How to analyze sentiment of Zendesk Support Call Transcripts with generative AI

Sentiment Analysis

Zendesk

As a customer support team, it's important to understand the sentiment of your customers during support calls. It can be difficult to manually track every conversation and evaluate the tone of each call. This is where sentiment analysis can be helpful. In this post, we'll show you how to use generative AI to analyze the sentiment of Zendesk support call transcripts, allowing you to better understand your customers' needs.

What is Sentiment Analysis?

Sentiment analysis is a natural language processing (NLP) technique that uses machine learning algorithms to automatically identify the emotions and opinions expressed in a given piece of text. The algorithms are trained on a dataset of labeled text samples, where each sample is labeled with its corresponding sentiment (positive, negative, or neutral). The model learns to recognize patterns and features in the text that are associated with different emotions and uses these patterns to predict the sentiment of new, unseen text.

Sentiment analysis has many applications, such as customer feedback analysis, social media monitoring, and market research. It's a powerful tool for organizations that want to understand how people feel about their products or services or to track public opinion on different issues. It can help automate tasks and extract valuable insights from large amounts of text data.

Example Use Cases

Some use cases for performing sentiment analysis on Zendesk support call transcripts include:

Identifying common issues that customers are experiencing
Tracking customer satisfaction levels over time
Identifying specific agents or teams that may need additional training or resources
Uncovering trends in customer sentiment that may inform product or service improvements

Teams that may find these use cases valuable include customer support, customer success, product development, and marketing.

Accessing and Analyzing Your Zendesk Support Call Transcripts

The first step in analyzing your Zendesk support call transcripts is to extract the data. This can be done through the Zendesk API or by exporting a CSV file of your call transcripts. Once you have your data, you can use a generative AI tool, such as Google Cloud's Natural Language API, to automatically analyze the sentiment of each transcript.

Google Cloud's Natural Language API offers a sentiment analysis feature that evaluates the emotional tone of a piece of text and assigns a sentiment score to it. The sentiment score ranges from -1 (negative) to 1 (positive), with 0 being neutral.

For example, if a call transcript includes positive language such as "I'm really grateful for your help," the sentiment analysis tool may assign a score of 0.8, indicating a positive sentiment. If the transcript includes negative language such as "I'm very frustrated with this issue," the sentiment analysis tool may assign a score of -0.6, indicating a negative sentiment.

Using sentiment analysis to evaluate your Zendesk support call transcripts can help you better understand your customers' needs and improve the quality of your customer support. By identifying trends and patterns in customer sentiment, you can make informed decisions about how to improve your products, services, and customer support processes.

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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