Welcome to the Superstore Customer Segmentation repository! 🎉
This project is a collaborative initiative brought to you by SuperDataScience, a thriving community dedicated to advancing the fields of data science, machine learning, and AI. We are excited to have you join us in this journey of learning, experimentation, and growth.
In this repository, you’ll find the work carried out by our community members in completing an end-to-end machine learning project. Please note that the resources available here is not to be used or copied without referencing the appropriate authors.
By utilizing clustering algorithms, we aim to provide actionable insights into customer preferences and behavior patterns, which will ultimately drive more effective customer engagement and increase profitability.
Understand Customer Behavior: Identify key patterns in customer purchasing habits, such as frequency of purchases, total spend, product preferences, and discount sensitivity. Create Customer Segments: Group customers into distinct segments based on their transactional data (e.g., purchase history, frequency, order quantity, discount usage, and profitability). Analyze Customer Segments: Understand the characteristics of each segment to tailor business strategies for different customer groups, such as high-value customers, occasional buyers, and discount-driven shoppers. Provide Strategic Recommendations: Based on the segmentation results, suggest marketing, sales, and retention strategies to target each customer segment effectively.
https://www.kaggle.com/datasets/jacopoferretti/superstore-dataset
- Who are the high-value customers?
- Which customer groups are most likely to respond to discounts or promotional offers?
- How can we improve customer retention by segment?
- What are the differences in customer behavior by region or segment?
- How can we personalize marketing strategies for different segments?
Data Exploration & Preprocessing (Week 1)
- Understand the dataset, clean the data, and prepare it for analysis.
Modeling & Segmentation (Week 2)
- Engineering new features (feature engineering) such as
- Segment by Geographic location
- Segment by Purchase Behavior
- Segment by Customer lifetime value (CLV), average order value (AOV) (Optional)
- Apply clustering algorithms (K-Means, DBSCAN) to segment customers into distinct groups.
Segment Analysis & Interpretation (Week 3)
- Analyze each segment’s characteristics and behaviors to draw actionable insights.
- For 3: High/M/Discount seekers
Reporting & Recommendations (Week 4)
- Create the final report and provide strategic recommendations for customer targeting (i.e. marketing campaigns)and retention strategies (i.e. upselling/cross selling)