Introduction In ecommerce companies like online retails, customer segmentation is necessary in order to understand customers behaviors. It leverages aqcuired customer data like the one we have in our case, transactions data in order to divide customers into groups. Our goal in this Notebook is to cluster our customers to get insights in: Increasing revenue (Knowing customers who present most of our revenue) Increasing customer retention Discovering Trends and patterns Defining customers at risk We will do RFM Analysis as a first step and then combine RFM with predictive algorithms (k-means). RFM Analysis answers these questions: Who are our best customers? Who has the potential to be converted in more profitable customers? Which customers we must retain? Which group of customers is most likely to respond to our current campaign? More about RFM here.

This project focus on customer analysis and segmentation. Which help to generate specific marketing strategies targeting different groups. RFM Analysis, Cohort Analysis, and K-means Clusters were conducted on a UK-based online retail transaction dataset with 1,067,371 rows of records hosted on the UCI Machine Learning Repository.

Data science project using the UCL Online Retail Dataset to perform customer segmentation based on spending behavior. Includes RFM analysis, K-Means clustering, customer profiling, and actionable business insights with visualizations.

Customer Segmentation based on RFM using K-Means & Hierarchical Clustering

After Covid-19, with the help of advances in the technology online shopping have become a part of daily life and it is expected to grow more all around the world. Accordingly Customer behavior are becoming more and more complex with the passage of time. With increasing competitor in the market, Retailers tries their best to hold their customer because attracting new customers cost several times then retaining the existing customer. For this purpose, Retailer analysis their customers purchase so that they can provide better service and maximize their profit margins. In this work, EDA of e-Retail data has been performed, using RFM analysis to identify the categorical segmentation of customers and Time Series Analysis with ARIMA Model to identify trends and clustering and classification models are implemented to identify the customers who are likely to churn. Furthermore, will also analyze top factors that influence user retention.

This project uses RFM (Recency, Frequency, and Monetary) segmentation to analyze customer behavior and provide insights for targeted marketing campaigns. By classifying customers based on their purchasing patterns, strategies can be tailored to improve customer retention, drive growth, and maximize the lifetime value of each customer.

Segmentation of customers in online retail databases using Python, including RFM analysis and clustering.

I used the online retail dataset from the UCI Machine Learning Repository for exploratory data analysis and customer segmentation using RFM Analysis K-Means Clustering.

Customer Segmentation in Online Retail using Python. Perfromed Cohorts Analysis & RFM+Kmeans

This project analyzes an ""Online Retail dataset"" to extract insights about sales, customers, products, and countries. The analysis includes data cleaning, exploratory data analysis (EDA), customer segmentation (RFM), and visualization.

An end-to-end customer segmentation project using RFM analysis and K-Means clustering on the Online Retail dataset. This repository covers data cleaning, cohort analysis, RFM feature engineering, and a comparison of rule-based vs. clustering-based segmentation to identify key customer groups.

Explore Online retail dataset and create customer segmentation using RFM (Recency, Frequency, Monetary) analysis. This is a transnational data set which contains all the transactions occurring between 01/12/2010 and 09/12/2011 for a UK-based and registered non-store online retail.The company mainly sells unique all-occasion gifts. Many customers of the company are wholesalers.

Performing Customer Segmentation in Online Retail dataset using Python and understanding purchase patterns through RFM Analysis and K - Means Clustering

Machine learning model to identify major customer segments for an online retail company.

RFM customer segmentation analysis using Online Retail dataset (UCI)

Utilized a year-long dataset of UK-based online retail transactions to perform RFM analysis (Recency, Frequency, Monetary) for customer segmentation. Insights guide targeted marketing, enhance engagement, and contribute to effective customer retention for a non-store retail company specializing in unique all-occasion gifts.

Customer segmentation using RFM analysis on online retail data

RFM analysis for customer segmentation using the Online Retail Dataset

Power BI dashboard analyzing online retail sales, customer segmentation using RFM analysis, and transaction-level insights from the UCI Online Retail dataset.

Exploratory data analysis of the Online Retail II dataset . Includes data cleaning, visualization, and customer segmentation (RFM analysis) using Python (Pandas, Matplotlib, Seaborn).

"Customer segmentation project using RFM analysis and K-Means clustering on online retail data. Built with Python, Scikit-learn, and PCA."

Repo for RFM (Recency, Frequency & Monetary) analysis and customer segmentation using the Online Retail II dataset from the UCI Machine Learning Repository.

Customer segmentation project using RFM (Recency, Frequency, Monetary) analysis on the UCI Online Retail dataset. Includes data cleaning, feature engineering, customer grouping, marketing strategy suggestions, and visualizations.

Using the [Online Retail dataset](https://archive.ics.uci.edu/ml/datasets/Online+Retail) from the UCI Machine Learning Repository for exploratory data analysis, ***Customer Segmentation***, ***RFM Analysis*** and ***Clustering*** with machine learning unsupervised algorithms

This project analyzes online retail transactions to extract meaningful business insights. It involves data cleaning, exploratory data analysis (EDA), and customer segmentation using RFM (Recency, Frequency, Monetary) analysis.

This project does customer segmentation and market basket analysis by applying rfm model, kmeans and apriori algorithm on the Online Retail dataset from kaggle. Link to dataset : https://www.kaggle.com/puneetbhaya/online-retail

RFM customer segmentation and marketing channel performance analysis using the UCI Online Retail II dataset. Segments 3,745 customers into 8 behavioural groups and identifies where marketing budget should go.

Customer segmentation using K-Means clustering on the Online Retail II dataset. The project performs RFM analysis, data preprocessing and unsupervised learning to generate actionable customer clusters for targeted marketing and retention.

It applies **RFM (Recency, Frequency, Monetary) analysis** to the **Online Retail Dataset (UCI)** to segment customers based on their purchasing behavior. The segmentation provides insights into **loyal customers, at-risk customers, and dormant customers**, enabling targeted marketing strategies.

Customer Segmentation Project for an E-Commerce Company using the Online Retail Dataset. The project includes data cleaning, exploratory data analysis (EDA), and RFM analysis to identify key customer segments for marketing and business insights. Tools Used: Excel, Tableau