Intelligent Customer Service Ticket Analysis System: End-to-End Machine Learning Practice from Priority Prediction to Escalation Risk Identification

This article deeply analyzes a complete intelligent customer service ticket analysis project, covering three core tasks: priority classification, escalation risk prediction, and resolution time estimation. It demonstrates how to use classic algorithms such as decision trees, random forests, and logistic regression to build a practical enterprise-level prediction system, addressing pain points like low efficiency of traditional manual sorting and delays in urgent issues, and helping customer service teams allocate resources rationally and respond to problems promptly.

Customer service ticket management faces core pain points of information overload and response delays: manual processing relies on experience and intuition, lacks unified standards, and easily leads to key issues being overlooked. The goal of this project is to build an end-to-end intelligent analysis system that automatically completes three prediction tasks—ticket priority classification, escalation risk identification, and resolution time estimation—to assist customer service teams in rational resource allocation and timely problem response.

The project uses a combination of classic machine learning algorithms:

• Priority classification: Compare decision trees (easy to understand) and random forests (reduces overfitting, better performance);
• Escalation risk prediction: Modeled as a binary classification problem, using logistic regression (high efficiency, strong probabilistic interpretability, focusing on recall);
• Resolution time estimation: Under the regression framework, compare linear regression (baseline), ridge regression (alleviates multicollinearity), and random forest regression (captures non-linear interactions).

Features are extracted from three dimensions:

• Structured features: Customer level, number of affected users, number of historical tickets, channel type, etc.;
• Text features: Heuristic rules (text length, word count, urgent keyword marking), lightweight and low-latency;
• Data labels: Since the original data has no labels, synthetic data is generated using business rules (infer priority, escalation status, and resolution duration based on customer level, impact scope, and keywords).

Evaluation metrics for each task:

• Priority classification: Use accuracy, macro-average F1, classification report, confusion matrix; random forests have stronger generalization ability;
• Escalation risk prediction: Focus on recall (to ensure fewer missed detections) and ROC-AUC (discrimination ability), balance precision and recall through threshold tuning;
• Resolution time estimation: Evaluate using MAE (mean absolute error in hours, easy to interpret), RMSE, and R².

Current tech stack: Python (Pandas, NumPy, scikit-learn, Matplotlib, Jupyter), full process verification completed; Future plans: Build FastAPI RESTful interfaces for real-time deployment, use Docker containerization to ensure environment consistency and simplify operation and maintenance.

This project demonstrates the path to applying machine learning in customer service scenarios: reasonable task decomposition, business-aligned feature engineering and model selection—practical systems can be built without deep learning. The key lies in understanding business needs, combining technical indicators with business goals, and achieving a value loop where technology empowers business.