How to classify AskNicely NPS Survey Comments with generative AI

Text Classification

AskNicely

How to Classify AskNicely NPS Survey Comments with Generative AI

As a business, it's crucial to know how your customers feel about your services or products. The Net Promoter Score (NPS) survey is one way to gather customer feedback. However, manually analyzing and categorizing the comments can be time-consuming and inefficient. In this post, we'll show you how to use generative AI to automatically classify AskNicely NPS survey comments for better analysis and decision-making.

What is Text Classification?

Text classification is a machine learning technique that involves assigning predefined categories or labels to a given piece of text. It's a subfield of natural language processing that uses statistical models to identify patterns and features in the text to classify new, unseen text data.

Text classification has various applications, from spam detection in emails to analyzing social media posts and reviews. It's particularly useful for businesses that rely on large amounts of text data to automate tasks and extract valuable insights.

Example Use Cases

Here are some examples of how you can use text classification to analyze AskNicely NPS survey comments:

Categorize comments by sentiment (Positive, Negative, Neutral)
Categorize comments by topic (Product quality, Customer service, Pricing, etc.)
Identify and remove spam or irrelevant comments
Identify recurring issues and prioritize them for resolution

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

Finding Your Input Data and Categories

The first step is to identify the data you want to analyze. In this case, we want to analyze AskNicely NPS survey comments. You can export the comments in CSV format from your AskNicely account or use the AskNicely API to extract data programmatically.

Next, you need to identify the categories you want to classify the comments into. These may include sentiment categories such as positive, negative, and neutral, or topic categories such as product quality, customer service, and pricing.

Once you have your data and categories, you can use generative AI to automatically classify your AskNicely NPS survey comments. This will help you to analyze customer feedback more efficiently and make data-driven decisions that improve your business.

Conclusion

Classifying AskNicely NPS survey comments with generative AI is a powerful way to analyze customer feedback and gain valuable insights. By automating the classification process, you can save time and resources while improving your decision-making capabilities. We hope this post has been helpful in explaining the basics of text classification and how it can be applied to AskNicely NPS survey comments.

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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