How to extract keywords from Promoter.io NPS Survey Comments using generative AI

Keyword Identification

Promoter.io

If you're looking to improve your company's Net Promoter Score (NPS), you'll want to analyze the comments your customers leave in response to your survey. However, reading and analyzing these comments can be time-consuming and challenging. Fortunately, there is an easy way to extract valuable insights from these comments using generative AI. In this post, we'll show you how to extract keywords from Promoter.io NPS survey comments so you can identify important themes and trends.

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. You can use it to extract key information and themes from text data, such as comments in NPS surveys. Keyword extraction can be performed manually, but it can also be automated using machine learning algorithms like generative AI.

Generative AI works by learning patterns and features in text data and using that knowledge to identify important keywords and phrases. This can save you time and effort when analyzing large amounts of text data.

Example Use Cases

Use cases for extracting keywords from Promoter.io NPS survey comments include:

Identifying common issues and trends
Improving product or service offerings
Enhancing customer experience
Tracking changes and improvements over time
Identifying areas for training and improvement

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 Promoter.io NPS survey comments, you'll first need to access the data. This can be done by exporting the data from Promoter.io in CSV format.

Next, it can be helpful (but not necessary) to identify common keywords that you may want to extract from your survey comments. This can be done manually or with the help of generative AI tools, which can suggest additional keywords you may not have been aware of. For example, you might find that recurring customer complaints about shipping times may provide insights into areas for improvement.

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from the NPS survey comments. This will help you quickly identify important themes and trends, which can inform your business decisions and improve your NPS.

Conclusion

Keyword extraction using generative AI is a valuable tool for analyzing Promoter.io NPS survey comments. By extracting meaningful insights from these comments, you can improve your product or service offerings, enhance customer experience, and track changes over time. With these tips, you can easily extract keywords from your NPS survey comments and use them to improve 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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