We want to use AI to find out what our own customers are complaining about in our support tickets over the last three years, but we do not have the time to read through 10,000 logs. How do we build a simple scraping and categorization routine to extract these operational pain points?

Question

Tyler Smith · Accepted Answer

Do not hire an expensive consulting firm or buy bloated enterprise software to analyze your support history. You can solve this problem quickly with a simple operational script and an LLM API.

First, have your technical team write a basic thirty line script using a tool like Playwright or a standard database exporter to pull the raw text of your support tickets from the last three years.

Next, pass this raw data through a large language model with a highly structured prompt. Instruct the model to ignore user specific details and focus entirely on categorizing the recurring operational pain points. Have it group the tickets into categories such as billing errors, delivery delays, product defects, or communication gaps.

Once the AI has categorized the data, have it rank the issues by frequency and financial impact. Take the top three recurring issues and bring them directly to your next quarterly planning session. Use the IDS process to solve these root operational problems permanently.

By using a simple, cost effective scraper to extract these public confessions of operational pain, you turn messy support logs into actionable data that directly shapes your company's Rocks and long term strategy.