🍃 Fresh Segments – Customer & Product Analytics

SQL-Based Customer Segmentation Project

The Fresh Segments project analyzes retail customer behavior and product preferences using SQL analytics. Leveraging advanced queries, joins, and window functions, the project identifies customer segments, product trends, and purchase patterns, enabling businesses to target campaigns, optimize inventory, and enhance customer engagement effectively.

Fresh Segments SQL Project

Project Highlights

  • Customer Segmentation

    Grouped customers based on purchase frequency, spend value, and product preferences using SQL CASE and ranking logic.

  • Product Performance

    Tracked top-selling and underperforming products across regions using aggregation, joins, and window functions.

  • Revenue Analysis

    Calculated total revenue, profit margins, and seasonal trends to guide inventory and marketing decisions.

  • Customer Retention Insights

    Analyzed repeat vs new customers and retention trends using SQL date and ranking functions.

  • Regional & Segment Analysis

    Compared performance across segments and locations to prioritize high-value areas and campaigns.

About the Project

Fresh Segments Overview

Overview

The Fresh Segments project helps retail businesses understand customer behavior and product performance. By integrating sales, customer, and product data, SQL analytics uncover patterns and segment customers effectively. These insights enable targeted marketing, optimized inventory, and improved customer experience.

SQL Process & Data Modeling

  • 📥 Extract: Combined sales, customer, and product data from multiple tables.
  • 🧹 Transform: Cleaned and standardized data fields, handled missing values, and prepared for analysis.
  • 🔍 Analyze: Applied SQL ranking, aggregation, and joins to segment customers and identify top products.
  • 📊 Model: Designed a data mart linking customers, segments, and product metrics for reporting.
Fresh Segments SQL Model
Fresh Segments Insights

Key Insights

  • 👥 Top 20% of customers contributed to 55% of total revenue.
  • 📈 Seasonal product trends showed higher demand in summer for fresh produce categories.
  • 💡 Repeat customers had 30% higher average order value than new customers.
  • 🌍 Regional segmentation highlighted high-value areas for marketing campaigns.

When:
2025

Mode:
SQL Data Analysis

Dataset:
Sales, Customers & Product Data

Focus:
Customer Segmentation & Product Analytics

Project Snapshots

Business Impact

📊 Allowed businesses to identify high-value customer segments and target campaigns effectively.

💬 Improved product stocking decisions based on customer demand patterns.

🚀 Enabled marketing and sales teams to boost revenue through segmentation-driven strategies.

Challenges & Learnings

⚙️ Managing multiple tables and ensuring accurate segmentation using SQL.

🧩 Creating actionable insights from large-scale transactional data efficiently.

💡 Strengthened skills in SQL analytics, data modeling, and customer behavior insights.

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