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https://github.com/robinmillford/superstore-sales-insights

In this data analysis project, I examined the sales dataset from the SuperStore. The objective was to gain insights into various aspects of the business, using both Python and SQL in a Jupyter notebook, alongside data visualization performed in Tableau. Here are the key findings:
https://github.com/robinmillford/superstore-sales-insights

data-cleaning data-visualization jupyter-notebook python sql tableau

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In this data analysis project, I examined the sales dataset from the SuperStore. The objective was to gain insights into various aspects of the business, using both Python and SQL in a Jupyter notebook, alongside data visualization performed in Tableau. Here are the key findings:

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**Project Summary: SuperStore Sales Analysis**



Tableau - https://public.tableau.com/app/profile/yamin3547/viz/SuperstoreSalesInsights_16959200256060/Dashboardmap#1



In this data analysis project, I examined the sales dataset from the SuperStore, sourced from the Real Life Data Project for HiCounselor. The objective was to gain insights into various aspects of the business, using both Python and SQL in a Jupyter notebook, alongside data visualization performed in Tableau. Here are the key findings:



1. **Percentage of Same-Day Shipments:** Explored the percentage of total orders that were shipped on the same date, providing insights into delivery efficiency.



2. **Top 3 High-Value Customers:** Identified and ranked the top 3 customers with the highest total order values, highlighting valuable clientele.



3. **Top 5 High-Average Sales Items:** Determined the top 5 items with the highest average sales per day, aiding inventory management decisions.



4. **Customer Average Order Value:** Calculated the average order value for each customer and ranked them by their spending habits.



5. **Customer Order Extremes by City:** Identified customers who placed both the highest and lowest orders from each city, offering insights into regional variations.



6. **Most Demanded Sub-category in the West:** Discovered the most demanded sub-category in the west region, aiding in regional marketing strategies.



7. **Highest Item Count:** Determined the item with the highest count, aiding inventory management.



8. **Highest Cumulative Value:** Found the item or category with the highest cumulative sales value.



9. **Preferred Shipping Segment:** Analyzed which customer segment is more likely to choose first-class shipping, offering insights into customer preferences.



10. **Average Order Shipping Time:** Calculated the average time taken to ship orders after they are placed, ensuring timely delivery.



11. **Segment Order Insights by State:** Discovered which customer segment places the highest number of orders from each state and which segment places the largest individual orders from each state.



12. **Consecutive High-Value Orders:** Identified customers who individually ordered on 3 consecutive days, with each day's total order value exceeding $50.



13. **Maximum Consecutive Sales:** Determined the maximum number of days for which total daily sales continued to rise, offering insights into sales trends.



In addition to Python and SQL analysis, data visualization was performed in Tableau to present these findings in an engaging and comprehensible manner. This comprehensive analysis provides actionable insights that can inform decision-making and strategy development for the SuperStore business.