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The Business Problem
In today’s dynamic business landscape, organizations are increasingly recognizing the pivotal role customer feedback plays in shaping the trajectory of their products and services. The ability to swiftly and effectively respond to customer input not only fosters enhanced customer experiences but also serves as a catalyst for growth, prolonged customer engagement, and the nurturing of lifetime value relationships. As a dedicated Product Manager or Product Analyst, staying attuned to the voice of your customers is not just a best practice; it’s a strategic imperative.
While your organization may be inundated with a wealth of customer-generated feedback and support tickets, your role entails much more than just processing these inputs. To make your efforts in managingg customer experience and expectations truly impactful, you need a structured approach – a method that allows you to discern the most pressing issues, set priorities, and allocate resources judiciously. One of the most effective strategies at your disposal is to harness the power of Support Ticket Categorization utilizing Natural Language Processing (NLP).
In this notebook, I’ll develop a support ticket categorization system that accurately classifies incoming tickets, assigns relevant tags based on their content, implements mechanisms and generate the first response based on the sentiment for prioritizing tickets for prompt resolution.
TL;DR - Analysis and Conclusion
This project implemented a NLP-based support ticket categorization system to improve operational efficiency in customer support. Using prompt engineering with a large language model, we developed a solution that accurately categorizes tickets, assigns relevant tags, prioritizes tickets, and generates initial responses based on content analysis.
The system successfully processed support tickets across four key tasks:
- Categorization: Assigned tickets to specific categories (Data Recovery, Hardware Issues, Technical Issues)
- Tagging: Generated relevant tags for each ticket to improve searchability and tracking
- Prioritization: Evaluated ticket urgency and assigned appropriate priority levels (High, Medium, Low)
- Response Generation: Created contextually appropriate initial responses to tickets
My engineered implementation demonstrates the potential of NLP to transform customer support operations by automating routine tasks, improving consistency, and enabling support teams to focus on complex customer issues.
Next Steps
- Implement agentic AI Agents to offload some of the lower-level tasks.
- Try different models to measure model response quality.
- Implement a ML pipeline to monitor model response.
- Evaluate safety and ethicial responses.
- Add guardrails to the response.
- Monitor performance of model.
- A/B test model with other model(s) in staging and production environments.
- Request product owner and/or project manager determine key metrics that can be used to measure productivity gains and cost savings.