A customer segmentation project can be approached in multiple ways. In this repository, we will explore advanced techniques for defining clusters and analyzing the results.

This contains projects based on Algorithmic Marketing like Marketing Mix Modeling, Attribution Modeling & Budget Optimization, RFM Analysis, Customer Segmentation, Recommendation Systems, and Social Media Analytics

Modern PHP 8.3+ ecommerce platform built on Symfony, Doctrine DBAL, Laminas, 100% vanilla JS. Drop-in replacement for Magento 1 projects with full compatibility. Complete toolchain: Composer/PHPStan plugins, language packs. Enterprise features: automated email marketing, customer segmentation, dynamic categories, passkey/2FA auth and so much more.

The objective of this project is to analyze the 3 million grocery orders from more than 200,000 Instacart users and predict which previously purchased item will be in user's next order. Customer segmentation and affinity analysis are done to study customer purchase patterns and for better product marketing and cross-selling.

Algorithmic Marketing based Project to do Customer Segmentation using RFM Modeling and targeted Recommendations based on each segment

Customer-Segmentation Project

An end-to-end project on customer segmentation

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 repository contains the code and resources for the supermarket customer segmentation project. Using clustering techniques, data visualization, data exploration, and ETL processes, this project aims to segment supermarket customers based on their purchasing behavior to enable targeted marketing strategies and improve customer service.

A collection of SQL-based data analytics projects demonstrating techniques for data extraction, transformation, analysis, and visualization. These projects showcase practical SQL queries and insights across various domains, including sales, customer segmentation, and employee attrition analysis.

This project requires to develop a customer segmentation to define marketing strategy. The sample dataset summarizes the usage behavior of about 9000 active credit card holders during the last 6 months. The file is at a customer level with 18 behavioral variables

This project aims to utilize Generative AI for the next marketing strategy in the case of e-commerce customer segmentation.

Analysis of an E-Commerce customer database that lists purchases made by over 4000 customers over a period of one year, developed a model that allows to anticipate the purchases that will be made by a new customer, during the following year, from its first purchase.

An end-to-end data analytics project using SQL, Python, and Power BI to perform customer segmentation

This project is related to customer segmentation for loans

Customer Segmentation Recommendation is a Machine Learning based project which analysis user data and preferences to provide product recommendations on a micro-segment level and also helps the marketing team in making better marketing-business strategy by analysing its customers based on their buying behaviour.

This is a data analysis project with price prediction with Random Forest Algorithm of cars in Indian market and also consist of customer segmentation with the help of Clustering analysis with KNN algorithm

This project shows how to perform customers segmentation using Machine Learning algorithms. Three techniques will be presented and compared: KMeans, Agglomerative Clustering ,Affinity Propagation and DBSCAN.

A Streamlit App for Customer Segmentation Project using Kmeans Clustering (Best Choice)

Advanced SQL analytics project extending prior EDA work. Includes change-over-time, cumulative trends, performance benchmarking, segmentation, part-to-whole analysis, and customer/product analytical reporting using window functions and real-world data warehouse logic.

Customer segmentation using K-Means clustering based on age, income, frequency, and spending.

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.

CustomerScope is a machine learning project for customer segmentation using clustering techniques. It helps analyze customer behavior and visualize meaningful customer groups.

In this project, based on the personal and purchase behavior data of store customers, we aim to segment customers into different clusters using clustering algorithms like DBSCAN and K-Means. This segmentation helps the store understand and optimize customer relationships, sales strategies, and marketing campaigns.

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.

A simulated SQL-based financial analytics project focused on customer segmentation, transaction behaviour, and regional performance within a Nigerian banking context.

The aim of this project is to build customer segmentation models for a delivery app using Kmena and DBSCAN.

A BDM Capstone Project titled, "Customer Segmentation and Price Optimization Strategies for Singi's kitchen" which is based on a local community restaurant. The Project deals with Customer segmentation, Menu Score Analysis and Inventory Management. The methods applied are data-driven to produce robust strategies & results.

Empower your business with "PowerBI Customer Segmentation Analysis" – a dynamic Power BI project for in-depth customer segmentation. Unleash valuable insights, visualize trends, and optimize strategies with this robust repository, leveraging the full potential of your customer data.

This project involves segmenting customers using k-means clustering in Jupyter Notebook. Customer segmentation is a powerful technique used in marketing and business analytics to divide customers into distinct groups based on their behaviors, preferences, or demographics.