rfm-customer-segmentation

RFM segmentation divides customers into groups based on Recency (last purchase date), Frequency (purchase count), and Monetary (total spending). This Skill automates RFM computation on CSV transaction data and applies K-means clustering to identify customer segments like VIP buyers, enabling targeted marketing campaigns.

Yes. This Skill uses the elbow method to automatically select the optimal number of K-means clusters from your transaction data, eliminating manual tuning and producing stable, reproducible customer segments.

RFM analysis requires CSV files with transaction-level data including user_id, order_date, quantity, and unit_price fields. The Skill handles data cleaning and Chinese character support, then outputs customer_segments.csv, visualizations, and VIP lists.

After RFM segmentation and K-means clustering, this Skill automatically scores customer segments and exports a VIP customer list alongside visualizations and segmentation summaries, ready for marketing use.

Yes. This Skill is built for Chinese datasets and provides full Chinese language support throughout RFM analysis, clustering, dashboard generation, and VIP customer reports.

RFM analysis relies on pandas and numpy for data processing, scikit-learn for K-means clustering, and matplotlib and seaborn for visualization and dashboard generation.