DATAANALYST

I’m Hyunchoul, a data analyst specializing in translating raw information into strategic insights that catalyze business expansion.

I empower organizations to leverage their marketing, sales, and product metrics to build robust systems for enhancing customer loyaty, maximizing transaction value, and scaling profitability.

Let's collaborate to convert your intricate datasets into streamlined, sustainable roadmaps that refine your operational efficiency and fast-track your path to success.

Mail
Velog
Tableau

Portrait of Hyunchoul, Data Analytics Consultant

My Services

📈 E-commerce Performance & Revenue Analysis
🧠 Customer Insights & Segmentation
🎯 Marketing Attribution & Campaign Optimization
📊 KPI Dashboards & Reporting Automation
🧹 Data Cleaning & Quality Assurance

Tools I Use

Excel

Tabeau

SQL

Python

Google Analytics

eCommerce Business Performance Analysis

NexaSphere’s 2019-2022 performance reveals a post-pandemic order collapse (-38%) and subsequent 28% revenue decline, though hero products like the 27 inches 4K Monitor and AirPods held resilient, driving $24M in sales. To reverse this, NexaSphere should launch dedicated care programs for the MacBook Air to mitigate its 14% return rate—especially targeting high-spending loyalty members. Simultaneously, deploying bundling strategies in the underpenetrated Latin American market (6% share) will diversify reach, normalize order volumes, and secure long-term profit stability.

View NexaSphere Analytics Project

Olist RFM Analysis for Revenue Optimization

I analyzed Olist’s customer data using SQL and RFM segmentation to uncover retention risks and delivery-related satisfaction issues. The analysis revealed that 97% of customers purchased only once, while the top 11% of spenders generated 46.5% of total revenue. Notably, the largest mid-tier segment suffered from longer delivery times and lower review scores. To drive retention and revenue growth, I recommended targeted win-back campaigns, VIP benefits for high spenders, and promoting faster-delivery products to dissatisfied segments.

View RFM Analysis Project

Regional Logistics Bottleneck Analysis in Brazil

I analyzed Olist logistics data and found a clear divide: the southeastern metropolitan area had a swift 10-day lead time, while outer regions took 25 to 30+ days. Notably, Rio de Janeiro (RJ) suffered from severe delay rates despite having a fast average shipping speed. A deep dive into the root causes revealed that 78% of all delays occurred during the carrier shipping phase. Furthermore, orders originating from PR and SP showed high delay ratios of 14% and 15% respectively, exposing critical bottlenecks in these specific routes. To fix this, I recommended establishing regional logistics hubs or optimizing delivery processes in the Northeast, and prioritizing supply chain improvements for the SP->RJ route.

View Logistics Analysis Project

Data Cleaning

I streamlined a fragmented 20,000-row e-commerce dataset into a robust, high-fidelity asset for strategic analysis. By standardizing disparate values and rectifying structural inconsistencies, I eliminated data noise and established a reliable foundation for data-driven decision-making.

View Data Cleaning Project