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Commercial Intelligence Framework: Multi-Channel Revenue & Margin Audit

Focus: Revenue Recovery, Unit Economics (CM3), and Growth Diagnostics

📌 Executive Summary

This repository houses a series of advanced SQL-driven audits designed to stabilize and optimize commercial performance across a fragmented e-commerce landscape. The framework was engineered to bridge the gap between "Gross Reporting" and "Net Profitability," specifically targeting revenue volatility and hidden operational leakages across 5+ marketplaces and a Direct-to-Consumer (DTC) storefront.


🛠️ Key Business Workstreams

1. Revenue Volatility Diagnostic (Supply-Chain vs. Market Demand)

  • The Problem: Observed a sharp 43% MoM contraction in top-line revenue, initially flagged as a potential decline in market share.
  • The Deep-Dive: Utilized SQL window functions to decouple Average Order Value (AOV) from total volume.
  • The Finding: Analysis confirmed that Customer Intent (AOV) grew by 5% (₹1,450 to ₹1,522). The revenue collapse was fundamentally an Inventory Stock-Out crisis, with availability in core "Hero SKUs" dropping from 94% to 38%.
  • Business Impact: Prevented a redundant ₹20L shift in marketing strategy and redirected focus toward Vendor Lead-Time (VLT) Optimization.

2. Margin Leakage & Settlement Reconciliation

  • The Problem: Discovered a widening delta between "Platform Sales" and "Final Bank Settlement."
  • The Deep-Dive: Architected a reconciliation engine to audit marketplace commission structures and logistics billing against our internal Order Management System (OMS).
  • The Finding: Identified a 15% Net Margin Leakage caused by Promotion Stacking. Platform-level automatic discounts were triggering concurrently with brand-level coupons, resulting in several thousand "Negative Margin" transactions.
  • Business Impact: Secured 15% in recovered margins by implementing a rigid promo-exclusion logic in the checkout backend.

3. Channel Profitability & Unit Economics (CM3)

  • The Problem: Scaling sales volume without a proportional increase in net EBITDA.
  • The Deep-Dive: Transitioned the reporting layer from "Gross Revenue" to Contribution Margin 3 (CM3)—accounting for COGS, Marketplace Commissions, Last-Mile Logistics, and CAC.
  • The Finding: The DTC channel delivered a 22% higher net margin per unit than the highest-volume marketplace, despite a lower traffic footprint.
  • Business Impact: Justified a 20% strategic budget reallocation to high-margin channels, optimizing for "Quality of Revenue" over "Gross Volume."

💻 Technical Methodology

  • Advanced Logic: Deployment of Common Table Expressions (CTEs) for modular logic and Window Functions (LAG, SUM OVER, RANK) for time-series velocity and trend detection.
  • Data Governance: Integrated NULLIF, COALESCE, and explicit type-casting to manage the "dirty data" reality of marketplace settlements, ensuring 100% financial accuracy.
  • Strategic KPI Engineering: Developed dynamic metrics for Days of Inventory Remaining (DOIR) and Basket Depth to provide forward-looking commercial signals.

🚀 Scalability & Future Roadmap

  • Retention Cohorts: Analyzing LTV (Lifetime Value) variance between discount-acquired vs. full-price customers.
  • Price Elasticity Modeling: Granular SQL modeling to find the "Point of Diminishing Returns" on promotional discounting.
  • Logistics SLA Audit: Tracking 3PL performance to claim refunds on delivery-time breaches.

About

A dedicated repository for advanced SQL-driven commercial analysis. Focusing on revenue diagnostics, margin recovery, and unit economics across multi-channel e-commerce datasets.

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