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The primary goal of this project is to assess coffee shop sales for three coffee shop locations over a span of 6 months commencing in January and concluding in June using Power BI.
Revenue and transactions
Busiest days of the week for each store
Top 3 product categories across all 3 stores.
a) Coffee
Coffee sales, contributing 39% to the combined revenue of all three stores, amount to $269,952, making it the top-selling product category across all locations.
Best selling coffee products:
Coffee transactions in Astoria and Hell's Kitchen are nearly identical, with only a 1k difference in revenue.
Lower Manhattan lags, with a 1k difference from Astoria and 2k from Hell's Kitchen.
Beverage preferences (that is sizes and prices of beverages) in different locations drive revenue variations.
Due to limited data, we cannot determine which products determine a greater profit.
b) Tea
c) Bakery
Store hours
Conclusion
In conclusion, the analysis of monthly sales, transaction trends, and product categories across three stores provides valuable insights into the business performance. Interestingly, Lower Manhattan, while making the least in revenue, makes the most during peak hours compared to the other stores. Astoria makes the most during non-peak hours compared to the other stores despite reduced operational hours. All stores are close to each other in terms revenue generated. There is room to understand more of the customer base between these two stores to understand these differences. This comprehensive analysis equips the business with actionable insights for optimizing operations and maximizing revenue across all three locations.
Actions performed in Power BI
I performed transformations on the date column to extract information from it.
I extracted the month name in order to create a month name column. (Shown in the transactions by month visualisation)
I extracted the month number in order to create a month number column which was used to order months in the correct order. (Shown in the transactions by month visualisation)
I extracted the name of day in the week. (Shown in the transactions by each day of the week visualisation)
I extracted the day number in order to create a day number column which was used to order the days of the week. (Shown in the transactions by each day of the week visualisation)
I performed transformations on the time column to extract information from it.
I extracted the hour from the time column in order to create the hour column. (Shown in the transactions per hour visualization)
Calculated column
I created a calculated column called total price. I used the unit price multiplied by the transaction quantity column in order to create this column. (This was used in the total revenue card and revenue generated by store location visualization)
Measure
I created a measure called Percentage which was used to generate a percentage based on the revenue generated by transactions. (Shown in % of revenue generated card)