Superstore-PowerBI-Project

superstore_performance_and_insights_AdobeExpress.2.mp4

The Superstore dataset is a rich and comprehensive dataset containing information on the sales, orders, and customers of a fictional superstore. The dataset provides a detailed view of the business operations of the superstore, including information on the products sold, the customers who purchase them, and the sales performance across different regions and categories. This dataset is a valuable resource for data analysts, business intelligence professionals, and anyone interested in understanding the dynamics of retail sales and customer behavior. With about 9,994 rows of data spanning four years (2014-2017), the Superstore dataset provides a wealth of information to analyze and explore.

I analyzed the superstore dataset and conducted a sales performance analysis to provide insight on the following business problems:

• What is the current market demand for our products and how does it vary by region?
• What is the customer satisfaction and loyalty level, and what actions can we take to improve it?
• What is the current sales performance and how does it compare to industry benchmarks?

Process

Steps taken toward the completion of this project include:

• cleaning the dataset
• Data transformation in Power Query
• Creating measures using DAX functions
• Data Visualization
• Publishing to Power BI service

Outcome

• The average monthly sales for the superstore is $47.86k and the average monthly target sales is $52.64k. Also, the average monthly profit is $5.97k and the monthly profit target is $5.67k. This shows that the monthly sales and profit targets were not met.
• This analysis shows that 2017 has the highest total sales of $733,215.3 and November and December are the biggest two months of the year by some distance in terms of sales volumes, possibly because the biggest sales events occur during this two month period.
• Using the Pareto principle, we can see 80% of total sales were made by 20% of the states represented in the data, which are California, New York, Texas, Washington, Pennsylvania, Florida, Illinois, Ohio, Michigan, Virginia, North Carolina, Indiana, Georgia, Kentucky and New Jersey respectively.
• Technology has 36.4% which is the category that has the highest sales followed by Furniture with 32.3% and Office Supplies with 31.3%.
• Technology and Office supplies takes the lead across all years represented in the data as its profit margin ranges from average to good, the profit margin for Furniture is very low.
• In 2014, Technology made the highest total sales of about $175k but Office supplies made the highest profit of $23k and from 2015-2017, Technology made the highest total sales and profit, followed by Office Supplies.
• The Consumer segment made the highest total sales and profit in all the years contain in this analysis, followed by Corporate and Home office segments correspondingly.
• The top high-value customers in 2017 are Raymond Buch, Hunter Lopez and Tom Ashbrook, In 2016 they are Tamara Chand, Adrian Barton and Sanjit Engle. In 2015 the top high-value customers are Christopher Martinez, Keith Dawkins and in 2014, they are Sanjit Chand and Tom Boeckenhauer.

Recommendation

• Increase focus on top selling categories: Based on this analysis, it is clear that some categories are more popular and profitable than others. The superstore should focus on these top selling categories to increase their sales and profitability.
• Improve sales in underperforming states and regions: The superstore should invest in these states and regions by using social media as a tool for brand building awareness, engaging with customers and promoting products. And also, monitor their competitors' pricing strategies to stay up-to-date.
• Implement targeted marketing campaigns: The superstore should identify and target the high-value customers with personalized marketing campaigns to increase their loyalty and encourage repeat purchases.