Coffee-Shop-Sales-Portfolio
Coffee Shop Sales Analysis
Project Overview
This project involved analysis of six months transactional data for a multi-location coffee retail chain. Raw sales logs was transformed into insights that identified growth patterns, optimizes staffing schedules, and evaluated product performance.
Data Source
Sales Data: The dataset used for this analysis is the “coffee_shop_sales_csv” file, containing detailed information about sales made by a company across locations.
Tools
- Excel - Data Cleaning, Data Analysis, Data Visualization
Data Cleaning
In the initial data preparation phase, I performed the following tasks
- Data loading and inspection.
- Data cleaning and formatting
Exploratory Data Analysis
- How has sales trended over time?
- Which days of the week tend to be busiest?
- What time of day tend to be most popular? Does the same trend hold across all locations?
- Which products are sold most and least often? Which drive the most revenue for the business?
- Most Performing location.
Data Analysis
Utilized Advanced Pivot Tables and Time-Series Analysis to aggregate sales by hour, day, and month.
Results
Analysis results are summarized as follows:
- A significant revenue growth of 104% was made in January to June.
- Friday, Thursday and Monday are the busiest days of the week.
- Operational peak hours were between 8:00 AM to 10:00 AM.
- Coffee (Barista Expresso) are the top revenue generating menu.
- Hell’s Kitchen is the location with the highest performance, contributing mostly to the total revenue.
Recommendations
Based on the analysis we recommend the fololowing actions
- Align labor shifts to the 7:00 AM–11:00 AM window to handle 60% of daily traffic.
- Launch “Morning Specials” (Coffee + Bakery) to increase the average transaction value.
- Implement “Zero Stockout” policy for Espresso and Chai Tea due to high turnover rates.
- Replicate the Hell’s Kitchen operational model across lower-performing stores.
👩💻 Author
Ms_Safiyah