E-commerce Customer Transactions for RFM Analysis

Customer transaction data supports RFM analysis, churn prediction, and segmentation tasks. The dataset is sourced from Kaggle, but specific details on volume, author, and recency are not provided.

Use Cases

• Perform RFM segmentation using transaction recency, frequency, and monetary value columns.
• Train churn prediction models using features like last purchase date and purchase frequency.
• Analyze customer cohorts based on transaction history and behavioral metrics.
• Build recommendation systems using customer purchase sequences and product categories.

Strengths

• Designed for core e-commerce analytics tasks like RFM and churn prediction.
• Focuses on actionable customer behavior metrics for segmentation.

Limitations

• Unknown row count prevents assessment of statistical power.
• Unspecified columns and sample data limit understanding of feature completeness.
• Lack of provenance details introduces uncertainty about data quality and bias.

Provenance

Source

Kaggle