How to classify CustomerGauge NPS Survey Comments with generative AI

Text Classification

CustomerGauge

How to Classify CustomerGauge NPS Survey Comments with Generative AI

As a business, understanding your customers' satisfaction levels is critical to your success. However, analyzing NPS survey comments can be a daunting and time-consuming task. In this post, we will explore how to use generative AI to classify CustomerGauge NPS survey comments, making it easier to understand customer feedback and take the necessary actions to improve your business.

What is Text Classification?

Text classification is a technique used in natural language processing (NLP) that involves using machine learning algorithms to automatically assign one or more 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 many applications, from spam detection in emails to sentiment analysis in social media posts and reviews. It is an essential tool for businesses 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 CustomerGauge NPS survey comments include:

Identify the main themes and concerns of your customers
Automatically classify feedback into positive, negative, or neutral sentiments
Identify areas of improvement for your business
Track changes in customer satisfaction over time
Provide actionable insights for different teams within the organization

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

Finding your input data and categories

The first step is to identify the data that you want to work with. In this case, we are looking at NPS survey comments from CustomerGauge. You can export this data in CSV format or query a list of comments from your data warehouse or BI tool.

Next, you need to find or create your list of categories for classifying the comments. This might include sentiments, themes, or areas of improvement.

Common examples of NPS survey comment categories include:

Product features and functionality
Customer service and support
Price and value for money
Website usability and design
Delivery and shipping
Overall satisfaction
Recommendation likelihood

Once you have your data and categories, you can use generative AI to automatically classify your CustomerGauge NPS survey comments. This will help you to better understand your customers' feedback and take the necessary actions to improve your business.

Conclusion

Classifying NPS survey comments with generative AI can save your business time and provide valuable insights into your customers' feedback. With the right data and categories, you can automate the process of analyzing NPS survey comments and gain a better understanding of your customers' satisfaction levels. Using this information, your business can make informed decisions and take action to improve 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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