RFM (Recency, Frequency, Monetary) analysis is a proven marketing model for behaviour based customer segmentation. It groups customers based on their transaction history in other terms– how recently (R), how often (F) and how much (M) did they buy.
In this project, a RFM model is implemented to relate to customers in each segment. Assessed the Data Quality, performed EDA using Python .
The idea of this challenge was to cluster customers based on a given dataset to align the marketing efforts. Four customer groups were characterized based on income, buying power, credit score, and other criteria
Bank Campaign Optimization: Targeting Term Deposit Customers
It is highly related to the Customer Segmentation problem, so with RFM Analysis itself as well
SegmentWise: Unveiling Customer Insights for Exploratory Data Analysis (EDA) and Customer Segmentation
Data Analytics virtual internship programme by KPMG AU on Forage.
The goal of the project is to group consumers into clusters using the elbow approach. The project also includes scatter plots to show the relationships between the variables and dataset's columns.
This repository contains project materials for the Spring 2024 STAT 208 class, specifically for Team 8. All materials are the property of Team 8, University of California, Riverside, A. Gary Anderson School of Management. Thank you for viewing our repository.
Analyzing a dataset containing data on various customers' annual spending amounts of diverse product categories for internal structure. Doing so would equip the distributor with insight into how to best structure their delivery service to meet the needs of each customer.
Customer segmentation using Python and PowerBI
Capstone Project for the Data Scientist Nanodegree by Udacity.
This is a project for the course "Machine Learning" - Master's degree in Data Science, University Milano-Bicocca
This repository contains data analytics projects that are important to our day to day Machine learning activities
Analyzing retail sales data to craft targeted marketing, elevate customer experiences, and forecast future sales.
This is a Customer Segmentation model made in Python
The purpose of this project was to perform customer segmentation on mall customers using sklearn Kmeans algorithm. Exploratory data analysis was first performed on the dataset to understand the data. Silhouette analysis was then used to determine the best number of clusters using age, annual income and spending score assigned to customers based …
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
Customer Segmentation using RFM analysis
Customer Segmentation Report for Arvato Financial Services. Analyzing demographics data for customers of a mail-order sales company in Germany and identifying most suitable parts of general population whom are most likely to be converted to customers through marketing campaigns.
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