How to classify Zoho Desk Support Tickets & Chats with generative AI

Text Classification

Zoho Desk

If you're looking for a way to automate your support ticket routing, generative AI might be the solution you need. In this post, we'll explain what text classification is, why it's useful, and how you can use it with Zoho Desk to streamline ticket classification and improve customer satisfaction.

What is Text Classification?

Text classification is the process of using machine learning algorithms to automatically assign categories or labels to text data. This can be useful for tasks like spam detection, sentiment analysis, and support ticket routing. The algorithms learn from a set of labeled data, identifying patterns and features that can be used to classify new, unseen data.

Example Use Cases

Here are some examples of how you might use text classification with Zoho Desk:

Automatically route incoming support tickets to the appropriate team based on category and urgency
Identify and flag spam tickets for review
Identify common issues or complaints to help prioritize product or service improvements

These use cases could be helpful for teams like customer support, product development, and operations.

Finding Your Input Data and Categories

To use text classification with Zoho Desk, you'll need to have access to your support ticket data and a set of categories to classify them into. You can extract data from Zoho Desk using their API, export it to CSV, or query it from a data warehouse or BI tool.

Here are some example categories you might use for support ticket classification:

Technical issues
Billing and payment issues
Product information and features
Customer feedback and suggestions
Shipping and delivery issues
Account management
General inquiries
Return and exchange requests
Training and education
Sales and marketing

Using Generative AI with Zoho Desk

Once you have your data and categories, you can use generative AI to automatically classify your support tickets.

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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