Superstore Sales Forecasting & Product Strategy

Table of Contents

• Project Overview
• Objective
• Key Business Questions
• Dataset Description
• Tools Used
• Data Cleaned
• Visuals
• Key Insights
• Recommendation
• How to use this Project
• Conclusion
• Author

Project Review

This project analyzes sales and profitability across regions, product categories, and customer segments using a superstore retail dataset.

Objective

To uncover business insights that improve revenue, product performance, and regional sales strategies.

Key Business Questions

• Which subcategories and categories are underperforming?
• Which region brings in the most sales and profit?
• How does discounting affect profitability
• Which customer segments generate the most revenue?
• What product strategies can boost sales & margins?

Data Description

• Superstore_Raw_Data
• Superstore_Cleaned_Data
• Source : Kaggle

Tools

• Microsoft Excel (data cleaning, formulas, KPI calculation)
• Power BI (Interactive multiple page dashboards, DAX)
• Power Query (data modeling and transformation)

Data Cleaning & Preparation

• Removed duplicates and blanks
• Standardize column headers (e.g., “Order-date”, “Sales”, “Profit Margin”)
• Created new column for “Year”, “Month”, and “Profit Margin”
• Filtered out invalid discount or sales values
• Converted data formats and ensured consistency

Visuals

• Sales_Peformance.pngSales Forecasting & Performance
• Profitability_Margins.pngProfitability & Margins
• Product_Strategy_Insights.pngProduct Strategy Insights
• Regional_Strategy.pngRegion Strategy
• Recommendations.pngRecommendations

Key Insights

• Monthly sales performance: November showed peak sales; February has the lowest
• Technology products had the highest profit margins
• Office Supplies had declining sales and margins
• Western region outperformed in both sales and profits

Recommendation

• Focus marketing in November; run off-season discounts in February
• Discontinue low-performing products like Eureka Bags
• Prioritize Technology products for marketing and bundling
• Optimize shipping costs and pricing in the Central region
• Invest in growing the West region further

How to use this project

1. Open the Power BI file, ‘Superstore_Dasboard.pbix
2. Use slicers to explore product and region performance
3. Review Excel cleaning steps in ‘Superstore_Cleaned_Data.xlsx.

Conclusions

This Project demonstrates how retail data can drive decisions in pricing, marketing, and regional sales planning. It highlights trends that can help increase profitability and optimize customer targeting.

Author

Elujulo Margaret Kehinde [elujulomargaret@gmail.com](mailto: elujulomargaret@gmail.com)