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 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

Companies often struggle to answer key questions: Which products generate the most revenue? When do customers buy the most? Which customers are most valuable? Which customers are likely to stop buying? This analysis uses SQL to transform raw sales data into insights that support better strategy, marketing, and customer retention.

Brazilian E-Commerce Sales & Customer Behavior Analysis

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

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

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

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

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

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

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)

A strategic analysis of customer retention and lifecycle value using SQL and Tableau. Features Cohort Analysis to track engagement over time and RFM Segmentation to identify high-value "VIP" customers versus at-risk segments.

UK data analytics portfolio — Retail Deep Dive, AI Marketing Pipeline, and ROI Optimizer. Python, SQL, Power BI.

End-to-end churn and retention analysis of League of Legends ranked players. A Python pipeline and Jupyter analysis on Riot API data with a logistic regression churn model, a SQL Server star schema, RFM segmentation in Excel, a published Tableau dashboard, and a benchmark against AI data tools.