This project presents an in-depth analysis of customer purchase behavior on Olist, an e-commerce platform, using data analytics techniques to uncover insights that can drive business decisions. By leveraging a comprehensive dataset of customer orders, payments, products, and more, this project delivers actionable insights into customer patterns, sales trends, and product performance.
The analysis showcases proficiency in data wrangling, cleaning, and visualization, making it valuable for roles focused on data-driven decision-making and customer analytics.
- Exploratory Data Analysis (EDA): Detailed exploration and visualization of the dataset to understand customer behavior and sales patterns.
- Customer Segmentation: Segmented customers based on purchasing frequency and monetary value, identifying key customer groups.
- Product Performance Analysis: Analysis of product sales trends, top-selling categories, and product popularity across regions.
- Payment Behavior: Insight into different payment methods used by customers and their correlation with order value.
- Data Visualization: Clear and informative visualizations to communicate insights effectively.
- Insights for Business: Actionable recommendations for improving customer retention, optimizing product offerings, and enhancing payment strategies.
The dataset used in this analysis is the Olist E-commerce Dataset, which includes the following key tables:
- Customers: Details of customer demographics and geographies.
- Orders: Information on customer orders including timestamps, statuses, and delivery times.
- Payments: Data related to payment methods and transaction values.
- Products: Information about product listings, categories, and their details.
- Sellers: Seller information, including geographical location.
- Reviews: Customer review data including satisfaction scores.
- Python: For data analysis, cleaning, and manipulation using libraries like pandas and numpy.
- Jupyter Notebook: To document and present the analysis.
- Matplotlib & Seaborn: For creating insightful visualizations.
- Scikit-learn: For customer segmentation and clustering analysis.
- Customer Demographics & Behavior: Analyzed customer distribution across regions and purchasing habits.
- Sales Trend Analysis: Insights into peak sales periods and overall sales growth.
- Product Category Analysis: Identified top-selling product categories and their contribution to revenue.
- Payment Methods: Investigated payment preferences and their impact on transaction success.
- Customer Segmentation: Used clustering techniques to group customers based on purchasing behavior.
The analysis includes several key visualizations:
- Customer distribution across geographies.
- Monthly sales trends over time.
- Heatmaps showing correlations between customer ratings and payment methods.
- Customer segments visualized through scatter plots.
- Clone the repository:
git clone https://github.com/vijayrangvani/Olist-Customer-Purchase-Behaviour-Analysis.git
- Install the required dependencies listed in
requirements.txt. - Run the Jupyter notebooks to explore the analysis and visualizations.
Olist_Customer_Analysis.ipynb: The main analysis notebook containing all the code, insights, and visualizations.requirements.txt: List of dependencies needed to run the project.
- Customer Patterns: Insights into customer distribution and buying habits across regions.
- Product Trends: Identified top-performing products and categories.
- Sales Optimization: Recommendations for improving payment methods and reducing transaction failures.
- Segmentation: Grouped customers based on purchasing behavior to target marketing efforts.