Beyond Revenue is an advanced SQL-powered data analytics solution designed to identify and quantify profit leakage in a mid-size omnichannel retail company. Despite growing revenue, particularly through online channels, the company faces stagnating or declining profit margins. This project consolidates disparate data sources into a unified star schema data mart, delivers actionable insights through sophisticated SQL queries, and provides dashboard-ready views to empower data-driven decision-making.
The project focuses on three key areas of profit erosion:
- High Product Returns: Impacting gross margins across product categories and channels.
- Inefficient Marketing Spend: Low ROI from marketing campaigns.
- Operational Inefficiencies: High costs in inventory and returns processing.
By implementing this analytics framework, the company can:
- Pinpoint Profit Leaks: Identify specific products, channels, or customer segments driving losses.
- Optimize Marketing Budgets: Focus on high-ROI campaigns and customer segments.
- Reduce Return Rates: Address problematic products or customer behaviors.
- Enhance Operational Efficiency: Streamline inventory and returns processing.
- Increase Net Profit Margins: Ensure sustainable growth through data-driven strategies.
To uncover the hidden causes of profit erosion in a mid-sized retail operation by using advanced SQL queries to analyze:
- Customer behavior
- Return trends
- Campaign inefficiencies
- Operational costs
- Inventory mismatches
Before we jump into our work, it would be better if have a look at the data which will be used to complete our task.
- Who are our most valuable customers (lifetime value)?
- How many customers are at risk of churn?
- Which campaigns wasted marketing dollars?
- What products are returned most frequently?
- Where is inventory not matching demand?
- Which regions and store types drive the most profit?
- How does shipping delay affect returns?
- Does shipping delay increase return likelihood?
- Which products are facing shipping delays, and could they contribute to higher returns or churn?
- Which popular products are generating weak returns per unit sold?
Let's Have a look at our solution:
From this complete investigation, profit is draining due to multiple, interconnected factors that are actively eroding margins:
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Customer Attrition: A large segment of customers (nearly 77% at risk or dormant, Q2) signifies a leaky bucket. This problem is exacerbated if these at-risk customers include high-value individuals (Q1), leading to a significant loss of potential lifetime revenue.
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Ineffective Marketing Spend: Campaigns with abysmal ROAS and high CPA (Q3) are wasting valuable marketing dollars, failing to acquire profitable customers and contributing to the overall profit drain.
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High Product Return Costs & Low Profitability Per Unit: Specific products demonstrate unacceptably high return rates (over 30% for some, Q4) and also show extremely low net revenue per unit (Q10). These products are direct profit drains due to quality issues, expectation gaps, or inadequate pricing strategies.
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Operational Supply-Demand Mismatches: Critical inventory drift (100% stockout risk for key products, Q5) means lost sales opportunities. Simultaneously, potential overstocking of other products ties up capital and incurs holding costs.
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Logistical Inefficiencies: The clear link between shipping delays and higher return rates (Q8 & Q9) reveals that inefficient delivery directly translates into increased operational costs and reduced net revenue.
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Regional and Channel Variances: While warehouse stores generally perform better, the analysis hints at profitability disparities across states and store types (Q6 & Q7). This suggests localized inefficiencies or missed opportunities that prevent optimal profit generation.
The root of the profit drain is a combination of customer retention failures, inefficient resource allocation (marketing & inventory), product-level profitability issues, and sub-optimal operational logistics. To address this, the company must:
- Prioritize proactive churn prevention strategies targeting high-value customers.
- Immediately redirect or eliminate low-ROAS marketing campaigns.
- Conduct deep dives into high-return AND low-revenue-per-unit products to fix quality, pricing, or expectation gaps.
- Optimize inventory forecasting and replenishment to minimize both stockouts and holding costs.
- Invest significantly in streamlining shipping and fulfillment processes to reduce delays and associated returns.
- Analyze regional and channel-specific profit drivers to replicate successes and address underperformance.
By completing this project, we demonstrated:
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Database Design: Created normalized star-schema with well-defined primary and foreign keys
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Data Modeling: Mapped real-world business processes into dimensional models
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ETL Development (via Python): Loaded CSV files into SQL tables using Python-based data loaders.
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Advanced SQL: Window functions, CTEs, joins, aggregations, date logic
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Dimensional Modeling: Star schema design with fact & dimension tables
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Profitability Analysis: Return rates, campaign ROI, churn metrics, etc.
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ETL Simulation: Data summarization and transformation using SQL scripts
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Performance Optimization: Filtering, aggregation, best-practice query design
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Storytelling with Data: Progressive narrative from data to insights to action
- ❌ No data pipeline (ETL) orchestration — consider adding Airflow or Azure Data Factory
- ❌ Not connected to BI dashboards yet — integrate with Power BI or Tableau for stakeholders
- ❌ Not automated — can add SQL stored procedures to refresh data periodically
- 🔧 Build an interactive Power BI dashboard for real-time insights
- 📦 Package into an Azure Data Factory pipeline for end-to-end automation
- 🧪 Incorporate A/B testing or time-series forecasting (e.g., campaign ROI over time)
- 📈 Add KPI Alerts and generate automated reports via SQL Agent / Fabric
- 🤖 Future stretch goal: Train a churn prediction model using customer behavior data