Performed precise RFM Analysis of insightful information about consumer behavior, purchase trends, and satisfaction for targeted marketing and retention strategies. The project is to perform RFM analysis on the E-commerce dataset, segmenting customers into distinct groups based on their RFM scores. These segments will serve as valuable indicators for refining marketing strategies and improving customer retention efforts.
Dataset - The dataset consists of 541,909 records spread across 8 columns. https://www.kaggle.com/datasets/carrie1/ecommerce-data
Imported the dataset and performed necessary data preprocessing steps, including data cleaning, handling missing values, and converting data types if needed.
Calculated the RFM metrics for each customer: Recency (R): How recently a customer made a purchase. Calculated the number of days since the customer's last purchase. Frequency (F): How often a customer makes a purchase. Calculated the total number of orders for each customer. Monetary (M): The total monetary value of a customer's purchases. Calculated the sum of the total price for each customer.
Assigned RFM scores to each customer based on their quartiles for each RFM metric. Combined the RFM scores to create a single RFM score for each customer.
Used clustering techniques to segment customers based on their RFM scores. Experimented with different numbers of clusters to find the optimal number that provides meaningful segments.
Analyzed and profiled each customer segment describing the characteristics of customers in each segment, including their RFM scores and any other relevant attributes.
Provided actionable marketing recommendations for each customer segment. How can the business tailor its marketing strategies for each group to improve customer retention and maximize revenue?