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

CLV PULSE - A DYNAMIC CUSTOMER LIFETIME VALUE PREDICTOR MODEL USING MACHINE LEARNING

📊🎯✨ Harness the power of the RFM (Recency, Frequency, Monetary) method to cluster customers based on their purchase behavior! Gain valuable insights into distinct customer segments, enabling you to optimize marketing strategies and drive business growth. 📈💡🚀

CRM Analysis of a E commerce company.

Segmenting customers using RFM model

Hotel Customer Segmentation and Behavioral Analysis

This is a basic workflow with CrewAI agents working with sales transactions to draw business insights and marketing recommendations. The agents will work on everything from the execution plan to the business insights report. It works with local LLM via Ollama (I'm using llama3:8B but you can easily change it).

This Program is for Clustering Customer Data On the Basis of their Spending, Income,Family and Children.

analyze the shopping behaviors and demographic profiles of customers visiting a mall using various clustering techniques.

 The goal of segmenting customers is to decide how to relate to customers in each segment in order to maximize the value of each customer to the business. The purpose is to understand customer response to different offers in order to come up with better approaches to sending customers specific promotional deals.

Customer Segmentation Python Project

Deploying clustering machine learning algorithms to segment survey respondents

This repository contains the data, code, and documentation for a project to analyze and predict churn in PowerCo's SME customer segment. The project includes data exploration, cleaning, and transformation, as well as the development and evaluation of a machine learning model to predict churn based on price sensitivity and other relevant factors.

Data quality assessment and insights generation

In this project, a RFM model is implemented to relate to customers in each segment. Assessed the Data Quality, performed EDA using Python and created Dashboard using Tableau.

This dashboard presents an overview of CUSTOMER data, trends and behaviour to understand customer segments and improve customer satisfaction. Building dashboard to help stakeholders, including salesmanager and executives to analyse Customers data respective to sales and profit and products bought.