The workflow starts by loading the dataset containing user transactions with details such as purchase date, amount, and category. The Group By RFM Features node groups data by user ID and calculates key metrics including purchase frequency, recency (days since the last transaction), and average intervals between purchases.

Additional attributes like total spend and customer age can be derived for richer behavioral analysis. The output, combining original and computed user-level features, is displayed through the Print N Rows node for validation.

This workflow helps uncover valuable user behavior trends and supports customer segmentation, retention, and lifetime value modeling.