Segmentation Customer with Recency, Frequency, and Monetary (RFM) Analysis

Overview

This repository focuses on customer segmentation using Recency, Frequency, and Monetary (RFM) analysis. The RFM model is a marketing analytics tool that segments customers based on their transaction behaviors. This analysis helps businesses tailor their marketing strategies for different customer segments, optimizing engagement and maximizing revenue.

Methodology

The analysis employs RFM metrics to categorize customers into distinct segments based on the following criteria:

• Recency: How recently a customer made a purchase.
• Frequency: How often a customer makes a purchase.
• Monetary: The monetary value of a customer's purchases.

The RFM scores are calculated, and customers are segmented accordingly.

Data

• Customer Transaction Data
• Purchase History
• Customer Information

Analysis

SQL and Jupyter Notebooks or scripts showcasing the RFM analysis process, data processing, and segmentation techniques are included in this section.

Results

In the conducted RFM analysis, customers are segmented into different groups based on their transaction behaviors. The resulting segments provide insights into customer engagement, loyalty, and value. This segmentation enables targeted marketing strategies, personalized communication, and improved customer satisfaction.

Contributors

1. Petrik Siano Okta Prima Lesmana
2. Dinandara Aliya Rahma Heryandya
3. Diva Ramadhan Radityatama
4. Dwi Yashinta Inayah Putri
5. Sania Auliyana Diatri

License

This project is licensed under the Bitlabs Academy.

Usage

1. Clone the repository:

   git clone https://github.com/strigoimort/segementation-customer-with-rfm-analysis.git