How to extract keywords from Zoho Desk CSAT Survey Comments using generative AI

Keyword Identification

Zoho Desk

As a business owner or manager, you know how important it is to get feedback from your customers. One of the most effective ways to do this is through customer satisfaction (CSAT) surveys. However, analyzing the results of these surveys can be time-consuming and overwhelming. Fortunately, with the help of generative AI, you can easily extract keywords from Zoho Desk CSAT survey comments to gain valuable insights into your customers' feedback.

What is Keyword Extraction?

Keyword extraction is a natural language processing (NLP) technique that involves identifying the most important words or phrases in a piece of text. It is used to extract key information and themes from text, and has many applications, such as search engine optimization (SEO), content analysis, and topic modeling.

Keyword extraction can be performed manually, but it can also be automated using machine learning algorithms. These algorithms learn to recognize patterns and features in the text that are associated with important words or phrases, and can be trained on a labeled dataset of text.

You can use keyword extraction 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 Zoho Desk CSAT survey comments include:

Identifying common issues and trends in customer feedback
Improving customer satisfaction by addressing recurring issues
Identifying areas for training and improvement in customer support
Measuring the effectiveness of customer support initiatives

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

Accessing Your Zoho Desk CSAT Survey Comments

To access your Zoho Desk CSAT survey comments, you can export them in CSV format from the Zoho Desk platform. Once you have your data, you can use generative AI tools to automatically extract keywords from the comments.

Before you begin your analysis, it can be helpful to identify common keywords that you may want to extract from your survey comments. This can help you focus your analysis and identify important themes more quickly.

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from your survey comments. This will help you gain valuable insights into your customers' feedback and improve the effectiveness of your customer support initiatives.

Conclusion

By using generative AI to extract keywords from Zoho Desk CSAT survey comments, you can gain valuable insights into your customers' feedback and improve the effectiveness of your customer support initiatives. Whether you're looking to identify common issues and trends, measure the effectiveness of customer support initiatives, or improve customer satisfaction by addressing recurring issues, keyword extraction can help you quickly identify the most important information and themes in your survey comments.

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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