Correctly classifying tickets is crucial for Managed Service Providers (MSPs). Accurate ticket classification ensures that the right issues are addressed by the right technicians, reducing delays and preventing unnecessary escalations. It optimizes workflows, helps in prioritizing urgent matters, and ultimately enhances customer satisfaction by providing faster resolutions. For MSPs that depend on seamless service, consistent ticket handling, and operational efficiency, getting ticket classification right is a fundamental need.
When it comes to optimizing IT support processes, MSPs face an important choice: leverage machine learning tailored to their own historical ticket data, or attempt to use a general large language model (LLM) to classify tickets. It’s tempting for MSPs to use widely available LLMs to try and classify tickets, but these models won’t be accurate enough and it will be hard to find the errors. At first glance, a general LLM might seem like a powerful, all-encompassing solution. However, the nuances of IT service management require more precision—precision that comes from training models on the MSP’s own data. Here’s why machine learning tailored specifically to your historical ticket data outperforms general LLMs for ticket classification.
Domain-Specific Knowledge
General LLMs are trained on vast amounts of publicly available data, covering a wide range of topics with a generalized understanding. However, this “jack-of-all-trades” approach means that while these models might handle basic tasks well, they’re not deeply familiar with industry-specific language, technical shorthand, or common issues unique to IT support.
An MSP’s historical ticket data, on the other hand, is packed with knowledge that is directly relevant to their day-to-day operations. Training a model specifically on this data allows it to recognize specific patterns, terminology, and recurring issues that a generic LLM would likely overlook. This specialized understanding makes for faster, more accurate classifications that translate directly to a smoother workflow.
The Power of Historical Data
Machine learning thrives on familiarity. When you train a model on your MSP’s own historical ticket data, you’re feeding it examples that reflect the real-world issues your business actually faces. This data includes the exact language used by clients, the types of recurring problems they experience, and the subtle context that distinguishes different types of support requests.
For example, the model learns that the term “VPN issue” might often be related to access during remote work hours or that “printer not working” usually needs a different escalation path based on the specific department impacted. These nuances come directly from your own historical data and cannot be adequately understood by a general LLM that lacks direct exposure to your environment.
Accurate Ticket Prioritization and Escalation
Correctly prioritizing and escalating tickets is critical in the MSP environment, where different types of issues have varied levels of urgency. Models trained on an MSP’s data understand how past tickets were categorized, prioritized, and resolved, including trends specific to different clients and systems. These insights enable more accurate predictions of when an issue requires urgent attention, should be assigned to a specific technician, or is likely to escalate.
A generic LLM, in contrast, lacks these nuanced understandings of the MSP’s historical support patterns and might misclassify high-priority tickets as routine or overlook complex relationships between issues. The consequences of such errors could range from extended downtimes to diminished customer trust.
The Pitfalls of General LLMs
General LLMs like ChatGPT or CoPilot are trained on broad datasets—ranging from Wikipedia articles to online forums—that may have little resemblance to the everyday language used in IT support. While these models are excellent at generating coherent text and understanding general queries, they lack the specificity needed to handle the nuances that make ticket classification effective.
For instance, a general LLM might interpret the phrase “network down” in a hundred different ways, lacking the context that tells it whether this typically means an outage affecting multiple users or just a single connection issue, depending on the client. That lack of familiarity often results in incorrect classifications, leading to delays, escalations, or improper ticket routing—all of which add friction to your support processes.
Consistent Language and Context Recognition
Every MSP has its own terminology, shorthand, and even recurring client-specific terminology that may not make sense in a more generalized setting. Models built on an MSP’s ticket history learn these unique language patterns and contexts, leading to a more consistent and reliable interpretation of tickets.
For example, a particular client may always refer to a piece of software by an internal nickname. A model trained on historical tickets from that MSP will recognize the nickname and properly classify the ticket, while a generic LLM would likely get lost. This context-driven approach saves valuable time and reduces frustration by minimizing the need for manual ticket reclassification or clarification.
Reducing Escalations, Improving Automation
One of the key benefits of using a machine learning model trained on your historical ticket data is its ability to minimize escalations. By correctly classifying tickets the first time, these models prevent unnecessary escalations and make sure the right technician is handling the right problem. This reduces bottlenecks and allows more issues to be resolved at the frontline rather than requiring senior engineers.
Moreover, automation initiatives—such as auto-classifying ticket categories, predicting issue types, or even suggesting initial resolutions—become more effective when they are based on the actual data of the MSP. A model that knows your business can provide meaningful automation, as opposed to generic suggestions that lack real applicability to your clients and systems.
Delivering a Competitive Edge
Using a general LLM might be sufficient for a company with no specific needs or industry context, but MSPs operate in a landscape where understanding the details is crucial. Machine learning based on your own data gives you a competitive edge by improving accuracy, reducing misclassification, and making your support process more efficient overall. It helps your MSP not only deliver faster responses but also more consistent, quality outcomes that drive customer satisfaction.
In an industry built on trust and rapid problem resolution, there’s no substitute for a model that knows your business inside and out. Machine learning tailored to your MSP’s historical ticket data delivers real, measurable results, outperforming general LLMs in both accuracy and utility.
Enhanced Security and Privacy
MSPs handle sensitive data, often specific to their clients’ environments, user information, and unique system configurations. Using a general LLM without significant customization might require sending ticket data to third-party providers, increasing privacy risks and exposing client information. A custom model trained on an MSP’s historical data can be securely hosted on-premises or in a secure cloud environment, ensuring data privacy and compliance.
Final Thoughts
Machine learning models trained on your historical ticket data are built to understand the intricacies of your clients, your issues, and your business processes. General LLMs, while powerful, simply can’t replicate the level of tailored insight that comes from leveraging your own data. If your goal is to improve ticket classification accuracy, reduce escalations, and streamline your IT support, there’s no better approach than utilizing machine learning crafted specifically for your MSP.
By training machine learning models on their own historical ticket data, MSPs can take full advantage of domain-specific insights, achieve consistent and accurate classifications, and improve efficiency, privacy, and customer satisfaction. It’s an investment that pays off in more accurate support workflows, helping MSPs scale effectively without compromising on service quality.
Read about CrushBank’s machine learning based SmartClassifier here.