGS and margin data.
Output:
- Lip products: 45% margin, 2.1x ROAS (profitable but low AOV, drives repeat purchase)
- Skincare sets: 58% margin, 3.8x ROAS (premium price point, longer decision cycle, highest CLV)
- Brushes: 32% margin, 1.4x ROAS (commodity bundling, recommendation: pause standalone promotion)
Recommendation: Allocate 60% of paid budget to skincare sets; position brushes as upsells, not primary campaign drivers.
What's Included
- SKILL.md instruction file: complete framework for analyzing DTC cosmetics campaign performance
- Multi-channel analysis template: structured format for evaluating paid social, email, search, and influencer data side-by-side
- CAC calculation worksheet: spreadsheet-ready formulas for true customer acquisition cost including hidden costs
- Cohort segmentation checklist: step-by-step guide to segment performance by customer type, source, and product
- Action prioritization framework: decision tree to rank optimization opportunities by impact and effort
Who It's For
- DTC Cosmetics Brand Managers — optimize campaign spend across all channels to maximize profitability
- Performance Marketing Directors — diagnose underperforming channels and make data-backed budget allocation decisions
- E-commerce Marketing Managers — improve CAC efficiency and customer lifetime value across beauty brands
- Growth Analysts — segment performance data and identify cohort-specific optimization opportunities
- Quarterly Business Review Owners — transform raw data into executive narratives and strategic recommendations
Best For
- Weekly/monthly campaign performance review and prioritization
- Multi-channel attribution and CAC benchmarking across paid, email, and organic
- Root cause analysis when campaigns underperform vs. external factors
- Channel budget allocation decisions (increase, pause, or optimize spend)
- Product-level profitability assessment and promotion strategy refinement
- Creative A/B testing interpretation and statistical significance evaluation
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