How to extract keywords from HubSpot CSAT Survey Comments using generative AI

Keyword Identification

HubSpot

As a business, it's essential to gather feedback from your customers to improve your products and services. However, analyzing each response manually can be time-consuming and inefficient. In this article, we'll show you how to use generative AI to extract keywords from HubSpot CSAT survey comments, making it easier to identify important insights.

What is Keyword Extraction?

Keyword extraction is a natural language processing (NLP) technique that involves identifying the most relevant words or phrases in a piece of text. It helps to extract key information and themes from large amounts of text data, making it easier to identify important insights. Keyword extraction can be performed manually, but it can also be automated using machine learning algorithms.

Example Use Cases

The following are some examples of how to use keyword extraction for HubSpot CSAT survey comments:

Categorizing and prioritizing feedback
Identifying common issues and trends
Improving response times
Personalizing customer interactions
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

Before you can extract keywords from your HubSpot CSAT survey comments, you need to access the data. You can do this by exporting the survey responses into a CSV file. Once you have your data, you can use generative AI tools to identify and measure the frequency of keywords.

It can also be helpful to identify preliminary keywords that you may want to extract from your survey comments. For example, if you're collecting feedback on a new feature, you might want to extract keywords related to the feature's usability or functionality. This can help you identify areas for improvement and make data-driven decisions.

Using Generative AI to Extract Keywords

Once you have your data and preliminary keywords identified, you can use generative AI to extract keywords automatically. There are several generative AI tools available that use machine learning algorithms to analyze text data and identify keywords.

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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