What is Support Ticket Analysis?

1. What is ticket classification?

Companies receive such a large number of support tickets every day that they need to consider how they manage, route, and analyze individual tickets to streamline the process.

Classification occurs when the tickets are categorized so that the right team members can work on resolving the issues. Ticket categories can be determined through three main methods:

• Manual classification, as you might imagine, can be complicated and slow paced. It takes up hours in the workday that could be used for more productive tasks.
• Rule-based classification uses basic decision trees and if-then statements to properly sort tickets, but these systems are rarely accurate given the immense diversity and range of intentions in the natural language of a customer ticket. Having to update the rule set and maintain the system is expensive to keep up.
• AI-powered classification, which uses machine learning techniques like text analysis to sort the tickets, solves many of these drawbacks. Artificial intelligence teaches itself how to categorize tickets without much human input.

2. How do you do ticket analysis with AI?

Ticket classification with AI relies on natural language processing (NLP) techniques to understand the raw text of a set of tickets. In the industry, this is called text analytics, and it’s what we specialize in at Keatext. By leveraging text analytics, it’s much smoother to sort tickets properly and get the right person to respond to them as quickly as possible. That’s because you automatically access a few key things in each support ticket:

Along with ticket resolution, agent training, and performance monitoring, there’s another piece of the puzzle that companies need to address: analyzing tickets to find core business insights directly mentioned by customers.

• Topics (shipping, returns, technical support)
• Sentiment (positive, neutral, negative, mixed)
• Language (English, French, Spanish)
• Urgency (low, moderate, or urgent priority)
• Channel (social media, email, live chat)

For example, AI can detect when a customer needs technical support, signaling you to forward the ticket to the IT department. Likewise, it can pick up on negative sentiment to detect how urgent the complaint is, helping you prioritize which cases to attend to first. Machine learning is an invaluable foundation for customer support ticket analysis thanks to its unparalleled capabilities:

• Scalability: Machines are much faster at processing hundreds or even thousands of support tickets, resulting in a significantly more scalable system than having to do everything manually. You don’t have to bog down your team with tedious tasks even when the number of tickets goes up.
• Consistency: Algorithms never tire and are impervious to bias. While manual sorting can introduce mistakes or inconsistent analyses, machine learning ensures a consistent performance at all times.
• Real-time operation: Building on the scalability and speed aspects, machine learning is fast enough to forward support tickets to the right agents almost immediately no matter the time of day, ensuring that responses are prompt to boost customer satisfaction.

Thanks to third-party vendors of help desk data analysis services, implementing these cutting-edge technologies into your workflow is actually quite easy. Most providers will walk you through the process:

1. Upload sample data of various support tickets.
2. Define the categories you intend to use.
3. Train the AI by specifying the categories for each sample ticket.
4. Test the program by giving it brand new tickets and checking its performance.
5. Once ready, implement the system through API and other integration tools.

From an operational standpoint, ticket analysis with AI helps customer support teams better respond to customers quickly to prevent churn. But there’s more to the story: good ticket analysis goes beyond its functional role as an enablement tool and provides powerful insights about agent performance and customer experience.

3. Why does ticket analysis matter?

Ticket analysis with AI not only allows businesses to streamline ticket resolution but gain valuable insight into the performance of their help desk and prioritize solutions for customer issues.

Think about the text data in a support ticket conversation. There are always two people involved – the customer support agent and the customer. There are vastly different insights to be gathered from this data, and a good ticket analysis solution can parse the conversation into its participants to give businesses the exact insights they’re looking to find.

General ticket management platforms work by:

• Consolidating tickets from multiple channels together in one place for easy management
• Notifying relevant employees of tickets with high urgency or ones that have waited too long for a response
• Checking whether tickets have been fully resolved
• Facilitating collaboration among multiple employees tasked to handle the same case
• Keeping all the data in one place to perform ticket trend analysis

Discovering patterns in the tickets you receive can help you resolve future cases using what you’ve already learned. For instance, if you know the most common types of technical support problems, you can bring solutions to new customer tickets involving IT more quickly.

4. What are the use cases for ticket analysis?

Agent performance

Service desk ticket analysis doesn’t end once customer complaints have been resolved. The information involved can still be used to help support agents with their jobs in the future and improve the products and services offered by the company. When it comes to agent performance, businesses can leverage ticket analysis to:

Support ticket analysis combines qualitative and quantitative data in a really useful way. You can bring together qualitative data from the text analysis like topics and sentiment, and quantitative data like customer satisfaction rates and the resolution of the ticket.

• Sort and analyze individual agents’ responses
• Monitor overall sentiment in agents’ responses to customers
• Analyze sentiment from customers to determine their level of satisfaction when the ticket is closed
• Assess which topics agents are good at responding to and lead to a high customer satisfaction rate

Support ticket analysis combines qualitative and quantitative data in a really useful way. In the examples above, we can bring together qualitative data from the text analysis like topics and sentiment, and quantitative data like customer satisfaction rates and the resolution of the ticket.

Voice of Customer

Support ticket conversations obviously contain a wealth of information coming from customers. And with support tickets, it’s expected that this is all about issues that customers are facing. Analyzing this data at scale with AI allows you to see topics and trends that individual support agents may have noticed on their own but your company may not have realized the full scope of.

For customer experience, businesses can leverage ticket analysis to:

• Identify key issues mentioned by customers across the board
• Sort tickets by product to assess sentiment for individual products
• Prioritize urgent issues mentioned by customers
• See customer suggestions to improve products

Support ticket analysis has immense potential to improve customer experience. All it takes is full-scale analysis with AI to make sure that your business is not missing what customers are telling you – as directly as possible – in their attempts to resolve issues through your help desk.

Keatext’s ticket analysis

At Keatext, we differentiate our support ticket analysis solution through our help desk integrations, dashboard builder, and recommendations. When it comes to support ticket analysis, integrating directly with your help desk tool is a key functionality that makes the difference between a solution that’s good, and one that’s excellent. That’s why Keatext integrates with Zendesk, Salesforce, ServiceNow, and 400+ other platforms.

Increasing help desk efficiency also comes down to time to resolution. Keatext provides automatic recommendations for the top 5 most impactful actions your team can take to improve customer experience and ultimately see the ROI from analyzing tickets at scale.

From there, you can build and share dashboards that summarize these key findings and action items to stakeholders within your organization. You can dive as deep as you like, into the most pressing negative issues or even agent performance.