How to classify Salesforce CSAT Survey Comments with generative AI
As a company, it’s important to understand how your customers feel about your products or services. One way to do this is by using customer satisfaction (CSAT) surveys. However, manually categorizing and analyzing the comments can be a tedious and time-consuming task. In this post, we’ll show you how to use generative AI to automatically classify Salesforce CSAT survey comments.
What is Text Classification?
Text classification is a natural language processing (NLP) technique that involves using machine learning algorithms to automatically assign one or more predefined categories or labels to a given piece of text. The algorithms typically learn from a training set of labeled text data and use statistical models to identify patterns and features in the text that can be used to classify new, unseen text data.
Text classification is used in a wide range of applications, from spam detection in emails to sentiment analysis in social media posts and reviews. It has become an essential tool for many industries that rely on large amounts of text data, helping to automate tasks and extract valuable insights from the data.
Example Use Cases
Use cases for classifying Salesforce CSAT survey comments include:
- Automatically classify comments by sentiment (positive, negative, neutral)
- Automatically classify comments by topic (product, customer service, shipping, etc.)
- Identify and classify common issues or complaints
- Track changes in customer sentiment over time
- Identify areas for improvement in products or services
Teams that might find these use cases helpful include: customer support, customer success, product, operations, and marketing.
Finding your Input Data and Categories
You first need to identify the data that you want to work with. Here, we are looking at Salesforce CSAT survey comments. You can extract this data using the Salesforce API, export it in CSV format, query a list of comments from your data warehouse or BI tool, or copy and paste with an example comment.
Next, you need to find or create your list of categories for classifying the comments. This might include sentiment categories (positive, negative, neutral), topic categories (product, customer service, shipping, etc.), or issue categories (common issues or complaints).
For example, common sentiment categories might include:
- Positive
- Negative
- Neutral
Once you have your data and categories, you can use generative AI to automatically classify your Salesforce CSAT survey comments. This will help you to quickly analyze customer feedback, track changes in customer sentiment, and identify areas for improvement in your products or services.
For more information on using generative AI for text classification, check out our previous post: How to Use Generative AI for Text Classification
Thank you for reading and happy analyzing!