A curated list of awesome customer analytics content

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

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…

This repo contains unsupervised models including the Latent Dirichlet Allocation (LDA) model applied to a corpus of research papers and a clustering analysis applied to customer segmentation.

This project is based on Unsupervised Learning

Customer Segmentation Anaylsis

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.

Customer Segmentation using Clustering (Machine Learning)

Applied Unsupervised Learning techniques on product spending data collected for customers of a wholesale distributor to identify customer segments hidden in the data.

These are all the assignments from Udacity Nanodegree Machine Learning course

Customer segmentation is a process where we divide the consumer base of the company into subgroups. We need to generate the subgroups by using some specific characteristics so that the company sells more products with less marketing expenditure.

We apply PCA transformations to the data and implement clustering algorithms to segment the transformed customer data

Code to perform clustering using self organizing maps on retail customer data.

Machine Learning Engineer Nanodegree, Unsupervised Learning, Creating Customer Segments

This project identify segments of the population that form the core customer base for a mail-order sales company in Germany. These segments can then be used to direct marketing campaigns towards audiences that will have the highest expected rate of returns. The data has been provided by Bertelsmann Arvato Analytics.

This project uses unsupervised learning techniques and decomposition methods to find meaningful structure in the data.

Reviewed unstructured data to understand the patterns and natural categories that the data fits into. Made predictions about the natural categories of multiple types in a dataset, then checked these predictions against the result of unsupervised analysis.

Projects of Udacity's Machine Learning Specialization Nanodegree program.

Project done in ML course

In this project, you will work with real-life data provided to us by our Bertelsmann partners AZ Direct and Arvato Finance Solution. The data here concerns a company that performs mail-order sales in Germany. Their main question of interest is to identify facets of the population that are most likely to be purchasers of their products for a mail…