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Omnichannel-Profit-Optimization-Engine

πŸš€ Live Demo

You can access the interactive simulation dashboard here: Profit Optimization Engine - Live Dashboard

🚨 Despite generating $1.98B in revenue, this company is losing $617M.

This project builds a decision engine to identify why and simulate how to fix it.

πŸ“Œ Executive Summary This project delivers a high-performance decision-support system for a global e-commerce entity operating in Turkey, Germany, and the UAE across D2C and B2B channels. Despite generating approximately $1.98 Billion in gross revenue, the company faced a critical $617.8 Million net loss.

This platform transforms static historical data into an interactive simulation engine, allowing executives to test "What-If" scenarios regarding marketing spend, return rates, and shipping shocks in seconds. Baseline-Profit Baseline Simulated-Waterfall Profit-Per-Order-Country-Segment

πŸ“‰ The Business Challenge (Data-Driven Insights) Comprehensive analysis identified several critical "Profit Leaks":

Erosive Return Rates: An overall 17.81% return rate significantly impacts the bottom line, resulting in a $178.6 Million loss in net revenue.

Segment-Specific Crises:

Efficiency Crisis (UAE D2C Entry): This segment exhibits the lowest efficiency with a profit margin plummeting to -34.89%.

Unit Loss Crisis (Germany B2B Premium): Analysis shows the highest loss per order occurs in this segment, reaching up to -$10,000 per transaction.

FX & Operational Pressure: With COGS and shipping costs heavily influenced by USD exposure, international logistics costs ($197.2M) create unpredictable margin pressure.

Marketing Inefficiency: High spending in segments with negative ROI prevents top-line growth from converting into sustainable profit.

🎯 Project Objectives & Strategic Goals The core mission is to provide an actionable framework for profit stabilization:

Granular Profitability Mapping: Visualizing net margins across Country x Channel x Segment using multidimensional heatmaps.

Real-time Scenario Simulation: Utilizing a Waterfall architecture to monitor how variables like marketing shifts or shipping cost shocks affect the final net profit.

Strategic Optimization: Identifying "Quick Win" segments (e.g., UAE D2C Entry) where targeted interventions in return rates can maximize investment efficiency.

πŸ› οΈ Technology Stack & Architecture Built on a modern data science stack designed for speed and scalability:

FastAPI: Serves the simulation logic as a high-performance, asynchronous backend API.

Streamlit: Provides a user-centric frontend with interactive sliders for real-time strategic testing.

Pandas & NumPy: Handles complex calculations across 300K+ rows of simulated transaction data.

Plotly: Powers dynamic Waterfall and Heatmap visualizations.

PostgreSQL / SQLAlchemy: Ensures production-ready data persistence and structured querying.

πŸ“Š Success Metrics Decision Agility: Reduced complex "What-If" analysis time from hours (manual Excel) to under 1 second.

Impact Potential: Simulations demonstrate that a 20% improvement in return rates can reduce total losses by over $140 Million.

Model Consistency:The generated data maintains internal consistency across financial metrics and business logic.

πŸ‘€ User Stories Marketing Manager: "If I increase the D2C Premium budget by 20% while reducing returns by 10%, how does the net margin shift?"

Operations Director: "How will a 15% shock in shipping costs (FX-driven) impact our German B2B segment viability?"

πŸ“‚ Data Architecture

The schema is designed to enable full traceability from order-level transactions to final profitability metrics.

orders: Core transaction data (Country, Channel, Segment, Revenue).

cost_structure: Detailed breakdown of COGS, labor, shipping, and marketing.

behavior: Tracking return status and payment cycles.

fx_rates: Historical and simulated currency conversion rates.

πŸš€ Recommended Strategy

Based on simulation results, the most effective path to reduce losses is:

  1. Prioritize return rate reduction in D2C channels β†’ Even a 20% improvement reduces losses significantly

  2. Reduce exposure to unprofitable B2B Premium segments β†’ High loss per order makes this segment structurally unsustainable

  3. Optimize marketing allocation β†’ Shift budget toward segments with lower return rates and higher efficiency

  4. Closely monitor shipping cost volatility (FX-driven) β†’ Implement dynamic pricing or cost controls

πŸ‘‰ Key Insight: Profitability improvement is not driven by revenue growth, but by cost and return optimization. The primary driver of losses is not insufficient revenue, but structural inefficiencies:

  • High return rates eliminate realized revenue
  • COGS nearly equals gross revenue, leaving no margin buffer
  • Fixed costs (marketing, logistics, labor) push the business into negative profitability

πŸ‘‰ This indicates a fundamentally broken unit economics model.

πŸ’‘ Final Takeaway: This is not a revenue problem it is a unit economics problem.

Without structural fixes, scaling revenue will only scale losses.

πŸ‘€ Author

Melek Δ°kiz
Building data-driven decision systems

πŸ“ TΓΌrkiye
πŸ”— LinkedIn: [(https://www.linkedin.com/in/melek-ikiz-520065373/)]