Ruby Ihekweme

Advanced End-to-end SQL analytics project demonstrating window functions, CTEs, cohort analysis, predictive modeling, Customer segmentation, RFM analysis, retention cohorts, time-series forecasting, and recursive queries on e-commerce data

A full stack customer analytics and forecasting project built in Python to analyze e-commerce purchasing behavior, segment customers using RFM (Recency, Frequency, Monetary) analysis, and forecast future sales trends. The project demonstrates a complete data workflow. From cleaning and feature engineering through analytics, visualization, etc.

Advanced SQL analysis on fintech fraud detection: engineered queries for anomaly detection, risk segmentation, and suspicious pattern identification across transactional datasets. Includes multi-stage logic, score computation, and diagnostic outputs to support real-time risk analytics and intervention workflows.

Comprehensive analysis of the company’s performance across revenue, profit, product categories, store locations, and seasonal trends. Advanced data modeling and DAX measures were implemented to empower strategic product decisions and cross-functional planning.

A comprehensive sales analysis in Excel, featuring advanced pivot modeling, KPI computations, trend insights, and segmentation logic to uncover performance patterns and strategic levers. Designed for data backed evaluation of revenue, product mix, and business behavior.

Comprehensive product performance analytics, leveraging optimized data models and advanced DAX calculations. Likewise, interactive dashboards and drill-throughs to deliver actionable insights, across sales, product strategy, and executive reporting.