analytic portfolio
what i have analysed
Great instincts open the door
Solid data keeps you in the room.
A look at how data turns into business results based on real performance from company with different scale.
CASE STUDIES
Health Care Business for Chronic Outpatient services.
Problem: Hospital leadership needed an evidence-based view of patient demand, demographic mix, financial exposure, and care continuity gaps to prioritize
capacity planning and chronic care programs.
Approach: Processed and analyzed 1 million chronic outpatient encounters spanning 2020–2025, covering volume trends, demographics, follow-up conversion, no-show patterns, and cost-outcome risk signals.
Found that typical demand runs at 98% of peak capacity with 87% stability even in the lowest months revealing a highly predictable baseline. Demographic analysis showed 56% of chronic patients are aged 50–80, guiding targeted resource allocation for older-adult care program.
Healthcare | Big Data | Patient Analysis
Gadget Bigshop E-Commerce Business Inteligence Analysis
Problem: An e-commerce smartphone retailer needed a full picture of where revenue and margin were actually being made or lost across products, campaigns, payment methods, logistics, and returns to guide pricing, promotion, and operational decisions.
Approach: Analyzed 250,000+ order records across 2024–2025, covering product mix, campaign funnels, payment and trade-in economics, delivery SLA by carrier and province, and return/warranty behavior.
Found that Ultra and Premium phones drive nearly all margin, while net margin sits at just ~7% after COGS and discounts. Identified that discounts beyond 5–10% flip orders into negative margin, and that late deliveries (45–47% of orders) account for over half of total revenue turning operational delay
into a hidden financial risk.
E-Commerce | Big Data | BI Strategy
Skincare Business Inteligence analysis Accross Southeast Asis’s Delivery
Problem: A Southeast Asia beauty e-commerce brand had a funnel that looked healthy on the surface high traffic, strong conversion, growing revenue but leadership suspected profit was quietly leaking
through discounts, slow fulfillment, and bundles that compressed net price.
Approach: Analyzed ~100,000 transactions from ~69,000 orders and ~20,000 customers across 23 months (2024–2025), covering product mix, channel performance, discount mechanics, daypart behavior, and fulfillment speed across Website, App, TikTok Shop, Instagram Shop, and Omni channel.
Found that Skincare drives the business as the clear P&L engine (~$740K revenue, ~$34 AOV), while Free Shipping outperformed all %OFF promotions (+3.2% revenue uplift vs. up to -4.5% for poorly designed bundles). Identified that weekdays carry 71% of revenue and evening hours (18:00–22:00) drive the strongest conversion providing a clear operating calendar for campaigns, bundling, and fulfillment priorities.
E-Commerce | Big Data | Strategi Growth
More project are still on going, and due to confidentiality i can’t open name of the premises.