How to classify Qualtrics CSAT Survey Comments with generative AI

Text Classification

Qualtrics

As a company, it’s important to understand how your customers feel and what they think about your product or service. One way to measure customer satisfaction is through CSAT surveys. However, manually analyzing and categorizing each comment can be time-consuming and error-prone. In this post, we’ll show you how to use generative AI to automatically classify Qualtrics CSAT survey comments.

What is Text Classification?

Text classification is a natural language processing (NLP) technique that uses machine learning algorithms to automatically assign predefined categories or labels to a given piece of text. The algorithms 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 variety of applications, including sentiment analysis, topic modeling, and spam detection. By classifying text data, it becomes easier to extract valuable insights and automate tasks.

Example Use Cases

Use cases for classifying Qualtrics CSAT survey comments include:

Automatically classify comments by sentiment (positive, negative, neutral)
Automatically classify comments by topic (customer service, product quality, pricing, etc.)
Identify and classify spam comments
Automatically prioritize comments based on sentiment or topic
Extract actionable insights to improve customer satisfaction

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. In this case, we are looking at Qualtrics CSAT survey comments. You can extract this data using the Qualtrics API, or export it in CSV format from your Qualtrics account.

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

Once you have your data and categories, you can use generative AI to automatically classify your Qualtrics CSAT survey comments. This will help you to extract insights from your customer feedback and take action to improve customer satisfaction.

For more information on how to use generative AI to classify text data, check out our blog post on "How to Use Generative AI for Text Classification."

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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