Superstore Sales Analysis (Identified Profit - Loss Pattern)

Tools Used: Excel | Python | Power BI

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Business Context

Retailers often have vast amounts of sales data, but it's difficult to pinpoint what’s truly driving profit — or causing losses.

This project dives into Superstore's historical sales data to uncover:

• Which customer segments and regions are profitable or in loss
• How discounts impact profit
• Which product categories are major contributors to losses

The ultimate goal: enable smarter pricing, discounting, and product decisions.

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Problem Statement

“How can we identify and reduce sales losses by analyzing customer segments, product categories, and regional sales patterns?”

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Project Objectives

1. Explore sales data to uncover trends, patterns, and outliers
2. Identify products, discounts, and regions that drive losses
3. Build an interactive Power BI dashboard for decision-makers
4. Provide actionable recommendations to boost profitability

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Key EDA Insights (Excel + Python)

| Insight | Description | | South region | Highest concentration of loss-making orders | | Tables & Bookcases | Contribute over 30% of total losses | | Discounts >30% | Often result in negative average profit | | Corporate Segment | More profitable than Consumer customers |

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Loss Driver Analysis

Performed deeper filtering and grouping on rows with Profit < 0.
Key techniques:

• Excel pivot tables to segment by Sub-Category and Region
• Python groupby() to isolate discount impact
• Visualized Discount vs. Profit trends

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Power BI Dashboard Highlights

Designed an interactive dashboard summarizing business performance.

** Visuals Included:**

• KPI Cards: Total Sales, Total Profit, Order Count, Loss Order Count
• Slicers: Region and Segment
• Bar Chart: Profit by Category
• Line Chart: Discount vs. Average Profit
• Table: Top 10 loss-making orders

** Preview Screenshot:**

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Tools & Technologies

• Excel: Data cleaning, filtering, pivot insights
• Python: EDA with pandas, matplotlib
• Power BI: Dashboard creation & DAX measures
• GitHub: Version control & documentation

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Business Recommendations

| # | Recommendation | Justification | | 1 | Cap Discounts at 20–25% | Prevent margin erosion on high-discount orders | | 2 | Audit Tables & Bookcases | These are frequent loss-making categories | | 3 | Focus on Corporate Clients | They deliver higher profitability | | 4 | Improve South/Central Strategies | These regions have the most losses despite decent sales |

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How to Run This Project

• Open .pbix file in Power BI Desktop to explore dashboard
• Review Excel pivots in EDA_Loss_Clean_Superstore.xlsx
• Use Jupyter or VS Code to run Python notebooks for EDA

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Author

Parul Dhami
Aspiring Data Scientist | Skilled in Power BI, Python, Excel, and SQL | Data Analyst [dhamiparul1@gmail.com]
GitHub: [https://github.com/Amodni007] LinkedInhttps://www.linkedin.com/in/paruldhami/:

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