| Name | ID | Department |
|---|---|---|
| Fares Hazem Shalaby | 20221443356 | Intelligent Systems |
| Ahmed Yousri Ali | 2103108 | Intelligent Systems |
| Ahmed Dawood Mohamed | 20221454408 | Intelligent Systems |
| Ahmed Ashraf Mohamed | 2103134 | Intelligent Systems |
An online retailer wants to segment its customer base into distinct groups based on purchasing behavior and demographics. The goal is to gain insights for developing targeted marketing strategies tailored to each segment's preferences and needs. The company has access to historical transaction data including customer information and order details.
The key tasks involve:
- Data preprocessing
- Feature engineering (e.g., RFM metrics, average order value, customer lifetime value)
- Applying clustering algorithms (e.g., K-means, hierarchical clustering) to segment customers
- Profiling and interpreting the characteristics of each segment
- Validating the identified customer segments
- Leveraging segment insights for targeted marketing campaigns, personalized recommendations, and promotions.
Successful implementation will enable better understanding of the customer base, optimized marketing efforts, and enhanced customer satisfaction and loyalty.
The data-set chosen is Online Retail. The data-set is from UCI Machine Learning Repository, it's associated with "Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining" paper. It's variables contains no missing values which will make suitable for a university project. There exists already a data-set on kaggle that contains UCI MLR's data, Customer Segmentation Dataset. Moreover, it's well maintained and suits our problem stated above.