RFM (Recency, Frequency, Monetary) analysis is a proven marketing model for behavior based customer segmentation. It groups customers based on their transaction history – how recently, how often and how much did they buy. RFM helps divide customers into various categories or clusters to identify customers who are more likely to respond to promot…

Trying out different analysis algorithms, at my times in college

Assessed brand loyalty patterns and price elasticity metrics to provide insights for brand’s market growth. Recommended strategies for the top 3 brands based on competitor analysis, customer profiling and customer retention through RFM analysis and multinomial logit.

This repository contains my work, related to machine learning and development done in hadoop eco system

EDA and customer segmentation with RFM analysis on hacker earth dataset. https://www.hackerearth.com/challenge/hiring/LMG-analytics-data-science-hiring-challenge

Easily install and load all Rsquared Academy R packages.

RFM Analysis

Portfolio of my Data Science projects

Python script (and IPython notebook) to perform RFM analysis from customer purchase history data

K-means as an unsupervised machine learning technique. Customer Segmentation Case.

This repository contains RFM analysis applied to identify customer segments for global retail company and to understand how those groups differ from each other.

Retail Sales Data Analysis

Basic RFM model to kickstart customer value segmentation. This project aims to guide the first time user to have a bouncing board into setting up their first segmentation model.

Study of customer preference, price elasticity and customer segmentation using RFM

Segmented customers based on Recency,Frequency & Monetary Value (RFM) metrics using K-means clustering algorithm

Methods for doing customer analytics in R

Recency, Frequency, Monetary Value of Customers