RFM-Engineered Customer Segmentation Dataset Derived from Digital Printing MSME Transaction History (XYZ Print: May 2024 – October 2025)

This dataset contains the processed Recency, Frequency, and Monetary (RFM) metrics derived from the raw transaction history of a digital printing Micro, Small, and Medium Enterprise (MSME), anonymized as XYZ Print, covering the period from May 2024 to October 2025. The dataset was engineered to support customer segmentation, behavioral analysis, and customer lifetime value (CLV) modeling in small-to-medium retail and printing operations. It serves as a primary feature-engineered dataset generated for thesis research on data-driven customer relationship management (CRM). Data Pipeline & Methodology Source Data: Raw transaction history containing individual sales records from May 2024 through October 2025. Preprocessing: Cleaned null values, duplicates, and cancelled/returned transactions. Aggregated sales figures per unique Customer ID. Feature Engineering: Recency (R): Number of days between the customer's last transaction and the observation cutoff date (October 31, 2025). Frequency (F): Total count of distinct transactions made by the customer during the 18-month period. Monetary (M): Total expenditure/revenue generated by the customer across all transactions (in IDR). File Structure & FieldsThe primary dataset (CSV format) includes the following attributes: CustomerId: Unique identifier for each anonymized customer. Recency: Days since last purchase relative to the end of October 2025. Frequency: Total number of orders placed between May 2024 and October 2025. Monetary: Total spent amount (IDR). Intended Use Cases Customer Segmentation: Clustering algorithms (e.g., K-Means, DBSCAN, Hierarchical Clustering). Benchmarking: Analyzing consumer purchase patterns in B2B/B2C digital printing services in emerging markets. Academic Research: Machine learning applications for MSME business intelligence and marketing strategy.