Customer Personality Analysis & Segmentation
This project performs a comprehensive analysis of customer data to identify key segments and optimize marketing strategies. Through rigorous data cleaning, feature engineering (creating metrics like Total Spending and Tenure), and statistical testing (Kruskal-Wallis), the study reveals distinct behaviors across demographic groups. The findings highlight that older, highly educated customers constitute a high-value segment, providing a data-driven foundation for targeted marketing campaigns.
Key Metrics & Results
Project Overview
This project applies unsupervised machine learning techniques to segment customers based on demographic attributes and purchasing behavior. The analysis uncovers meaningful customer personas to aid strategic marketing decisions
The Problem
Customer data contained diverse demographic and purchasing behaviors, but lacked clear segmentation. This made it difficult to understand customer differences and derive meaningful insights for targeted marketing strategies.
The Solution
Applied unsupervised learning techniques to segment customers based on demographic and behavioral features. The resulting clusters revealed distinct customer personas and enabled data-driven marketing insights.
📊 Data Visualizations & Insights
Purchases by Age Group
Chart
Shows how total purchases vary across different age groups. The analysis highlights that customers aged 45 and above contribute the highest purchase volume, indicating stronger engagement among middle-aged and senior customers.
Purchases by Marital Status
Chart
Illustrates purchasing behavior across marital categories. Married customers account for the largest share of purchases, suggesting household and family status plays a significant role in buying behavior.
Customer Purchase Channels
Chart
Displays the distribution of purchases across store, web, and catalog channels. In-store purchases dominate, followed by web purchases, emphasizing the importance of an effective omnichannel strategy.
Customer Responses to Marketing Campaigns
Chart
Breaks down how customers engage with marketing efforts across different channels and interactions. Direct purchase channels show higher engagement compared to deal-based interactions, providing insight into customer response patterns.
Business Impact
- ✓Enabled data-driven customer segmentation by transforming raw demographic and purchase data into clear, interpretable customer groups.
- ✓Provided actionable insights for targeted marketing, helping identify high-value and behaviorally distinct customer segments.
- ✓Improved understanding of customer purchasing channels and engagement patterns, supporting more informed channel and campaign planning.
- ✓Identified key demographic factors (age, marital status) influencing purchase behavior, assisting personalization and audience targeting strategies.
- ✓Established a reusable customer analysis framework that can be extended to campaign optimization, churn analysis, or recommendation systems.
Technologies & Tools
Python
Core language for customer data analysis and segmentation
NumPy & Pandas
Data cleaning, preprocessing, and feature engineering
Matplotlib & Seaborn
Data visualization and insight generation
Jupyter Notebook
Interactive environment for exploratory data analysis
✨ Key Features
- ▸Customer behavior and demographic analysis
- ▸Purchase patterns by age, marital status, and channel
- ▸Marketing response and engagement insights
- ▸Insight-driven visual storytelling
⚡ Challenges & Solutions
⚠️Understanding High-Dimensional Customer Data
Solution Applied:
Performed structured exploratory data analysis using grouping, aggregation, and visual comparisons to break down complex customer attributes into interpretable insights.
⚠️Identifying Meaningful Customer Patterns
Solution Applied:
Analyzed purchasing behavior across age groups, marital status, channels, and campaign responses to uncover clear behavioral and demographic trends.
⚠️Communicating Insights Clearly
Solution Applied:
Designed intuitive visualizations and concise summaries to translate raw data into business-friendly insights for marketing decision support.
🚀 Future Enhancements
- →Add clustering for deeper customer segmentation
- →Introduce predictive modeling for customer behavior
- →Develop interactive dashboards for insight monitoring
- →Validate insights using controlled experiments
Interested in this project?
I'd love to discuss the technical details, methodology, and learnings from this project. Feel free to reach out to learn more!