How to classify SurveyMonkey CSAT Survey Comments with generative AI

Text Classification

SurveyMonkey

As a business, understanding customer satisfaction is crucial for growth and success. One way to measure customer satisfaction is through SurveyMonkey CSAT surveys. However, manually analyzing and categorizing the comments can be time-consuming and prone to error. In this post, we’ll show you how to use generative AI to automatically classify SurveyMonkey CSAT survey comments, making it easier to identify trends and take action on customer feedback.

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. In the case of SurveyMonkey CSAT survey comments, text classification can be used to identify the sentiment of the comment (positive, negative, neutral) and the topic or theme of the comment (product, service, support, etc.).

Text classification is used in a wide range of applications, from sentiment analysis in social media posts to email categorization in customer support. It can help businesses automate tasks, extract insights, and improve customer experiences.

Example Use Cases

Use cases for classifying SurveyMonkey CSAT survey comments include:

Identifying trends in customer satisfaction levels
Identifying areas for improvement in products and services
Identifying areas where customers are particularly satisfied
Identifying customer pain points
Improving customer experiences and loyalty

Teams that might find these use cases helpful include: customer support, product, marketing, and operations.

Finding your input data and categories

You first need to identify the data that you want to work with. Here, we are looking at SurveyMonkey CSAT survey comments. You can extract this data using the SurveyMonkey API, export it in CSV format, query a list of comments from your data warehouse or BI tool, or copy and paste an example comment.

For more information on the SurveyMonkey API see here: https://developer.surveymonkey.com/api/v3/

Next, you need to find or create your list of categories for classifying the comments. This might include sentiment (positive, negative, neutral) and topic (product, service, support, etc.).

Once you have your data and categories, you can use generative AI to automatically classify your SurveyMonkey CSAT survey comments. This will help you to identify trends, pain points, and opportunities for improvement in your products and services based on customer feedback.

Conclusion

Classifying SurveyMonkey CSAT survey comments with generative AI can save time and improve the accuracy of your analysis. By automating the process, you can quickly identify trends and take action on customer feedback to improve your products, services, and customer experiences.

Using AirOps to perform Keyword Identification

With AirOps, you can easily extract relevant keywords and phrases from your text-based data using the Keyword Identifier data app. Here's how:

Select "Keyword Identifier" from the Data Apps page. The input required for Keyword Identifier is the "text_field" which is the input text data.
Decide where you want the analysis to be performed and stored. The Keyword Identifier data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_KEYWORD_IDENTIFIER.

Here is an example SQL query:
```
SELECT
AIROPS_KEYWORD_IDENTIFIER(text_field) as result
FROM
your_table
```
Execute the keyword extraction analysis by running the SQL query. The output will contain an array of keywords and phrases extracted from the input text data.

Example Input:

"Hello, I am having trouble with my account. I cannot seem to log in and I have tried resetting my password multiple times."

Example Output:
```
"keywords": ["trouble", "account", "log in", "resetting", "password", "multiple times"],"summary": "A customer is having trouble logging into their account and has tried resetting their password multiple times."
```

Using AirOps to perform Sentiment Analysis

With AirOps, you can easily perform sentiment analysis on any text data such as reviews, support tickets, or sales calls using Sentiment Analyzer. Here’s how:

Select "Sentiment Analyzer" from the Data Apps page. The only input for Sentiment Analyzer is some text to analyze.
Decide where you want the analysis to be performed and stored. The Sentiment Analyzer data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_SENTIMENT_ANALYZER.

Here is an example SQL query:
```
SELECT
AIROPS_SENTIMENT_ANALYZER(text_field) as result
FROM
your_table
```
Execute the sentiment analysis by running the SQL query. The output will contain a sentiment score and sentiment summary, as well as a list of positive and negative keywords extracted from the input text data.
Input:
"I'm sorry to say that I had a terrible experience with your product. The customer service was unresponsive and the product didn't work as advertised."
Output:
```
"positive_keywords": [],"negative_keywords": ["terrible experience", "customer service", "unresponsive", "product", "didn't work", "advertised"],"score": -0.8,"sentiment": "Very Negative"
```

Using AirOps to perform Text Classification

With AirOps, you can easily perform classification using generative AI. Here’s how:

Select "Text Classifier'' from the Data Apps page. Below are the possible inputs for Text Classifier.text_field: The input text data.categories (optional): Categories can be specified as a comma-separated list. Leave empty for automatic determination.multi_category: Set to “true” if the text can belong to multiple categories, or “false” if it can only belong to one category.
Decide where you want the analysis to be performed and stored. The Text Classifier data app can be easily used in the AirOps Data App page and via API, but in this example, the analysis will be performed in Snowflake through an external function called AIROPS_CLASSIFIER.

Here is an example SQL query:
```
SELECT
AIROPS_CLASSIFIER(text_field, categories, multi_category) as result
FROM
your_table
```
Execute the classification analysis by running the SQL query. The output will contain a list of keywords extracted from the input text data that are relevant to the identified categories and a list of categories that the input text data belongs to based on the provided categories or automatic determination.

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