Tea Sales Performance Analysis

Project conducted: May 2025 (was not uploaded at the time)

Uncovering actionable business insights from tea product sales data through exploratory data analysis and visualization.

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Introduction

This project focuses on analyzing sales data from a Vietnamese tea business. The dataset includes detailed transaction records covering order times, customer segments, product categories, quantities sold, and revenue generated.

Project Objective:
To derive key insights about customer behavior, product performance, and revenue trends to support strategic decision-making for marketing, inventory, and sales optimization.

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Dataset

Source

The dataset was provided internally by the tea business for analysis. It contains pre-cleaned transactional data, ready for direct exploratory data analysis.

Key Columns

• Thời gian tạo đơn: Order creation timestamp
• Mã đơn hàng: Order ID
• Mã khách hàng: Customer ID
• Tên khách hàng: Customer name
• Mã PKKH: Customer segment code
• Mô tả Phân Khúc Khách hàng: Customer segment description
• Mã nhóm hàng: Product category code
• Tên nhóm hàng: Product category name
• Mã mặt hàng: Product ID
• Tên mặt hàng: Product name
• SL: Quantity sold
• Đơn giá: Unit price
• Thành tiền: Total revenue per line item

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Tools & Technologies

• Language: Python
• Libraries: pandas, numpy, matplotlib, seaborn
• Tools: Tableau
• Notebook: Jupyter Notebook (.ipynb)

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Project Workflow

1. Data Understanding:

   • Loaded dataset and reviewed column meanings, data types, and business context.

2. Exploratory Data Analysis (EDA):

   • Analyzed total sales by product category and individual product.
   • Identified top-selling products and highest revenue contributors.
   • Evaluated sales distribution across customer segments.
   • Explored order frequency and customer purchase patterns.

3. Insight Generation:

   • Generated actionable business insights (e.g., key customer groups, best-selling items, seasonal trends).
   • Suggested strategic recommendations to increase sales and optimize product offerings.
   • Compiled actionable recommendations in the code_insights_decisions.ipynb

4. Visualization:

   • Created bar plots, pie charts, and trend lines to visualize key metrics and support storytelling.
   • You can refer to this link Tableau Public Dashboard or tea_sales_dashboard.twbx.

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Key Insights (Preview)

• Certain customer segments (e.g. office workers aged 36–45) contribute significantly to high-value orders.
• Bột cần tây (celery powder) is a consistently top-selling product, indicating strong demand in detox and health-focused customer groups.
• Combo sets (e.g. trà hoa cúc trắng set) are popular purchases, suggesting potential for bundling promotions.
• Sales volume is dominated by core SKUs, implying inventory planning should prioritize these items for stock availability.
• For full insight breakdowns and strategic recommendations, please refer to the code_insights_decisions.ipynb included in this repository.

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Learning Outcomes

• Practiced exploratory data analysis techniques on real business datasets.
• Developed data storytelling skills to communicate insights effectively.
• Strengthened ability to translate raw sales data into strategic business decisions.

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Files

• Dataset: data_tea_sales.csv
• Analysis Notebook: code_insights_decisions.ipynb

For full insights, visualizations, and recommendations, please refer to the Jupyter Notebook included in this repository.