# Using LLMs to Analyze Customer Feedback in Business Travel

DevFeed: [Using LLMs to Analyze Customer Feedback in Business Travel](<https://devfeed.tech/articles/how-we-re-using-llms-to-transport-customer-experience-in-business-travel-22593.md>)

Original publisher: [Read original article](<https://medium.com/amex-gbt-technology/how-were-using-llms-to-transport-customer-experience-in-business-travel-7dcf9608836e?source=rss----60a0578f4096---4>)

Author: Mahad Mohamed

Published: 2026-06-01T08:01:01Z

Content type: article

Language: en

Sources: [Amex GBT Technology](<https://devfeed.tech/sources/amex-gbt-technology.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [data-science](<https://devfeed.tech/tags/data-science.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [llms](<https://devfeed.tech/tags/llms.md>)

## AI overview

The article describes how LLMs are used to analyze large volumes of unstructured feedback from business travelers and travel managers. The process includes anonymizing, categorizing, and extracting insights that inform the product roadmap, supply strategy, and service model.

## Source excerpt

Business travel is changing fast. Travelers expect seamless, consumer-like experiences with additional service. Travel managers expect deeper insights and more marketplace content, while organizations expect strategic value. At the heart of this shift? Great customer experience. And this starts with truly understanding them, their pain points, preferences, frustrations, and aspirations. For years, it wasn't a lack of feedback that was the problem. Surprisingly, it was the volume and variability of it. Thousands of traveler comments, travel manager surveys, support cases, and contact center calls created a rich source of data but in unstructured, inconsistent formats that were nearly impossible to analyze efficiently. Today, thanks to advancements in large language models (LLMs), that's changed completely. In this article, I'll share how we're using LLMs to analyze feedback from travel managers and travelers anonymizing, categorizing and extracting key insights and how it now directly shapes our product roadmap, supply strategy, and service model. The problem: huge amounts of feedback but limited insights Every year, we receive tens of thousands of data points from: - Support cases - Traveler surveys - Travel manager feedback - Client reviews - Meetings and QBRs While each individual input is valuable, the collective intelligence within this data was historically untapped. Traditional analytics tools could count keywords or measure sentiments but they couldn't understand meaning, nuance or context. For example: - "Can you make changes easier during disruptions?" - "I couldn't rebook quickly when my flight was cancelled." - "Need clearer options when travel plans shift." These comments mean the same thing, but conventional categorization treats them as different issues. LLMs do the opposite. They connect the dots. Step 1: Using LLMs to analyze and interpret customer feedback Instead of manually reviewing comments or using basic text analytics, we feed all unstructured