How to classify Zoho Desk CSAT Survey Comments with generative AI

Text Classification

Zoho Desk

As a company that values customer satisfaction, it’s essential to understand the feedback you receive from your customers. Zoho Desk’s Customer Satisfaction (CSAT) survey allows you to collect feedback from your customers, but analyzing the comments can be a daunting task. In this post, we’ll show you how to use generative AI to automatically classify Zoho Desk CSAT survey comments.

What is Text Classification?

Text classification is a subset of natural language processing (NLP) that involves categorizing text into predefined categories. It’s a machine learning technique that involves training a model on labeled data and then using that model to classify new, unseen data. In the context of Zoho Desk CSAT survey comments, text classification can help you identify the topics that are most commonly mentioned by your customers.

Example Use Cases

Use cases for classifying Zoho Desk CSAT survey comments include:

Identifying the most common issues your customers face
Detecting patterns in customer feedback to improve product or service
Identifying and addressing recurring issues
Segmenting feedback by customer type or location

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

Finding your input data and categories

You first need to identify the data that you want to work with. Here, we are looking at Zoho Desk CSAT survey comments. You can extract this data using the Zoho Desk API, export it in CSV format, or query a list of comments from your data warehouse or BI tool.

Next, you need to find or create your list of categories for classifying the comments. This might include product or service issues, feature requests, or customer service feedback.

Common examples of CSAT survey comment categories include:

Product or service issues
Feature requests
Customer service feedback
General feedback or comments

Once you have your data and categories, you can use generative AI to automatically classify your Zoho Desk CSAT survey comments. This will help you to quickly identify the topics that are most commonly mentioned by your customers and take action to improve your product or service.

Conclusion

Classifying Zoho Desk CSAT survey comments with generative AI can help you understand your customers’ feedback and improve your product or service. By using this technique, you can quickly identify the most common issues and address them proactively. This will lead to higher customer satisfaction and loyalty, which ultimately benefits 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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