How to analyze sentiment of SurveyMonkey NPS Survey Comments with generative AI
As a business, it’s important to understand how your customers feel about your product or service. One way to do this is by measuring your Net Promoter Score (NPS). This score is based on a survey in which customers are asked to rate how likely they are to recommend your product or service to others. However, to gain deeper insights into the feedback provided, you need to perform sentiment analysis on the comments provided in the survey. In this post, we’ll show you how to use generative AI to automatically perform sentiment analysis on SurveyMonkey NPS survey comments.
What is Sentiment Analysis?
Sentiment analysis is a natural language processing (NLP) technique that involves using machine learning algorithms to automatically identify and extract the emotions or opinions expressed in a given piece of text.
The algorithms are trained on a labeled dataset of text samples, where each sample is labeled with its corresponding sentiment (positive, negative, or neutral). The model learns to recognize patterns and features in the text that are associated with different emotions, and uses these patterns to predict the sentiment of new, unseen text.
Sentiment analysis has many applications, such as customer feedback analysis, social media monitoring, and market research. It's a powerful tool for organizations that want to understand how people feel about their products or services, or to track public opinion on different issues. It can help automate tasks and extract valuable insights from large amounts of text data.
Example Use Cases
Some use cases for performing sentiment analysis on SurveyMonkey NPS survey comments include:
- Identifying trends in customer feedback to improve product or service offerings
- Understanding how customers feel about specific features or aspects of your product or service
- Identifying areas for improvement in customer support and experience
- Tracking changes in customer sentiment over time
Teams that might find these use cases helpful include: product, customer success, marketing, and operations.
Accessing your Data and Confirming your Sentiment Scale
To perform sentiment analysis on SurveyMonkey NPS survey comments, you will need to extract the comments from your survey data. SurveyMonkey allows you to export your survey results in CSV format, which you can then import into a tool for analysis.
Next, you need to confirm the sentiment scale you will use for assessing customer sentiment. Typically, sentiment is measured on a scale of -1 (most negative) to 1 (most positive). You may also assign sentiment ratings, such as:
- Very Negative
- Negative
- Neutral
- Positive
- Very Positive
Once you have your data and sentiment scale, you can use generative AI to automatically assess the sentiment of your SurveyMonkey NPS survey comments. This will help you gain deeper insights into your customer feedback and improve the quality of your product or service offerings. This can help you both retain customers and attract new ones.
For more information on sentiment analysis and how it can benefit your business, contact our team today.