To describe (step by step) how to create a dashboard in Power BI for CRM (such as CLV, RFM, customer segmentation, and Cohort, etc.) and share it with Medium.

This project analyzes online retail transaction data to identify distinct customer segments using RFM (Recency, Frequency, Monetary) analysis and calculates Customer Lifetime Value (CLV) using Predictive CLV models.

End-to-end retail analytics with PostgreSQL KPIs, CLV/RFM segmentation, and JSON data exports. Interactive dashboard built using HTML/CSS/JS and Chart.js. Covers sales trends, customer value, store ranking, and product performance.

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Predicting Customer Lifetime Value (CLV) using machine learning techniques based on purchase history, frequency, recency, and demographics. Includes customer segmentation using RFM analysis and clustering.

E-Commerce Customer Segmentation & CLV Analysis - RFM Segmentation, Customer Lifetime Value Prediction

RFM-T Analysis & CLV-based Customer Segmentation for Marketing Strategy

Customer Lifetime Value (CLV) prediction and customer segmentation using RFM analysis and machine learning.

Capstone project on customer segmentation using RFM, CLV, clustering, and ML models

E-commerce customer analytics — RFM segmentation, churn prediction, SHAP, CLV forecast, Plotly Dash · Python · SQL

Customer segmentation pipeline using K-Means, RFM analysis, CLV estimation and business strategy generation for telecom

End-to-end Customer Intelligence System using RFM Segmentation, Churn Prediction, and CLV Modeling with an interactive Streamlit dashboard.

End-to-end data analytics project on Customer RFM Segmentation and CLV modeling using SQL, Python ETL, and Power BI insights.

Customer Segmentation and Retention Analysis using RFM and CLV modeling — built with Python, SQL, and Power BI to generate actionable business insights.

End-to-end Customer Lifetime Value (CLV) prediction system using RFM features, BG/NBD, Gamma–Gamma, and XGBoost models on the UCI Online Retail dataset, with customer segmentation for marketing optimization.

This Customer Lifetime Value (CLV) Analysis project leverages Python libraries including Lifetimes, Pandas, and Scikit-learn to predict and segment customer value over time. By applying RFM modeling, BG/NBD, Gamma-Gamma model, and customer segmentation, to enable targeted marketing strategies.

An end-to-end customer analytics project using the Online Retail II dataset. This work features RFM segmentation, churn prediction with XGBoost, Customer Lifetime Value (CLV) forecasting with BG/NBD & Gamma-Gamma models, and statistical A/B testing.

This project analyzes e-commerce customer behavior to predict lifetime value (CLV) and purchase probability. Using RFM analysis and machine learning, it offers insights for targeted marketing, customer segmentation, and retention strategies. An interactive dashboard provides real-time analytics.

End-to-end Customer Lifetime Value (CLV) Prediction & Retention Analytics System built with Python, XGBoost, and Streamlit — includes RFM segmentation, cohort analysis, persona insights, model monitoring, drift detection, logs analytics, and automated executive summary reporting.

End-to-end SaaS customer analytics project covering cohort retention, revenue analysis, RFM segmentation, and customer lifetime value (CLV) modeling. Transforms raw transaction data into actionable insights with automated visual dashboards and growth-focused financial metrics to support data-driven decision making.

End-to-end MLOps pipeline for e-commerce customer analytics. It uses the Online Retail II dataset to run RFM segmentation, churn prediction, and CLV modeling on Spark. Airflow orchestrates the workflow, MLflow tracks experiments and models, DVC versions data, and Streamlit provides an interactive UI—services are containerized with Docker.

Customer Insights, RFM Segmentation & CLV Prediction

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This project focuses on calculating Customer Lifetime Value (CLV) using a cohort-based approach for a more accurate and actionable analysis. Leveraging SQL and Google Sheets, the analysis includes weekly revenue per user, cumulative revenue trends, and future revenue predictions for 12-week cohorts.

Customer Lifetime Value and RFM analysis project

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"Analyze customer behavior using RFM and CLV models for effective profiling. This project integrates RFM segmentation with Customer Lifetime Value (CLV) analysis to create detailed customer profiles, visualize insights, and develop targeted marketing strategies. Includes data, code, and visualizations

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