Data Analyst with strong experience in SQL, Python, Power BI, DAX, and data modeling I focus on turning raw data into dashboards that help teams make confident decisions. My work both business intelligence and advance analytics, enabling me to explore complex datasets, indentify behavioral trends, and translate data into recommendations that support read-world strategy
Conducted a large-scale behavioral analysis on 10,000+ anime titles to identify the factors driving audience growth in the streaming era. Evaluated release timing, episode structure, and viewer ratings to uncover patterns that inform strategic licensing decisions. The analysis reveals a 7x increase in typical audience size post-2010 and highlights mid-length series as the strongest performers — demonstrating how data can guide smarter content acquisition.
Analyzed the complete user funnel from acquisition to paid conversion for a SaaS-style product. Evaluated 5,000+ users across multiple funnel stages to identify drop-offs, activation delays, and monetization gaps. The dashboard reveals opportunities to improve paid conversion by ~6–8% and optimize revenue through better funnel design.
Developed an operations-focused dashboard to monitor warehouse efficiency and inventory health. Tracked order fulfillment rates, inventory turnover, and demand volatility at the SKU level. The analysis supports operational decisions that can reduce overstock/understock risk by ~20% and improve fulfillment performance.
Designed a demand forecasting and reorder planning system to minimize stockouts and excess inventory. Modeled demand patterns for 300+ SKUs, calculated dynamic reorder points, and flagged high-risk items. Insights from the dashboard help reduce stockouts by ~30% while optimizing inventory holding costs and improving supply chain efficiency.
Implemented RFM segmentation to classify customers based on recency, frequency, and monetary value. Segmented 10K+ customers into actionable groups such as Champions, Loyal, At-Risk, and Churned. The insights enable targeted marketing strategies that can increase repeat purchase rates by ~22% and improve campaign ROI.
Built a cohort-based retention and LTV model to understand long-term customer behavior. Analyzed 24 months of user data to track churn, retention curves, and revenue contribution by cohort. The analysis identifies high-value customer segments and supports strategies that can improve retention by 10–15% and maximize lifetime revenue.
End-to-end sales performance analysis built to identify revenue drivers, margin leakage, and growth opportunities. Analyzed 50K+ transactions across products, regions, and time periods, enabling leadership to optimize pricing and product mix. The dashboard highlights trends that support a ~18% profit improvement and improved YoY revenue visibility through automated KPI tracking.