An end-to-end Excel-based marketing analytics system built to analyze campaign performance, identify funnel inefficiencies, and optimize budget allocation across multiple channels.
Brand: Voss Collective (Simulated) Industry: Streetwear Fashion E-commerce (mid-tier — not luxury, not low-cost) Business Model: Direct-to-Consumer (DTC, Shopify-like store) Markets: United States, United Kingdom, Canada Timeframe: Full-year performance analysis (Jan – Dec 2025)
Disclaimer: This project uses a fully simulated dataset and fictional brand created for portfolio purposes. The goal is to demonstrate real-world marketing analytics and decision-making skills.
Voss Collective invested $2.6M across Google, Meta, and TikTok, generating:
- $13.3M revenue
- 5.12x blended ROAS
- $2.99M estimated real profit
- 197K total purchases
Despite strong performance, analysis uncovered:
- Major funnel drop-off at Checkout → Purchase (48.52% vs 50–70% benchmark)
- Uneven efficiency across channels and markets
- Missed profit opportunities due to suboptimal allocation
- Identified opportunity to increase profit via 10–15% budget reallocation
- Estimated +$5K to $50K incremental profit potential
- Highlighted checkout optimization as the highest ROI lever
How can we:
- Maximize real profitability (not just ROAS)?
- Identify where customers drop in the funnel?
- Allocate budget across channels & markets more efficiently?
This project combines multiple analytical approaches:
- Funnel Analysis (Click → Purchase)
- KPI Benchmarking (Industry standards)
- Channel & Country Performance Comparison
- Scenario Simulation (What-if analysis)
- Budget Optimization Modeling
- Pivot Tables
- Advanced Formulas
- KPI Modeling
- Scenario Simulation
- Marketing Analytics
- Funnel Analysis
- Budget Optimization
- Data Storytelling
- Business Decision Support
- Google = top-performing channel (ROAS 5.22x, $1.03M profit)
- Meta & TikTok perform closely but with slightly lower efficiency
- Checkout → Purchase = 48.52% (below 50–70% benchmark)
- Largest leakage point in the entire customer journey
- Funnel CVR = 2.85%
- Slightly below benchmark (2.9%–3.4%)
- US, UK, Canada performance is relatively balanced
- Minor inefficiencies suggest optimization potential
- Improve UX, payment options, trust signals
- Even +5% improvement = significant revenue increase
- Shift budget toward top-performing channels (Google)
- Avoid over-scaling to prevent diminishing returns
- Test 5–10% budget reallocation scenarios
- Validate before scaling
Simulated budget shift:
- From: Meta
- To: Google
- Increase in estimated revenue and profit
- Slight improvement in blended ROAS
Demonstrates data-driven decision-making
data/
└── voss_collective_final.xlsx
assets/
├── dashboard.png
├── dashboard_2.png
├── dashboard_3.png
├── budget_optimizer_4.png
├── scenario_simulator_5.png
├── campaign_ranking_6.png
└── funnel_analysis_7.png- Simulated dataset (not real company data)
- No user-level granularity (aggregated metrics only)
- No attribution modeling (assumes simplified attribution)
If implemented in a real business:
- Build Power BI dashboard for real-time tracking
- Add customer-level analysis (LTV, retention, cohorts)
- Run A/B tests on checkout flow
- Integrate CAC vs LTV modeling
Ziad Diab Marketing Analyst | E-commerce Data Analyst