Situation
- The project focuses on using Excel to analyze operational and financial data from a fictional coffee shop. The goal was to gain insights into sales performance, customer preferences, and operational efficiency.
- The context was to help the coffee shop's management make data-driven decisions to improve profitability and optimize operations.
Task
- My role in this project was to act as an Analyst and to use Excel to examine sales trends, track inventory, and evaluate financial performance.
- I was responsible for processing sales data, creating dashboards, and identifying key performance indicators (KPIs) that would support decision-making.
Action
- I started by importing daily sales data into Excel and cleaning it to ensure accuracy and consistency. I then created pivot tables to summarize the data by product, day, and sales category.
- Using Excel formulas such as SUMIFS, VLOOKUP, and AVERAGE, I calculated key metrics like total revenue, average daily sales, and product profitability.
- To visualize the results, I built a dashboard with dynamic charts showing trends in sales by product and category, as well as an analysis of the top-selling items.
Result
- The analysis provided the coffee shop's management with valuable insights, such as identifying the best-selling products and peak sales periods. This allowed them to optimize their menu and focus on high-margin items.
- My work helped improve inventory management by identifying which products required more frequent restocking and which ones had slower sales.
- Overall, the project demonstrated how Excel can be used effectively to track business performance and drive strategic decision-making.
Reflection
- From this project, I honed my skills in Excel data analysis, including the use of advanced formulas and pivot tables to create meaningful reports.
- If I had more time, I would automate some of the processes using macros or explore using Power Query for more efficient data handling.
- My advice to someone starting a similar project is to focus on cleaning the data thoroughly before analysis and to clearly define the key metrics you want to track for better insights.
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