How to extract keywords from Intercom CSAT Survey Comments using generative AI
How to Extract Keywords from Intercom CSAT Survey Comments using Generative AI
Intercom CSAT surveys are a valuable source of customer feedback. However, analyzing the comments section of these surveys can be time-consuming and overwhelming for your support team. In this article, we will show you how to use generative AI to extract keywords from Intercom CSAT survey comments, allowing you to quickly identify important insights and themes.
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, which 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 Intercom CSAT survey comments include:
- Identifying common issues and trends
- Improving product development and customer satisfaction
- Personalizing customer interactions and improving retention
- Identifying areas for training and improvement for your support team
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 Intercom CSAT survey comments. You can export this data in CSV format from your Intercom account or query a list of comments from your data warehouse or BI tool.
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 the most important keywords from your Intercom CSAT survey comments. This will help you identify important insights and themes in your customer feedback, allowing you to improve your product development and customer satisfaction.
For more information on Intercom's API see here: https://developers.intercom.com/intercom-api-reference/reference
Extracting keywords from Intercom CSAT survey comments using generative AI can provide valuable insights into your customer feedback. By identifying common issues and trends, improving product development and customer satisfaction, personalizing customer interactions, and identifying areas for training and improvement for your support team, you can improve your overall customer experience and retention. Try implementing this technique in your organization today!