How to extract keywords from Help Scout CSAT Survey Comments using generative AI

Keyword Identification

Help Scout

Are you struggling to sift through hundreds or thousands of customer satisfaction survey comments manually? Do you want to extract valuable insights from these comments quickly and efficiently? In this post, we’ll show you how to use generative AI to automatically extract keywords from Help Scout CSAT survey comments. This can help you identify common issues, prioritize improvements, and personalize interactions with your customers.

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. Keyword extraction can be performed manually, but it can also be automated using machine learning algorithms.

Generative AI algorithms can learn to recognize patterns and features in the text that are associated with important words or phrases. They can be trained on a labeled dataset of text to automatically extract the most important keywords. This can be used to analyze and summarize large amounts of text data to quickly identify the most important information and themes.

Example Use Cases

Use cases for extracting keywords from Help Scout CSAT survey comments include:

Identifying common issues and trends
Personalizing customer interactions
Improving response times
Identifying areas for training and improvement

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

Accessing and Analyzing Data

To access the data that you want to work with, you can export Help Scout CSAT survey comments in CSV format. You can then use a generative AI tool to automatically extract the most important keywords from these comments.

You can also identify common keywords that you may want to extract from your survey comments. Generative AI tools can be used to both identify and measure the frequency of keywords but also to suggest additional keywords you may not have been aware to look for. For example, recurring customer inquiries around a certain product feature may provide insights into product improvement opportunities non-obvious to the initial survey response.

Once you have your data and preliminary keywords identified, you can use generative AI to automatically assess the sentiment of your Help Scout CSAT survey comments. This will help you improve the quality and consistency of your customer support. This can help you both reduce churn and improve the efficiency of your support team.

By using generative AI to extract keywords from your Help Scout CSAT survey comments, you can quickly and efficiently identify important insights and trends. This can help you improve your customer support and increase customer satisfaction.

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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