Trip.com Global Service Quality Analytics & ROI Simulation

Role: Senior Quality Analyst Assessment | Methodology: Python (Pandas, Matplotlib)

📌 Project Overview

This notebook contains the complete data processing pipeline, exploratory data analysis (EDA), and strategic ROI modeling for the Trip.com global service quality assessment. The objective is to transition from descriptive statistics to actionable, P&L-focused operational strategies.

🚀 Key Methodologies & Architectural Designs

1. Financial ROI Modeling: Designed and simulated a "Time-Tiered Compensation Matrix" to replace the historical flat-rate payout model. This algorithmic reallocation projects a 51.2% net budget reduction for P0 'Cannot Check-in' tickets while funding long-tail trust recovery.
2. Workload Anomaly Detection: Identified systemic routing bottlenecks (a uniform regional mix across all service teams) using multi-dimensional aggregation. This led to the architectural design of a 3-Tier Skill-Based Routing (SBR) Engine.
3. Vendor Defect Normalization: Engineered a 'Topic Share' normalization metric to bypass the limitation of missing absolute order volume. This isolated structural vendor quality gaps against internal direct-contract baselines.

📊 Visualizations Generated

• Combo Dashboard: Strategy Baseline vs. Financial ROI Waterfall Chart.
• Workload Matrix: Stacked bar charts revealing routing uniformity.
• Vendor Defect Radar: Trellis line charts highlighting structural supplier gaps.