Cohort and RFM (Recency-Frequency-Monetary) Analysis with Unsupervised Machine Learning models

Sales prediction for a segment of product.

This project involves performing customer segmentation and RFM (Recency, Frequency, Monetary) analysis on customer data from a retail company. The primary goal is to categorize customers into segments based on their buying behavior and identify potential target groups for marketing campaigns.

Our goals here are finding CLV each customer, segement customer using RFM and CLV, and making recommendation

NextBuyPredictor is a machine learning project designed to predict whether a customer will make their next purchase within a specified timeframe. By analyzing customer purchase history and behavioral patterns, this tool helps businesses forecast buying behavior, optimize marketing strategies, and improve customer retention.

RFM (Recency, Frequency, Monetary) analysis

To Identify Major Customer Segments On Transnational Dataset Using Unsupervised ML Clustering Algorithms

Predicted customer transactions using recency, frequency, spend behaviour and Social Network metrics over lifetime using MLlib

This project aims to perform customer segmentation and revenue prediction for a gaming company based on customer attributes. The company wants to create persona-based customer definitions and segment customers based on these personas to estimate how much potential customers can generate in revenue.

Applied SAS techniques for data analysis and machine learning in a milestone project. Base SAS Programming and SAS Viya tools were utilized for preprocessing, customer profiling, sales analysis, promotions, supplier evaluation, and customer segmentation. Results were visualized comprehensively.

This project focused on applying machine learning to build a clustering model to segment and analyze customer characteristics in the airline industry based on LRFMC scores using K-Means and suggest business strategy recommendations based on the results.