Customer support tickets are more than a system to track technical, administrative, or service issues. The information contained within these tickets can also be used for several other purposes, including the surfacing of additional process, product, or service insights, as well as capturing “softer” insights, such as customer sentiment.

The most basic insights can come from simply analyzing the tags contained within each ticket. By categorizing tickets based on specific criteria, various trends can be surfaced and analyzed, helping to home in on issues that need to be addressed. While the process of tagging tickets once required support staff to manually fill in categories as custom fields for each new ticket, most modern support or help-desk software can handle this task automatically. That includes offerings from Kapture, MonkeyLearn, and Labelf, Freshdesk, Gorgias, Wowdesk, and Tido, among others.

These applications utilize natural language processing (NLP), and AI models to analyze and annotate data in existing tags, so that future tags are automatically classified properly based on their contents. Additionally, helpdesk and customer experience platforms are incorporating generative AI to quickly analyze ticket data, and then provide a recommended next-best-action for support staff, based on the content and previous successful resolutions to similar issues.

Automating the ticketing process can inform the setting of priorities around providing technical or service fixes, the development and rollout of new features and tools, and insights around process issues. But additional data contained within a ticket can be just as valuable, and can often be used in conjunction with other customer feedback to unlock additional insights.

• Assess customer sentiment: If a ticket includes a field for additional or open-ended comments (it should, by the way), the data within should be analyzed to detect customer sentiment. Several customer experience and customer feedback tools now incorporate sentiment analysis, which analyzes the words used, punctuation, structure, and even emojis, to better understand a customer’s sentiment when filling out the ticket. Better yet, a deeper analysis of a customer’s overall sentiment when dealing with an organization can be conducted by analyzing the customer’s sentiment during interactions over time, to assess whether there are any trends or patterns emerging. This information can be extremely useful for customers who may be ready to churn or drop off as customers.
• Surface nascent trends: Analyzing support tickets can also be used to surface nascent trends that may not be apparent through other channels. For example, if customers report that they are trying to take a specific action, but the product does not support it, this may indicate that a competitor’s product has that capability. It also could indicate that demand for a certain feature or capability is rising, and may be worth integrating into the next product revision. These trends, of course, should be correlated with feedback captured through other means, such as post-purchase surveys, social listening, and monitoring of user communities.
• Identify service issues at a granular level: When creating support tickets, allowing customers to list any previous interactions with agents may be useful in identifying how well agents are doing, particularly with respect to the dissemination of information, instructions, or guidance. By identifying a specific agent that interacted with a customer, a manager can dive into specific interaction records to identify situations where there may be a gap in training, a lack of resources available to an agent, or an opportunity to provide a better next-best-action prompt to ensure a better outcome.
• Optimizing support resources: In addition to right-sizing the support team based on ticket volume, assessing the types of tickets can ensure that the right mix of human support workers, versus automated support bots, are in place. On a longer-term basis, the types of issues raised by these tickets can be used to train bots to provide resolutions, both in terms of providing immediate responses to customers, but also by interacting with back-end systems to complete tasks.