Every support ticket tells a story about where your product or documentation falls short. When users reach out for help, they reveal critical gaps in your customer experience—whether it's confusing product flows, unclear documentation, bugs, or missing features. By analyzing these interactions, you can spot patterns that point to deeper issues: Are users consistently getting stuck at the same step? Are they asking questions that should be self-evident? Do they need help with tasks that should be automated? Each ticket is a window into how real users experience your product and a chance to make it better.
When customers contact support, each interaction is a signal that reveals something important about their experience. These signals typically fall into three types:
Documented Knowledge:
Undocumented Knowledge:
System Access, Feature Request, or Bug:
When deploying AI in customer support, the journey often starts with the easiest wins: answering questions that are already documented. For these cases, AI can quickly surface answers and resolve tickets with minimal effort—this is the low-hanging fruit and can be done quite easily.
When documentation falls short, LLMs enable powerful analysis of customer interactions across all support channels. By processing call transcripts, chat logs, and release notes at scale, LLMs can identify patterns and solutions that were previously impractical to discover. They can extract successful resolution steps from agent interactions, detect documentation gaps, and automatically suggest documentation updates. This creates a virtuous cycle: AI answers documented questions, mines support interactions for new insights, updates documentation, and incorporates additional context as needed. Over time, this loop makes your documentation and AI support smarter while providing valuable customer insights to drive sales and product improvements.
To maximize the impact of this analysis, consider implementing these product recommendations:
Documentation Gap Analysis
Product Enhancement Insights
Interaction Quality Monitoring
Real-time Support Analytics
In one case, we have seen AI-generated context by analyzing three months of call transcriptions triple the number of cases an AI agent was able to handle.
The more context you provide to AI, and the more deeply you integrate it with your product's UX, the faster users can get work done. For example, if the AI knows what error a user is seeing, what page they're on, or what actions they've just taken, it can anticipate customers' needs and help them move faster.
The role of support teams is evolving beyond just handling escalations. They are becoming the voice of the customer within the organization, driving sales and product improvements.
