How to extract keywords from CustomerGauge NPS Survey Comments using generative AI

Keyword Identification

CustomerGauge

If you want to extract valuable insights from your customer feedback, keyword extraction is an essential technique that can help you identify the most important themes and topics. In this article, we’ll show you how to use generative AI to extract keywords from your CustomerGauge NPS survey comments, so you can improve your products, services, and customer experiences.

What is Keyword Extraction?

Keyword extraction is a natural language processing (NLP) technique that involves identifying the most important words and phrases in a piece of text. By analyzing the frequency, relevance, and context of these keywords, you can gain valuable insights into the themes and topics that matter most to your customers.

Generative AI algorithms can learn to recognize patterns and features in the text that are associated with important words and phrases, and can be trained on a labeled dataset of text. This means that you can automate the keyword extraction process and save time and effort.

Example Use Cases

Some use cases for extracting keywords from CustomerGauge NPS survey comments include:

Identifying the most common reasons for customer satisfaction or dissatisfaction
Discovering new trends and patterns in customer feedback
Comparing the feedback from different customer segments or regions
Identifying the most important product or service features for your customers
Improving your marketing messaging based on customer feedback

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

Accessing the Data and Identifying Preliminary Keywords

To extract keywords from your CustomerGauge NPS survey comments, you first need to access the data. You can do this by exporting your NPS survey results in CSV format, querying them from your data warehouse or BI tool, or copying and pasting them from the CustomerGauge platform.

Once you have your data, it can be helpful to identify some preliminary keywords that you want to extract. These might be specific product or service features, customer pain points, or common phrases that you see in the feedback. These preliminary keywords can help you narrow down the scope of your analysis and focus on the most relevant themes.

Generative AI tools can help you identify and measure the frequency of these preliminary keywords, and suggest additional keywords that you may not have thought of. This can give you a more comprehensive view of the feedback and help you discover new insights.

Extracting keywords from your CustomerGauge NPS survey comments can help you gain a deeper understanding of your customers and their needs. By using generative AI to automate the process, you can save time and effort, and focus on the most important insights.

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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