{"id":25711549,"url":"https://github.com/dpb44/superstore-business-performance-analysis","last_synced_at":"2026-03-04T10:01:14.150Z","repository":{"id":276969982,"uuid":"930839736","full_name":"dpb44/Superstore-Business-Performance-Analysis","owner":"dpb44","description":"Interactive Dashboard \u0026 Performance Report Highlighting Key Insights and Strategic Recommendations","archived":false,"fork":false,"pushed_at":"2025-02-11T11:57:15.000Z","size":1986,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-25T10:55:07.120Z","etag":null,"topics":["business-analytics","dashboard","data-visualization","dataanalytics","excel","superstore-data-analysis"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dpb44.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-11T09:50:06.000Z","updated_at":"2025-02-11T11:57:18.000Z","dependencies_parsed_at":null,"dependency_job_id":"bdb36aff-e40d-4b45-a7be-e5bc19dcdfc4","html_url":"https://github.com/dpb44/Superstore-Business-Performance-Analysis","commit_stats":null,"previous_names":["dpb44/superstore-business-performance-analysis"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/dpb44/Superstore-Business-Performance-Analysis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dpb44%2FSuperstore-Business-Performance-Analysis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dpb44%2FSuperstore-Business-Performance-Analysis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dpb44%2FSuperstore-Business-Performance-Analysis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dpb44%2FSuperstore-Business-Performance-Analysis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dpb44","download_url":"https://codeload.github.com/dpb44/Superstore-Business-Performance-Analysis/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dpb44%2FSuperstore-Business-Performance-Analysis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30078306,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-04T08:01:56.766Z","status":"ssl_error","status_checked_at":"2026-03-04T08:00:42.919Z","response_time":59,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["business-analytics","dashboard","data-visualization","dataanalytics","excel","superstore-data-analysis"],"created_at":"2025-02-25T10:54:17.152Z","updated_at":"2026-03-04T10:01:14.144Z","avatar_url":"https://github.com/dpb44.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# Superstore-Business-Performance-Analysis\n\n## **Overview**\n\nThis project is a **comprehensive sales dashboard** built entirely in **Microsoft Excel**, utilizing **Power Query, Power Pivot, and DAX** for data processing, modeling, and visualization. The dashboard provides insights into key performance metrics, product market trends, and regional sales distribution, allowing users to interactively explore sales data through **slicers and navigational controls**.\n\n## **Features**\n\n- **Built-in Excel**: No external tools or software required.\n- **Power Query for Data Cleaning \u0026 Structuring**: Efficiently cleaned, transformed, and modeled the dataset.\n- **Power Pivot for Data Modeling**: Created relationships, calculated measures, and developed a dynamic calendar table.\n- **Interactive Dashboards**: Users can navigate seamlessly between three dashboards covering different aspects of sales performance.\n- **Slicers for Filtering**: Users can filter by **Time, Region, Product Category, and Customer Segment**.\n- **Diverse Visualizations**: Includes **KPIs, Pareto charts, bar charts, line charts, maps, and heatmaps** to highlight key insights.\n\n![Image](https://github.com/user-attachments/assets/c6769b3d-e42a-46fb-a79d-3fc6694d8c63)\n---\n\n## **Introduction**  \n\nThis project analyzes a **Superstore Sales Dataset** from **January to June 2015**, focusing on sales performance across different product categories, regions, and customer segments. The data pertains to orders within the **United States**, and all monetary values are in **USD ($)**.  \n\nThe dataset includes:  \n\n- **Orders Data:** Contains all essential transaction details, including order date, product category, quantity, sales, discount, profit, shipping mode, and customer segment.  \n- **Returns Data:** Tracks product returns, helping assess return rates and their impact on profitability.  \n- **User Data:** Includes managerial and regional assignments, aiding in sales performance analysis across different areas.  \n\nTo facilitate **time-based analysis**, a **Calendar Table** was created in **Power Query**, allowing advanced **DAX measures** to calculate critical date-based insights. The **Data Model** establishes relationships between these tables, enabling seamless data connectivity and in-depth analysis.\n\n---\n\n## **Performance Report – Superstore (Jan–June 2015)**  \n\nThis report provides a **data-driven assessment** of Superstore’s overall performance from **January to June 2015**, identifying key patterns, anomalies, and actionable insights based on sales, profit, and segment-wise contributions.  \n\n\n\n### **Key Financial Metrics:**  \n- **Total Revenue:** **$1.9M**  \n- **Total Profit:** **$0.2M** (**12% profit margin**)  \n- **Total Orders:** **1.4K**  \n\nWhile revenue figures are promising, the **12% margin** indicates opportunities for cost optimization and pricing adjustments.  \n\n\n\n### **Profitability Trends – Identifying Anomalies**  \nA **month-over-month analysis** reveals significant fluctuations in profitability:  \n\n| **Month**  | **Sales ($M)** | **Profit (%)** | **Key Observations** |  \n|------------|---------------|----------------|------------------------|  \n| January   | 0.3M          | 0%             | **Zero profit despite stable revenue**—possible excessive discounting or high operational costs. |  \n| February  | 0.3M          | 11%            | Recovery likely due to **seasonal demand (Valentine’s Day) or strategic promotions.** |  \n| March     | 0.3M          | 0%             | **Profitability drops back to zero**, highlighting an inconsistent pricing model. |  \n| April     | 0.4M          | 14%            | First signs of sustained growth. **Small Business segment peak at 30%.** |  \n| May       | 0.3M          | 22%            | **Most profitable month**—suggesting strong demand and better product mix. |  \n| June      | 0.3M          | 19%            | Profit remains strong, **sales led by Corporate and Home Office segments.** |  \n\n**Takeaway:** The **inconsistent profitability** in Q1 (Jan–March) suggests **poor cost control or heavy discounting**, whereas **April–June saw stabilization**, potentially due to refined sales strategies. This highlights a **need to reassess pricing and discount structures, particularly in Q1.**  \n\n\n\n### **Customer Segment Performance – Revenue Contribution \u0026 Stability**  \nA breakdown of revenue by **customer segment** highlights key drivers:  \n\n- **Corporate (35%)** – Largest contributor but shows **profitability inconsistencies** (losses in January and March).\n\n![Image](https://github.com/user-attachments/assets/07a4a172-da99-4242-8445-387fa536dbb3)\n  \n- **Home Office (24%)** – Second-largest contributor but with **unsteady profit margins** (loss in April).\n\n![Image](https://github.com/user-attachments/assets/0eaeb81c-b2f4-4b4a-b9b4-af7263cac9af)\n\n- **Consumer (21%)** – Generally profitable, except for **March (-23%) loss**.\n\n![Image](https://github.com/user-attachments/assets/bb05533c-0576-46b4-9d8d-5f430928ee87) \n \n \n- **Small Business (21%)** – Modest profits in Q1, **significantly stronger performance in later months (21-26%).**\n\n![Image](https://github.com/user-attachments/assets/2ccec213-99ed-43c4-8387-961cab7141e9)\n\n\n**Takeaway:** **Corporate \u0026 Home Office segments require margin optimization.** Bulk orders from Corporate may be heavily discounted, reducing profitability. Meanwhile, **Small Business is emerging as a high-growth segment** in Q2, warranting **targeted engagement strategies.**  \n\n\n\n### **Order Trends – When Are Sales Happening?**  \n**Weekly Order Spikes:** **Consistent increases in sales occur in Weeks 4 (Jan, May), Week 5 (March), and Week 2 (April, June).**  \n\n![image](https://github.com/user-attachments/assets/b699512c-824f-4191-891e-7451643a3a4a)\n\n **Day-Wise Performance:** **Saturday (24%) and Friday (16%) dominate sales**, while mid-week sales remain lower. \n\n ![image](https://github.com/user-attachments/assets/64b702b9-9a12-4ee1-bc81-8d8de7f6b571)\n\n\n**Analysis:**  \n- High **weekend sales** indicate a **strong B2C demand cycle**—potential opportunity to introduce **midweek promotions** to balance revenue distribution.  \n- End-of-month order spikes **could be driven by corporate procurement cycles**—a signal to **optimize bulk order offerings around these peaks.**  \n\n\n\n### **Regional Performance – Identifying Gaps \u0026 Growth Areas**  \nA **manager-wise revenue breakdown** exposes regional disparities:  \n\n| **Manager (Region)** | **Revenue Share (%)** | **Profitability Trend** | **Key Concern** |  \n|----------------------|----------------------|----------------------|----------------|  \n| **Chris (Central)**  | 23%                  | **Stable (17%)**      | Balanced performance. |  \n| **Erin (East)**      | 31%                  | **Improving (14%)**   | High losses in Jan \u0026 Feb (-12%, -5%). |  \n| **Sam (South)**      | 19%                  | **Negative (-4%)**    | **Major losses in Feb (-24%) \u0026 March (-27%).** |  \n| **William (West)**   | 27%                  | **Stable (14%)**      | Weak April (-6% profit). |  \n\n**Critical Concern: Sam (South) is underperforming (-4% loss overall).**  \n- **February (-24%) and March (-27%) losses** indicate possible **weak regional demand, operational inefficiencies, or high return rates.**  \n- Needs **urgent intervention—product realignment, price optimization, and region-specific marketing.**  \n\n**Takeaway:** Targeted **regional interventions are needed.** While Erin (East) has recovered, **Sam’s region remains a loss center.