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

Keyword Identification

SatisMeter

If you're collecting NPS survey comments from your customers, you're likely sitting on a goldmine of valuable insights into what your customers care about and how they perceive your product or service. However, manually sifting through these comments to identify key themes and trends can be a daunting task. In this post, we'll show you how to use generative AI to automatically extract keywords from SatisMeter NPS survey comments, so you can quickly identify the most important insights and take action based on them.

What is Keyword Extraction?

Keyword extraction is a natural language processing (NLP) technique that involves identifying the most important or relevant words or phrases in a piece of text. In the case of NPS survey comments, this can help you quickly identify common themes and sentiments expressed by your customers. By extracting these keywords, you can gain a deeper understanding of what your customers care about, what they're happy with, and where there's room for improvement.

Keyword extraction can be automated using generative AI algorithms, which learn to recognize patterns and features in the text that are associated with important words or phrases. These algorithms can be trained on a labeled dataset of text to improve their accuracy and relevance.

Example Use Cases

Use cases for extracting keywords from SatisMeter NPS survey comments include:

Identifying common issues and trends
Improving product or service quality based on customer feedback
Identifying areas for product or service improvement
Measuring sentiment and customer satisfaction levels
Identifying potential churn risks and taking proactive steps to retain customers

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

Accessing your SatisMeter NPS Survey Data and Identifying Preliminary Keywords

The first step in using generative AI to extract keywords from your SatisMeter NPS survey comments is to access your survey data. You can do this by logging into your SatisMeter account and exporting your survey data in CSV format. Once you have your data, you can use generative AI tools to identify preliminary keywords and measure their frequency in the comments.

Once you have your preliminary keywords, you can use them to refine your analysis and gain deeper insights into what your customers are saying about your product or service.

In Conclusion

By using generative AI to extract keywords from your SatisMeter NPS survey comments, you can quickly identify common themes and sentiments expressed by your customers. This can help you take proactive steps to retain customers, improve your product or service, and ultimately drive growth and success for your business.

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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