📊 Retail Sales Analytics Dashboard

🚀 Project Overview

This project is an end-to-end Retail Sales Analytics Solution built using:

• SQL for data cleaning & preprocessing

• PostgreSQL for structured data storage

• Power BI for dashboard development

• DAX for advanced analytics and KPIs

The goal of this project was to transform raw retail transaction data into a professional, interactive business intelligence dashboard that provides actionable insights into sales performance, customer behavior, and revenue trends.

🗄️ Database & Data Engineering (PostgreSQL + SQL)

🔹 Data Storage

The dataset was stored and managed in a PostgreSQL relational database to ensure:

Structured schema design

Efficient querying

Data consistency

Scalability for BI tools

🔹 Data Cleaning & Preprocessing (SQL)

Using SQL, the following transformations were performed:

Removed null or invalid customer IDs

Cleaned inconsistent date formats

Filtered cancelled/negative invoices

Created calculated revenue column (quantity * unit_price)

Standardized column naming conventions (lowercase schema)

Handled duplicates

Optimized data types for performance

📈 Power BI Dashboard

The dashboard provides an executive-level summary of retail performance.

🔹 Key Dashboard Sections

1️⃣ Sales Overview Panel

Total Sales: $8.9M

Total Orders: 397K

Total Customers: 4,338

Total Quantity Sold

2️⃣ Sales Trend Analysis

Monthly Revenue Trend

Daily Sales Distribution

Quarter-based customer growth

3️⃣ Customer Analytics

Monthly distinct customers

Repeated vs One-time customers

Retention indicators

4️⃣ Product Performance

Top 3 Products by Revenue

Revenue contribution breakdown

5️⃣ Interactive Monthly Analytics Panel

Custom Month Button Slicer (Jan–Dec)

Dynamic KPI updates

Performance-driven visual filtering

Interactive trend visualization

📊 DAX Measures & Business Logic

Advanced DAX measures were created to power dynamic analytics:

🔹 Average Order Value (AOV)

🔹 Previous Month Revenue

🔹 Order Per Customer

🔹 Customer Type

New Date Table

🧠 Skills Demonstrated

🔹 SQL & Data Engineering

Data cleaning & transformation

Conditional filtering

Calculated columns

Data validation

Query optimization

Schema structuring

🔹 PostgreSQL

Database creation

Table management

Data integrity handling

Relational data modeling

🔹 Power BI

Data modeling

Relationship management

Date table implementation

Interactive slicers

Edit interactions

Executive KPI layout design

Visual storytelling

🔹 DAX

Aggregation functions

Time intelligence (PREVIOUSMONTH)

Context transition (CALCULATE)

FILTER logic

DIVIDE for safe calculations

Customer segmentation logic

🔹 Business Intelligence Concepts

Revenue analysis

Customer retention metrics

AOV calculation

Month-over-Month growth

KPI performance monitoring

Executive dashboard design

🛠️ Tech Stack

SQL

PostgreSQL

Power BI

DAX

Data Modeling

Business Intelligence