**  \n\n---\n\n## **Product Market Analysis**  \n\nThis section evaluates product performance, profitability, and purchasing behaviors, offering data-driven insights to optimize sales strategies.  \n\n\n### **Key Sales \u0026 Profitability Metrics**  \n- **Total Quantity Sold:** **25K products**  \n- **Average Discount Provided:** **5%**\n\nThe discount is more or less consistent within the 4.5 to 5.2% range. So our previous consideration of possible high discounts causing imbalnces is disregarded. \n\n**Top Performers and Underperforming Products**  \n![image](https://github.com/user-attachments/assets/ce51b8d5-8843-47db-bffe-d4633c7d3904)\n\n\n**Labels (143% profit)** lead profitability, likely due to their low cost, high markup, and strong corporate demand, followed by copiers (24%), binders (32%), and fax machines (24%), which remain essential office staples. Conversely, rubber bands (-86%) suffer from excessive low margins, while scissors, rulers, trimmers (-19%), and envelopes (-11%) face intense competition and commoditization, limiting profitability.\n\n**Takeaway:**  \n- **Office supplies dominate profitability,** reinforcing that **corporate and home office customers drive demand.**  \n- **Unprofitable items (rubber bands, scissors, envelopes) may require price adjustments or bundling strategies** to improve margins.  \n\n\n### **Revenue Concentration – The 70/30 Rule (Pareto Chart Analysis)** \n\n![image](https://github.com/user-attachments/assets/d2e072f0-9e64-47d4-8dce-50c7d8f9e7f5)\n \nA **Pareto analysis** shows that **70% of revenue** is generated by just a few key product categories:  \n- **Top Contributors:** **Office Machines, Chairs, Telephones, Tables, Binders, Storage, Organizers.**  \n- **Next 17% Contribution:** **Bookcases, Copiers, Computers, Office Furnishings, Appliances, Paper, and Art Supplies.**  \n- **Bottom 3% Contribution:** **Rubber Bands, Scissors, and other low-margin office supplies.**  \n\n**Takeaway:**  \n- **The business is highly reliant on office-related products.**  \n- **Low-revenue items are insignificant contributors to overall sales** and should either be repositioned or phased out.  \n\n\n\n### **Delivery \u0026 Returns Analysis** \n\n\n- **Return Rate:** **~1%** (Low, indicating good product-market fit)  \n- **67% of returned items are Tech Products** (Copiers, Telephones, etc.), likely due to **defective units or buyer remorse.**  \n\n![image](https://github.com/user-attachments/assets/4d060242-d2b1-4375-a2f0-af953c2fc7a3)\n\n\n**Delivery Mode Efficiency:**  \n- **Average Delivery Time:** **~2 days**  \n- **Low-priority orders take ~4 days (Regular Air, Truck), ~3.5 days (Express Air).**  \n- **All other priority orders (Medium, High, Critical) are delivered within 1.5 days, regardless of transportation mode.**\n\n![image](https://github.com/user-attachments/assets/f7640fae-6553-4562-9c02-ac2e6cefcd1e)\n\n\n**Takeaway:**  \n- **Expedited delivery offers little benefit unless for low-priority items**—indicating a potential cost-saving opportunity by limiting unnecessary \"express\" shipments.  \n- **High return rate in tech products suggests a need for better product descriptions or post-sales support.**  \n\n\n### **Packaging Insights** \n\n**Majority of orders are packed in:**  \n- **Small Boxes (60%)** – Likely office supplies (Binders, Labels, Paper).  \n- **Wrap Bags (19%)** – Likely lightweight, smaller items.  \n- **Small Packs (16%)** – Often used for multipack office essentials.\n\n![image](https://github.com/user-attachments/assets/23fcb8dd-99fa-4da0-8f9e-0ca01fa8e049)\n\n**Larger packaging (Jumbo Box, Drum) is mainly used for furniture (Tables, Chairs, Bookcases).**  \n\n![image](https://github.com/user-attachments/assets/01afeea9-b993-4892-897f-4e1495c09947)\n\n\n**Takeaway:**  \n- **Most shipments are small, lightweight office products,** reinforcing that corporate and home office customers drive sales.  \n- **Furniture-related products require bulkier packaging, leading to higher shipping costs.**  \n\n\n### **Product Category Analysis**  \n\n| **Category**         | **Top Performers**                                     | **Loss-Makers**                                  | **Key Insights**                                                 |\n|----------------------|------------------------------------------------|--------------------------------------|----------------------------------------------------------------|\n| **Furniture**       | Chairs (19%), Office Furniture (19%)           | Bookcases                            | Bulk corporate purchases drive profitability; high storage \u0026 transport costs hurt bookcases. |\n| **Office Supplies** | Labels (143%), Binders, Appliances             | Rubber Bands, Scissors, Envelopes    | Small, low-cost items may be underpriced or over-discounted. |\n| **Technology**      | Copiers, Telephones, Office Machines (All Profitable) | None                                 | Higher price points create better margins despite higher return rates. |\n\n**Takeaway:**  \n- **Office supplies and tech products are core revenue drivers.