https://github.com/mnitin-reddy/coffee-shop-sales-analytics
This project analyzes coffee shop sales data to uncover key insights, focusing on customer behavior, product performance, and sales trends. Using Pandas, Matplotlib, and Seaborn, the analysis identifies peak sales hours, popular product categories, and opportunities for upselling or bundling.
https://github.com/mnitin-reddy/coffee-shop-sales-analytics
datasceince datavisualization exploratory-data-analysis matplotlib numpy pandas python seaborn
Last synced: about 1 month ago
JSON representation
This project analyzes coffee shop sales data to uncover key insights, focusing on customer behavior, product performance, and sales trends. Using Pandas, Matplotlib, and Seaborn, the analysis identifies peak sales hours, popular product categories, and opportunities for upselling or bundling.
- Host: GitHub
- URL: https://github.com/mnitin-reddy/coffee-shop-sales-analytics
- Owner: MNitin-Reddy
- Created: 2024-08-14T10:24:34.000Z (9 months ago)
- Default Branch: master
- Last Pushed: 2024-10-21T13:43:41.000Z (7 months ago)
- Last Synced: 2025-02-08T11:44:01.302Z (3 months ago)
- Topics: datasceince, datavisualization, exploratory-data-analysis, matplotlib, numpy, pandas, python, seaborn
- Language: HTML
- Homepage: https://mnitin-reddy.github.io/Coffee-Shop-Sales-Analytics/
- Size: 18.9 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
__Visit the project website for detailed information:__ [Coffee Shop Sales Analysis](https://mnitin-reddy.github.io/Coffee-Shop-Sales-Analytics/).
# Coffee Shop Sales Analytics - EDA
## Introduction
This project aims to explore the sales data of a coffee shop chain for the first half of 2023, from January to June. The dataset includes information about transactions, product categories, unit prices, and revenues across three stores. The goal is to uncover insights into sales trends, customer preferences, and potential areas for improvement.
## Stakeholders
* __Coffee Shop Management:__ Interested in understanding sales trends and customer behavior to optimize product offerings and improve sales strategies.
* __Marketing Team:__ Looking for insights to create targeted marketing campaigns and promotions.
* __Operations Team:__ Aiming to improve store operations based on sales data, such as staffing and inventory management.
## Objectives
* Analyze sales data to identify key trends and patterns in customer behavior.
* Determine the most popular product categories and their sales performance.
* Evaluate the impact of pricing on sales volumes across different product types.
* Provide actionable recommendations to optimize sales and customer engagement.
## Methodology Used
* __Data Collection and Integration:__ Data was gathered from various sources and consolidated into a coherent dataset.
* __Data Cleaning and Transformation:__ The dataset was cleaned using Python to handle missing values, remove duplicates, and standardize formats.
* __Exploratory Data Analysis (EDA):__ Conducted an in-depth analysis using statistical methods and visualization techniques to uncover trends and patterns in the data.
* __Visualization:__ Used Python libraries such as Matplotlib and Seaborn to create visual representations of the data to aid in the analysis.
* __Insights and Recommendations:__ Derived insights from the analysis and provided recommendations to improve sales and operations.
## Final Insights and Conclusion
* __Sales Consistency Across Stores:__ The total revenue generated across all stores is quite similar, indicating consistent sales performance across different locations.
* __Customer Purchase Behavior:__ Most transactions involve one or two items, presenting an opportunity to increase the number of items purchased per transaction through bundle discounts and loyalty rewards.
* __Product Popularity:__ Coffee is the most popular category, followed by tea and bakery items, suggesting a strong customer preference for these products.
* __Pricing and Sales Volume:__ Higher-priced items such as loose tea, coffee beans, and packaged chocolates have lower sales volumes, while lower-priced items like coffee and tea have high sales volumes. This presents an opportunity to optimize the sales of high-priced items through targeted promotions and product lineup adjustments.
* __Sales Trends:__ Sales peak during morning hours from 8 a.m. to 10 a.m., with a moderate level of activity from 11 a.m. to 7 p.m. Utilizing this information can help optimize staffing and marketing efforts throughout the day.