Retail-Sales-Analysis

Table of Contents

Project Overview

Objectives

Dataset Overview

Tools Used

Data Cleaning & Transformation

Key Insights

Dashbaord Highlights

Recommendations

Conclusion

Files in Repository

What I Learned

Future Improvements

Project Overview

This project presents a detailed analysis of retail sales data using Power BI, aimed at uncovering insights into customer behavior, product preferences, and sales performance over time. The insights derived from this project are designed to help businesses make data-driven decisions that optimize inventory, enhance marketing strategies, and boost revenue.

Objectives

• Analyze customer purchasing patterns by age and gender

• Identify seasonal and weekly sales trends

• Explore product category performance and preferences

• Investigate price sensitivity and purchasing volume

• Understand transaction behavior and average order values

Dataset Overview

Source: Kaggle

Rows: 1,000 transactions

Columns: 9

Key Fields:

• Transaction ID, Date, Customer ID, Gender, Age

• Product Category, Quantity, Price per Unit, Total Amount

Tools Used

• Power BI – Data visualization and dashboard creation

• Excel – Data preprocessing and wrangling

• Kaggle – Dataset source

Data Cleaning & Transformation

• Handled missing values and duplicate entries

• Standardized date formats and ensured data type accuracy

• Created custom fields such as Age Group, Workday Type, and Year

• Enhanced categorical consistency for product categories

Key Insights

Metric	Insight
Total Sales	$456,123
Units Sold	2,514 units
Top Product Category	Electronics – $155,400
Best Performing Age Group	19–28 years
Most Profitable Day	Saturday
Top Sales Quarter	Q4 (Oct–Dec)
Optimal Price Point	$500
Gender Spending	Females: 51.06% of sales; Males: 48.94%

Dashboard Highlights

Built in Power BI, the interactive dashboard includes:

• Sales Trends by Date

• Sales by Gender & Age Group

• Top Product Categories & Price Points

• Units Sold per Transaction

• Sales by Day of the Week

Note: The dashboard includes slicers to filter by gender, age group, and product category for dynamic insights.

Recommendations

1. Optimize Inventory

• Stock popular categories (Beauty in Q1/Q4; Electronics in Q2/Q4) ahead of peak seasons.

2. Target High-Value Demographics

• Focus marketing on the 19–28 age group.
• Use gender-based product recommendations.

3. Leverage Price Insights

• Emphasize premium pricing around $500.
• Offer bundles or tiered pricing options.

4. Capitalize on Shopping Patterns

• Promote weekday offers; maximize weekend traffic with exclusive deals.

5. Enhance Customer Retention

• Implement loyalty programs and personalized product recommendations.

Conclusion

The analysis revealed a consistent trend of higher sales during workdays, a strong preference for premium-priced items, and gender-based product differences. These insights can help inform better inventory planning, pricing strategies, and targeted promotions to boost revenue.

Files in Repository

• Retail_Sales_Analysis.pbix – Power BI dashboard file (https://github.com/FisayoAnalyst/Retail-Sales-Analysis-Using-Power-BI/blob/main/Retail_Sales%20Analytics.pbix)

• Retail_Sales_Data.xlsx – Cleaned dataset (https://github.com/FisayoAnalyst/Retail-Sales-Analysis-Using-Power-BI/blob/main/retail_sales_dataset.xlsx)

• Retail_Sales_Report.pdf – Detailed project report. (https://github.com/FisayoAnalyst/Retail-Sales-Analysis-Using-Power-BI/blob/main/Retail%20Sales_Analysis_Report.pdf)

• README.md – Project summary

What I Learned

• End-to-end dashboard creation using Power BI

• Translating raw data into actionable insights

• Communicating complex patterns through visual storytelling

• Aligning business strategy with data analysis

Future Improvements

• Include customer segmentation for deeper personalization

• Integrate external factors (e.g., holidays, promotions)

• Expand product categorization granularity (e.g., sub-categories)