How to extract keywords from Qualtrics CSAT Survey Comments using generative AI
How to Extract Keywords from Qualtrics CSAT Survey Comments Using Generative AI
If you want to improve your customer experience, you need to know what your customers are saying about it. Qualtrics CSAT surveys are a valuable source of feedback, but analyzing the comments can be time-consuming and complicated. In this post, we’ll show you how to use generative AI to automatically extract keywords from Qualtrics CSAT survey comments.
What is Keyword Extraction?
Keyword extraction is a natural language processing (NLP) technique that involves identifying the most important or relevant words or phrases in a piece of text. You can use it to extract key information and themes from text has many applications, such as search engine optimization (SEO), content analysis, and topic modeling.
Keyword extraction can be performed manually, but it can also be automated using machine learning algorithms. These algorithms learn to recognize patterns and features in the text that are associated with important words or phrases, and can be trained on a labeled dataset of text.
You can use keyword extraction to analyze and summarize large amounts of text data to quickly identify the most important information and themes.
Example Use Cases
Use cases for extracting keywords from Qualtrics CSAT survey comments include:
- Identifying common issues and trends
- Improving response times
- Personalizing customer interactions
- Identifying areas for training and improvement
Teams that might find these use cases helpful include: customer support, customer success, product, marketing, and operations.
Finding your input data and identifying preliminary keywords
You first need to identify the data that you want to work with. Here, we are looking at Qualtrics CSAT survey comments. You can extract this data using the Qualtrics API, export it in CSV format, query a list of surveys from your data warehouse or BI tool, or copy and paste with an example comment.
For more information on the Qualtrics API see here: https://api.qualtrics.com/reference
Next, it can be helpful (but not necessary) to identify common keywords that you may want to extract from your survey comments. Generative AI tools can be used to both identify and measure frequency of keywords but also to suggest additional keywords you may not have been aware to look for. For example - you might find that recurring customer inquiries around billing may provide insights into product improvement opportunities non-obvious to the initial support inquiry.
Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from your Qualtrics CSAT survey comments. This will help you improve the quality and consistency of your customer support. This can help you both reduce churn and improve the efficiency of your support team.