Three business analytics case studies were undertaken, encompassing market basket analysis, customer segmentation, and campaign management. SAS Visual Data Mining and Machine Learning on SAS Viya was utilized to explore data and provide insights. A comprehensive report addressing both technical and business aspects was delivered.

This project uses RFM (Recency, Frequency, and Monetary) segmentation to analyze customer behavior and provide insights for targeted marketing campaigns. By classifying customers based on their purchasing patterns, strategies can be tailored to improve customer retention, drive growth, and maximize the lifetime value of each customer.

End-to-end retail analytics: Azure Databricks + Power BI | Star schema, RFM segmentation, RLS

End-to-end SQL project analyzing e-commerce sales, product performance, and customer behavior

End-to-end e-commerce BI using SQL & Python: EDA, RFM customer segmentation & operational KPIs

End-to-end sales analysis on a 640K+ row e-commerce dataset using MySQL

SQL-powered customer behavior analysis using Instacart’s market order dataset. Includes RFM segmentation, market basket analysis, and reorder pattern discovery.

Brazilian E-Commerce Sales & Customer Behavior Analysis

Performing RFM customer segmentation and developing relevant marketing strategies for Flipkart using eCommerce data.

BA-style analysis built for Amazon's BA Intern role (Job ID 3100573) — revenue trends, customer RFM segmentation, delivery funnel & one-page recommendation brief. By Kavyanjali Karan | SOA University 2027

Dashboard built in streamlit for customer behaviour analysis, covering RFM, CLV and more

A marketing analytics tool combining Causal Inference, Marketing Mix Modeling (MMM), and RFM Segmentation to measure the true sales impact of a retail promotional campaign.

An end-to-end analysis of customer purchasing behavior

Explore Superstore sales data with MySQL database setup, data insertion, and cleaning. Perform EDA and RFM customer segmentation using Excel & SQL. #DataAnalysis #CustomerSegmentation #MySQL #EDA

SaaS Product Analytics Portfolio: funnel, cohort retention, A/B testing, RFM segmentation · Python · Streamlit · Plotly

Cut customer churn risk by identifying $2.1M at-risk CLV across 10,000 customers. Production-grade analytics platform: XGBoost churn prediction (AUC 0.858), RFM segmentation, 48-month cohort retention, revenue forecasting. Out-of-time validated. Live Streamlit dashboard.

Junior Data Analyst | Transforming raw retail data into automated business insights using SQL, Python (Pandas), and Power BI.

Customer segmentation project using the RFM (Recency, Frequency, Monetary) model to categorize customers based on purchasing behavior. Includes automated data cleaning, RFM scoring, business segment labeling, visualization, and K-Means clustering for unsupervised segmentation.

End-to-end retail analytics project — SQL RFM segmentation, K-Means clustering in Python, and a 3-page Power BI dashboard with DAX measures and conditional formatting. Dataset: UCI Online Retail (UK, Dec 2010–Nov 2011, 4,338 customers)