Customer support centers process thousands of customer complaints every month. Manual ticket classification and routing slow response times, increase operational costs, and make it difficult to maintain Service Level Agreements (SLAs) during periods of high demand.
This project combines Natural Language Processing (NLP), Machine Learning, and Queue Simulation to automate ticket classification, optimize support operations, and improve customer service performance.
The simulation models customer support center operations under both normal and high-volume conditions, enabling management to evaluate staffing requirements and maintain SLA compliance.
Customer complaints arrive as unstructured text and must be reviewed before being assigned to the appropriate support team.
Manual ticket triage becomes a major operational bottleneck, particularly during system outages or periods of unusually high ticket volume.
Without automated classification, critical incidents may remain undetected while support agents spend valuable time processing routine requests.
Manual ticket routing negatively impacts both customer experience and business performance.
High ticket volumes significantly increase response times, resulting in missed Service Level Agreements and reduced customer satisfaction.
When urgent technical issues become buried beneath routine support requests, engineering teams receive delayed notifications, increasing downtime, financial losses, and reputational risk.
The objective of this project was to develop an intelligent support system capable of:
- Automatically classifying customer complaints
- Prioritizing high-impact incidents
- Improving ticket routing efficiency
- Forecasting support center capacity under different workload scenarios
More than 62,000 customer complaints were analyzed to understand complaint categories, customer concerns, and operational trends.
Natural Language Processing techniques were applied to clean and transform unstructured complaint text.
The project included:
- Text Cleaning
- Tokenization
- Bigram Analysis
- Feature Engineering
- Machine Learning Classification
The classification model automatically predicts:
- Ticket Category
- Priority Level
- Appropriate Support Team
A queue simulation was developed to evaluate support center performance under varying ticket volumes.
The simulation compared:
- Normal operating conditions
- High-volume outage scenarios
The results demonstrated how automated ticket routing improves queue stability and supports SLA compliance.
The analysis demonstrated that:
- NLP significantly reduces manual ticket classification.
- Automated routing improves response times.
- Queue simulations help forecast staffing requirements.
- Intelligent prioritization ensures critical incidents receive immediate attention.
Deploy the NLP classification model within the organization's ticket management platform to automate Tier 1 ticket routing and accelerate incident response.
Operations managers should regularly use the queue simulation to forecast staffing requirements and ensure adequate support capacity before periods of increased ticket demand.
- Python
- Pandas
- NumPy
- Scikit-learn
- NLTK
- Plotly
- Matplotlib
- SimPy
- Customer Complaint Analysis
- Natural Language Processing Pipeline
- Machine Learning Classification Model
- Automated Ticket Routing
- Queue Simulation
- SLA Performance Analysis
- Workforce Planning Recommendations