**  \n- **Heavy furniture (Tables, Bookcases) needs better logistics cost management.**  \n\n\n### **Customer Segment Analysis – Who Buys What?**  \n**Consumers:** **Favor Labels, Paper, and Binders** but experience **losses on Scissors.**  \n**Corporate:** **Huge profit margin (365%) on Labels, Binders, Fax Machines.**  \n**Home Office:** **Purchases mostly Chairs, Labels, Scissors.**  \n**Small Business:** **Major buyers of Copiers \u0026 Envelopes.**  \n\n**Takeaway:**  \n- **Corporate customers dominate high-profit items (Labels, Binders).**  \n- **Home Office \u0026 Small Business show diverse product needs, requiring tailored marketing.**  \n\n---\n\n## **Geographical Analysis and Trends**  \n\nThe **East (31%) and West (27%)** drive the majority of sales, while **Central (23%)** follows closely.  Despite strong revenue generation, **Central (17%)** remains the most profitable region, followed by **West (14%) and East (14%)**, whereas **South faces a -4% loss.** \n\n![image](https://github.com/user-attachments/assets/7caa5b31-6219-4537-ab34-409417b021d5)\n\nThe **South (19%)** underperforms, largely due to inconsistent profitability across its cities:\n\n![image](https://github.com/user-attachments/assets/23b93781-2465-417f-9475-8235429ead6e)\n\nCity-wise, **Pensacola (33%) and Asheville (10%)** lead in profits, whereas **Danville (-57%) and Kissimmee (-18%)** drag overall performance down. Unlike other regions with stable city-level profitability, **South is highly volatile, with extreme highs and lows.**  \n\n\n### **Product \u0026 Segment Performance by Region** \n  \n\n| **Category**       | **Highest Sales**       | **Profitability**                                      | **Key Concern**                                  |\n|--------------------|------------------------|------------------------------------------------------|------------------------------------------------|\n| **Furniture**      | East \u0026 West            | **West (27%) profit**, **East (0%) despite high sales** | East's **high revenue but no profit** suggests cost inefficiencies. |\n| **Office Supplies** | East                   | **East (27%) profit**, **Central (25%) profit despite lower revenue** | South struggles with losses in this category. |\n| **Technology**     | West                    | **West (5%) profit**, **Central \u0026 East (19-25%) profit despite lower sales** | West has **high sales but low margins**—potential pricing or cost issue. |  \n\n\n\nCustomer-wise, **West dominates sales but has lower profit margins than Central \u0026 East.** **South struggles with corporate clients (-21% loss), making it the weakest segment-region combination.**  \n\n---\n\n## Key Recommendations – Data-Driven Business Actions  \n\n| **Focus Area**              | **Recommendation**                                      | **Expected Impact**                              |\n|-----------------------------|--------------------------------------------------------|------------------------------------------------|\n| **Corporate Segment**       | Reevaluate bulk discounting, introduce tiered pricing. | Improve profit margins without sacrificing sales. |\n| **South Region Strategy**   | Adjust product pricing, customer engagement, and cost structure. | Reduce losses and stabilize profitability. |\n| **Office Supplies Pricing** | Reduce discounts on high-demand items (Labels, Binders, Copiers). | Maximize revenue from already strong-performing products. |\n| **Loss-Making Products**    | Reassess Rubber Bands, Scissors, Envelopes—reprice, bundle, or discontinue. | Eliminate unprofitable SKUs or reposition them for better sales. |\n| **Furniture Profitability** | Optimize shipping/storage costs, negotiate vendor terms. | Reduce cost inefficiencies, making East region profitable. |\n| **Tech Returns Management** | Stricter quality checks and better post-sales support. | Minimize return rates, enhancing overall profitability. |\n| **Peak Profit Periods (May \u0026 June)** | Amplify marketing \u0026 promotions during these months. | Capitalize on historically high revenue and profit trends. |\n\n---\n\n##  Conclusion  \n\nThis project provides a **comprehensive data-driven analysis of Superstore Sales**, covering **Overall Performance, Product Market Trends, and Regional Performance**. By implementing the recommended business actions, Superstore can **increase profitability, optimize logistics, and improve pricing strategies**.  \n\n---\nThis repository includes:  \n📂 **Excel Dashboard File (Power Query \u0026 Power Pivot)**  \n📂 **Raw Dataset for Further Analysis**  \n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdpb44%2Fsuperstore-business-performance-analysis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdpb44%2Fsuperstore-business-performance-analysis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdpb44%2Fsuperstore-business-performance-analysis/lists"}