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

Keyword Identification

Retently

Retently NPS survey comments are a rich source of feedback from your customers. However, manually analyzing each comment can be time-consuming and impractical. In this post, we’ll show you how to use generative AI to extract keywords from Retently NPS survey comments and gain insights into your customers’ feedback.

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. It can be used to extract key information and themes from text and has many applications, such as search engine optimization (SEO), content analysis, and topic modeling.

Generative AI algorithms can be trained to recognize patterns and features in the text that are associated with important keywords or phrases. This makes it possible to automate the keyword extraction process and quickly identify the most important information and themes in large amounts of data.

Example Use Cases

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

Identifying trends in customer feedback
Improving product features and customer experience
Identifying areas for training and improvement in customer support teams
Personalizing customer interactions
Creating more effective marketing campaigns

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

Accessing and Analyzing Retently NPS Survey Comments

You can extract your Retently NPS survey comments using the Retently API or by exporting them in CSV format. Once you have your data, you can use generative AI tools to automatically extract and analyze keywords from the comments.

Before analyzing your data, it can be helpful to identify preliminary keywords that you may want to extract from your Retently NPS survey comments. This can include keywords related to product features, customer service, and overall customer satisfaction. Generative AI tools can also help to identify additional keywords that you may not have been aware of.

Once you have identified your preliminary keywords, you can use generative AI to extract and analyze the keywords from your Retently NPS survey comments. This will help you gain insights into your customers’ feedback and improve your product features and customer experience.

Step 1: Use Retently API or Export Survey Comments in CSV format

You can extract your Retently NPS survey comments using the Retently API or by exporting them in CSV format. For more information on the Retently API see here: https://developers.retently.com/docs/api-v1/

Step 2: Identify Preliminary Keywords

Identify preliminary keywords that you may want to extract from your Retently NPS survey comments. This can include keywords related to product features, customer service, and overall customer satisfaction. Generative AI tools can also help to identify additional keywords that you may not have been aware of.

Step 3: Use Generative AI to Extract and Analyze Keywords

Use generative AI to extract and analyze the keywords from your Retently NPS survey comments. This will help you gain insights into your customers’ feedback and improve your product features and customer experience.

Overall, keyword extraction using generative AI can help you quickly identify important insights from your Retently NPS survey comments. By automating the keyword extraction process, you can gain valuable insights into your customers’ feedback and improve your product, customer experience, and overall business operations.

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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