In this project, I performed customer segmentation using an RFM (Recency, Frequency, Monetary) analysis combined with K-Means clustering. By leveraging Python libraries such as pandas, seaborn, and scikit-learn, I segmented customers into distinct groups based on their purchasing behavior. This segmentation can help businesses tailor their marketing strategies, enhance customer satisfaction, and increase revenue. I recommend using these insights to target high-value customers with personalized marketing campaigns and retention strategies. The data used for this analysis is from Amazon articles.
Business Problem
The business problem addressed in this project is the need for effective customer segmentation to improve marketing strategies. In a competitive market, understanding customer behavior is crucial for personalized marketing efforts. By segmenting customers based on their purchasing history, businesses can identify high-value customers, predict future behaviors, and optimize resource allocation.
Methodology
Data Collection and Preprocessing:
Imported and cleaned the dataset using pandas.
Converted date columns to datetime format.
Filtered the dataset to keep only the most recent purchase date for each customer.
RFM Analysis:
Calculated recency, frequency, and monetary value for each customer.
Visualized RFM distributions using seaborn.
Outlier Removal:
Removed outliers using Z-scores.
Feature Scaling:
Standardized the features using StandardScaler.
K-Means Clustering:
Applied the Elbow method to determine the optimal number of clusters.
Performed K-Means clustering and evaluated the model using the silhouette score.
Results & Business Recommendations
Segmented customers into 4 distinct clusters based on their RFM values.
Visualized the clusters to understand their characteristics and behaviors.
The silhouette score of 0.436 indicates a good clustering performance.
Possible Next Steps
High-Value Customers: Focus on retaining and upselling to these customers through loyalty programs and exclusive offers.
Frequent Buyers: Encourage these customers to increase their purchase value through bundle deals and discounts.
Recent Buyers: Engage with these customers to build long-term loyalty with follow-up communications and personalized recommendations.
Low Engagement Customers: Develop targeted re-engagement campaigns to reactivate these customers.
Refinement of Clusters: Further refine the clusters by incorporating additional customer attributes such as demographics and engagement metrics.
Predictive Modeling: Build predictive models to forecast future customer behavior and lifetime value.
A/B Testing: Implement A/B testing for targeted marketing campaigns based on the clusters and measure their effectiveness.
Integration with CRM: Integrate the customer segments with the company's CRM system for real-time marketing automation and personalized communication.
Continuous Monitoring: Regularly update and monitor the clusters to adapt to changing customer behaviors and market trends.