Support ticket analysis starts with ticket classification. Ticket classification refers to the process of categorizing customer support requests (tickets) based on their content, purpose, or issue type.

An accurate, useful classification involves sorting tickets into categories based on their topic, for example "refund requested", "delivery issue", or "food arrived mouldy", or their sentiment, for example "positive", "netural" or "negative".

The primary goals of support ticket classification include:

1. Improving Response Times: By accurately classifying tickets, support teams can route them to the most appropriate department or support agent who is best equipped to handle the specific issue. This helps in reducing the time it takes to respond to and resolve customer issues.
2. Improving Customer Servicev Outcomes: Classification allows for more personalized and effective responses to customer inquiries, as it ensures that the right expertise is applied to each query.
3. Resource Allocation: By understanding the types of support requests being received, organizations can better allocate their resources, ensuring that areas with higher demand have adequate support staff.
4. Identifying CX Trends and Issues: Classification can help in identifying trends and recurring issues, which can inform product improvements, training needs, and customer service strategies.
5. Automation and Prioritization: Most support ticket systems can sort tickets by tags, which can also help in prioritizing urgent or high-priority issues.

The process of support ticket classification can be manual, with staff members reading and categorizing each ticket, or automated, using natural language processing (NLP) and AI algorithms that analyze the support request and categorize it.

At SentiSum, we put the power of automated support ticket analysis in your hands. Using the latest developments in artificial intelligence, and our easy-to-use platform, you'll have access to high quality tags & insights at your fingertips.

Human beings are excellent at two things: (1) Tasks that require creativity and (2) tasks that require empathy.

And, we're also not good at two things: repetitive, boring tasks and tasks that require speed of processing.

But lucky us, AI is the complete opposite.

AI can categorize 100,000 support tickets in minutes and make sure those categories apply exactly the same way to each ticket. Amazing, right? That would take us forever.

Here's three parts of support ticket analysis that AI excels at:

1. Handling High Volumes: AI excels at processing vast numbers of support tickets efficiently. This capability allows your system to manage an increasing volume of inquiries without overwhelming your team. The need for additional staffing is reduced, even as customer interactions grow.
2. Uniform Application: AI operates without fatigue or personal biases, ensuring a level of consistency in analysis that manual processing can't guarantee. Human error or variability in performance, often seen with manual reviews, is significantly minimized, ensuring every ticket is treated with uniform attention and accuracy.
3. Immediate Processing: Leveraging its rapid processing capabilities, AI ensures that support tickets are promptly assigned to the appropriate agents. This swift action facilitates near-instantaneous responses, regardless of the volume of incoming tickets or the time they are received, greatly enhancing customer satisfaction through timely support.

And an added bonus? Nobody on your team gets bored out of their mind tagging support tickets all day.

We wrote about how to manually analyze support tickets in detail in our guide on Support Driven Growth.

There are four key steps:

1/ Collate your feedback data

Collect it in our free template here.

2/ Decide on your taxonomy

Categorising feedback isn’t always easy. Choose topics that are granular enough to be insightful but high level enough that you can draw out similarities. When dealing with high volumes of unstructured free text (such as customer support ticket logs) you’ll want to create both high-level themes and more granular sub-categories.

We have a complete guide to creating help desk ticket categories here.

3/ Start tagging your tickets

Add your tickets and categories to the spreadsheet. We suggest one person tags all the tickets to ensure objectivity—two people may interpret the same piece of text differently, categorising it differently.

4/ Look for patterns and share insights

Apply filters to your spreadsheet to reveal which topics are mentioned in the highest volumes, and send the data off to the relevant team. The popularity of the theme and category can be a clear indicator of what needs prioritisation.

It's worth noting that we'd only recommend doing a manual support ticket analysis if you have a handful of tickets each month. Otherwise your insights will quickly become biased and non-useful.

We built our ticket analysis reporting to be simple and sharable. The more eyeballs the better for encouraging teams across your company to improve customer experience.

In this FAQ, I'll show you several examples of our analysis reports at SentiSum.

1. Our ticket analysis reporting dashboard for voice calls:

2. Our customer support ticket analysis dashboard:

3. Our daily summary email that highlights key trends in your support conversation topics

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Yes, we do. Alongside granular CX insights, our voice call analysis feature also analyzes agent performance and summarizes feedback for the agent on every single call. Our customers love using it to accelerate and improve quality assurance.