QuickKart Sales & Customer Analytics

Project Overview

This project focuses on analyzing QuickKart’s sales performance and customer behavior using SQL, Python, and Power BI. The objective of this project was to identify key business insights related to customer purchasing patterns, customer retention, discount effectiveness, and revenue trends.

The project involved SQL-based querying, Python Exploratory Data Analysis (EDA), and Power BI dashboard development to derive meaningful business insights and recommendations.

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Problem Statement

QuickKart aimed to better understand customer purchasing behavior, sales performance, and customer retention patterns. The business lacked clarity on whether factors such as discounts, customer ratings, city-wise behavior, and customer segments significantly influenced revenue and repeat purchases.

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Tools & Technologies Used

• SQL Server (SSMS)
• Python
• Pandas
• Matplotlib
• Power BI
• Jupyter Notebook

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Project Workflow

1. Data Extraction & Querying using SQL
2. Exploratory Data Analysis (EDA) using Python
3. Dashboard Development using Power BI
4. Business Insights & Recommendations

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Power BI Dashboard

Executive Overview Dashboard

• Total Revenue
• Total Orders
• Average Order Value
• Total Customers
• Monthly Revenue Trend
• Revenue by City
• Customer Segment Revenue
• Customer Churn Risk

Customer Insights Dashboard

• Repeat Purchase %
• Average Customer Rating
• Loyal Customers
• High Risk Customers
• Customer Value Distribution
• Discount Effectiveness
• Customer Rating Distribution
• Repeat Purchase Rate by Customer Segment

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Key Insights

• August recorded the highest revenue, while February showed comparatively lower revenue.
• Medium-value customers formed the largest customer group.
• High-value customers were fewer but contributed stronger spending.
• Discounts showed only moderate influence on spending behavior.
• Customer ratings alone did not strongly influence repeat purchasing behavior.
• Mumbai recorded the highest average order value.

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Business Recommendations

• Introduce loyalty rewards for high-value customers.
• Focus retention strategies on regular customers.
• Implement targeted discount strategies instead of blanket discounts.
• Strengthen customer engagement strategies across all customer segments.

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Dashboard Preview

Executive Overview

Customer Insights

Repository Structure

QuickKart-Sales-Customer-Analytics/
│
├── SQL/
├── Python/
├── Dashboard/
├── Documentation/
└── README.md

Author

Kesar Deaulkar
Data Analyst | SQL | Python | Power BI