Superstore Sales Analysis

Practical retail sales analysis project focused on business decisions, not just charts.
I used Python and notebook workflows to identify where revenue is concentrated, where demand changes across the year, and where teams could improve planning and pricing.

Executive Summary

This project analyzes multi-year Superstore order data to answer common questions a business team would ask:

• What is driving total revenue?
• Which customer groups matter most?
• When does demand typically rise or slow down?
• Where should operations and marketing focus first?

The result is a set of clean, reusable outputs in data/processed/ plus notebook-based analysis that can be used in Excel or Tableau.

Business Problem

Retail teams often have data, but not a clear story.
The goal here was to turn raw order-level records into actionable direction for inventory planning, segment strategy, and performance review.

Dataset Overview

Item	Details
Dataset	Superstore sales data (portfolio dataset)
Scope	Multi-year U.S. retail orders
Core fields	Order Date, Ship Date, Category, Sub-Category, Segment, Region, Sales, Profit
Main outputs	Aggregated CSVs by category, segment, and month

Key Business Questions

• Which categories and customer segments contribute most to sales?
• How does monthly demand shift over time?
• What periods should inventory and campaign planning prioritize?
• Where should deeper profitability analysis happen next?

Key Metrics Analyzed

Based on processed outputs in data/processed/:

• Total sales analyzed: $2.26M
• Sales mix by category:
  • Technology: $827K (36.6%)
  • Furniture: $729K (32.2%)
  • Office Supplies: $705K (31.2%)
• Sales mix by segment:
  • Consumer: $1.15M (50.8%)
  • Corporate: $688K (30.4%)
  • Home Office: $425K (18.8%)
• Monthly seasonality signal:
  • Highest month in the dataset: Feb 2017 ($42.97K)
  • Lowest month in the dataset: Mar 2015 ($6.72K)

Key Findings

1. Consumer drives most revenue.
   Roughly half of all sales come from Consumer customers, which makes this segment the most direct lever for top-line impact.

2. Revenue is fairly balanced across categories, with Technology leading.
   Technology is first, but Furniture and Office Supplies are close enough that planning cannot rely on one category alone.

3. Sales are seasonal, not flat.
   Monthly results show clear swings, so inventory and campaign timing should follow demand cycles instead of fixed monthly assumptions.

4. The dataset supports operational decision-making, not only reporting.
   Aggregated outputs make it easier to connect category and segment trends to practical actions in marketing, pricing, and supply planning.

Business Impact

This analysis gives a junior-analyst-style decision pack that a manager can use quickly:

• Planning: prioritize inventory around historically stronger months instead of spreading stock evenly.
• Commercial focus: align promotions to the Consumer segment for broader reach while keeping targeted offers for Corporate accounts.
• Performance tracking: monitor category mix month over month to catch early shifts in demand.
• Operational alignment: use recurring trends to coordinate marketing, fulfillment, and purchasing decisions from one data view.

Recommendations

• Build monthly planning targets by category and segment using the processed outputs as a baseline.
• Add profitability by sub-category and region to pair revenue growth with margin protection.
• Add a simple KPI dashboard in Tableau with filters for month, segment, and category.
• Document a recurring review cadence so analysis can support ongoing decisions, not one-time reporting.

Tools Used

• Python (pandas, matplotlib, seaborn)
• Jupyter Notebooks
• CSV exports for Excel-friendly review
• Tableau-ready aggregated datasets

Repository Structure

superstore-sales-analysis/
├── analysis.py
├── README.md
├── requirements.txt
├── data/
│   ├── raw/
│   ├── processed/
│   └── superstore_sales.csv
├── notebooks/
│   ├── 01_exploratory_analysis.ipynb
│   ├── 02_aggregate_sales_by_category.ipynb
│   ├── 03_monthly_sales_trends.ipynb
│   ├── shipping_performance_analysis.ipynb
│   └── superstore_analysis.ipynb
├── scripts/
├── docs/
│   └── case_study.md
└── visuals/

Future Improvements

• Expand profitability analysis to sub-category and state level
• Publish a finished Tableau dashboard with core KPI cards
• Add a short business presentation deck for stakeholder walkthroughs

About Me

I am Phil Greene, an entry-level data analyst transitioning from operations, electrical, and customer-facing roles into analytics.

I am currently pursuing a BS in Data Analytics at SNHU, and I am building projects that show practical business thinking, clear communication, and reliable analysis workflows.

My background in independent e-commerce and operations work helps me frame analysis around real decisions, not just technical output.

Connect

• LinkedIn: Phil Greene
• X/Twitter: @vtguy65
• GitHub: @WebCraftPhil

If you are hiring for a junior data analyst role, I would be glad to connect and share more of my work.