In-depth customer behaviour, segmentation and marketing channel analysis using Python. Includes data cleaning, KPI calculations, customer segmentation, and visual insights for decision-making.
- Author: Amrita Veshin
- Email: amritav99@gmail.com
An end-to-end data analysis project aimed at understanding customer behaviour and evaluating marketing strategies through Exploratory Data Analysis (EDA), KPI measurement, segmentation, and visual insights.
📊 Built using Python in Jupyter Notebook — this project helps drive customer-centric business decisions through data.
- Overview
- Project Structure
- Key Highlights
- Getting Started
- Technologies Used
- Results & Insights
- License
- Contact
The notebook explores customer-related data across the following stages:
- Loading and inspecting datasets
- Data cleaning and EDA
- Key Performance Indicators (KPIs)
- Customer Segmentation
- Marketing Channel Analysis
- Final Visualizations
customer-segmentation-marketing-analytics/
│
├── Customer_Behaviour_and_Marketing_Analysis.ipynb # 📘 Main notebook
├── customers.csv # 📄 customers sample dataset
├── orders.csv # 📄 orders sample dataset
├── order_items.csv # 📄 order items sample dataset
├── products.csv # 📄 products sample dataset
├── website_sessions.csv # 📄 website sessions sample dataset
└── README.md # 📘 Project documentation
- 🔍 Exploratory Data Analysis (EDA) on customer attributes
- 📈 Custom KPI calculations (AOV, Conversion Rates, Retention)
- 🧩 Customer segmentation using RFM
- 📣 Marketing channel performance breakdown
- 📊 Matplotlib/Seaborn-powered visualizations
Ensure you have Python 3.8+ and the following packages:
pip install pandas numpy matplotlib seaborn- Python 3
- Pandas
- NumPy
- Matplotlib & Seaborn
- Jupyter Notebook
This analysis enables:
- Identification of high-value customer segments
- Detection of underperforming marketing channels
- Strategic insight into behavioural patterns
- Data-driven decisions for campaign optimization