How to classify Zendesk Support Call Transcripts with generative AI

Text Classification

Zendesk

Introduction

Providing excellent customer service is crucial to businesses, and one part of that is processing support call transcripts. However, manually classifying each transcript can be time-consuming and prone to errors. This can lead to negative customer experiences, such as tickets being routed to the wrong point of contact or being closed prematurely. In this article, we'll show you how to use generative AI to automatically classify Zendesk support call transcripts, improving efficiency and customer satisfaction.

What is Text Classification?

Text classification is an NLP technique that uses machine learning algorithms to assign predefined categories or labels to text data. These algorithms learn from a training set of labeled text data and use statistical models to identify patterns and features in the text to classify new, unseen text data.

Some examples of text classification include spam detection in emails, sentiment analysis in social media posts, and support ticket classification.

Example Use Cases

Here are some examples of how you can use text classification to improve your Zendesk support call transcript processing:

Automatically classify transcripts by category and subcategory
Identify and classify spam or irrelevant transcripts
Automatically prioritize urgent transcripts
Reduce average resolution time
Improve customer satisfaction

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

Accessing and Identifying Data for Classification

To get started with text classification, you need to identify the data you want to work with, in this case, Zendesk support call transcripts. You can extract this data using the Zendesk API or export it in CSV format.

Next, you need to identify the categories you want to classify the transcripts into. These categories could be pre-existing labels in Zendesk, such as ticket categories or priority levels. Alternatively, you can define your own categories based on your business needs, such as product issues, billing inquiries, or feedback.

Once you have your data and categories, you can use generative AI tools, such as Google Cloud AutoML or Amazon Comprehend, to automatically classify your transcripts. These tools use natural language processing and machine learning to identify patterns and features in the text to assign categories to each transcript.

Conclusion

Text classification is a powerful tool for automating the processing of Zendesk support call transcripts. By using generative AI, you can save time and improve the accuracy of classification, ultimately leading to better customer experiences. With the right tools and categories in place, you can streamline your support workflow and focus on providing excellent customer service.

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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