Download the dataset below to solve this Data Science case study on RFM Analysis.
RFM analysis is a powerful technique used by companies to better understand customer behaviour and optimize engagement strategies. It revolves around three key dimensions: recency, frequency, and monetary value. These dimensions capture essential aspects of customer transactions, providing valuable information for segmentation and personalized marketing campaigns.
The given dataset is provided by an e-commerce platform containing customer transaction data including customer ID, purchase date, transaction amount, product information, ID command and location. The platform aims to leverage RFM (recency, frequency, monetary value) analysis to segment customers and optimize customer engagement strategies.
Your task is to perform RFM analysis and develop customer segments based on their RFM scores. The analysis should provide insights into customer behaviour and identification of high-value customers, at-risk customers, and potential opportunities for personalized marketing campaigns.
