Customer Segmentation using RFM Analysis

Customer Segmentation Analysis with RFM, using Python and Power BI.

This code performs customer segmentation using RFM (Recency, Frequency, Monetary) analysis. It creates an RFM dataframe, determines optimal clusters with k-means, and orders clusters from worst to best. Customers are segmented into high, mid, and low value groups and visualized in a 3D plot for better marketing strategies.

By means of this project I am trying to create a value-based customer segmentation model using RFM(Recency, Frequency, Monetary) analysis in python using pandas, numpy and matplotlib

This project focuses on customer segmentation using RFM (Recency, Frequency, Monetary) analysis, a proven method for identifying and categorizing customers based on their purchasing behavior.

Customer Segmentation using RFM analysis

Marketing analysis using RFM analaysis, unsupervised clustering for automatic customer segmentation and association rules

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.

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I explored a sales dataset and generated various analytics and insights from customers' past purchase behavior. I used SQL to analyze sales revenue and create a customer segmentation analysis using the RFM technique.

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.

A production-scale customer segmentation system using RFM Analysis implemented in PostgreSQL and Python. The project analyzes 151K+ e-commerce records to segment 5,000 customers into 8 behavioral groups, enabling data-driven marketing strategies and identifying high-value and churn-risk customers.

Web App for Product Recommendation based on Market Basket Analysis and Customer Segmentation using RFM Analysis

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.

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Leverage eCommerce data with RFM segmentation. Uncover recency, frequency, and monetary patterns, refining marketing strategies. Apply robust clustering for precise segmentation, providing potent recommendations for enhanced retention and revenue. Explore and visualize data comprehensively.

This project segments customers based on their purchasing behavior using Recency, Frequency, and Monetary (RFM) metrics. Leveraging KMeans Clustering, it helps identify key customer groups such as high-value loyal buyers, at-risk customers, and occasional shoppers.

Customer segmentation using RFM analysis and KMeans Clustering

Customer Segmentation Using RFM Analysis and Order Cancellation Prediction

RFM customer segmentation analysis of £17.7M retail dataset using K-means clustering and Python

In this project, we explore the sales data for a retail company and generate various analytics and insights from customer's past purchase behavior. I used SQL to analyze sales revenue. We also perform customer segmentation analysis using the RFM technique.

Analyzing and predicting customer behavior in the UK retail sector using RFM segmentation, temporal analysis, and machine learning models like XGBoost, ExtraTrees, and LGBM.

I explore a sales dataset and generate various analytics and insights from customers' past purchase behavior. We go from analyzing sales revenue to creating a customer segmentation analysis using the RFM technique

Customer Segementation is used in marketing to better understand customers of a business and target them accordingly. Segmentation of customer can take many forms, based on demographic, geographic, interest, behavior or a combination of these characteristics. Segmentation for this analysis was conducted based on their purchase behavior, the features to be analyzed were Recency, Frequency and Monetary Value, (RFM) for short.

🔓 Harness the potential of customer segmentation using RFM analysis within NetSuite ERP's Saved Search. Uncover valuable insights into customer behavior and craft targeted marketing strategies. 🚀 Stay ahead of the competition by comprehending recency, frequency, and monetary value. 📊 Elevate your business with decisions rooted in data. 📈

In any Retail and E-Commerce industry one of the key elements in shaping the business strategy of a firm would be the understanding of customer behaviour. More specifically, understanding their customers based on different business metrics: how much they spend (revenue), how often they spend (frequency), are they new or existing customer, what are their favourite products, etc. Such understanding would in turn help direct marketing, sales, account management and product teams to support better the customer and improve the product offering in turn improving the business. RFM model proves to be one such way of understanding the customers. RFM analysis is a data-driven customer behaviour segmentation technique where RFM stands for Recency, Frequency, and Monetary value. The idea is to segment customers based on their last purchase (Recency), how often they’ve purchased in the past (Frequency), and how much they have spent (Monetary). All three of these measures have proven to be effective predictors of a customer’s which is also been used to perform better campaign’s which in turn helps to increase the business ROI.

Using Quintile and Python to make RFM analysis model for behavior based customer segmentation

Customer Segmentation using RFM Analysis and K-means clustering in Python