How to classify HappyFox Support Call Transcripts with generative AI

Text Classification

HappyFox

How to Classify HappyFox Support Call Transcripts with Generative AI

As a customer service representative, it is essential to provide quick and efficient solutions to customer queries. However, manually categorizing each support ticket can be a time-consuming and error-prone task. This is where generative AI comes in handy. In this post, we will show you how to classify HappyFox support call transcripts with generative AI.

What is Text Classification?

Text classification is a machine learning technique that involves assigning predefined categories or labels to a given piece of text. It is widely used in detecting spam emails, sentiment analysis, and customer support ticket classification.

Text classification uses statistical models to identify patterns and features in the text, which can be used to classify new, unseen data.

Example Use Cases

The following are some possible use cases for classifying HappyFox support call transcripts:

Automatically categorize support tickets by topic
Prioritize urgent support tickets
Identify spam tickets
Reduce response time

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

Accessing the Data and Identifying Categories

To classify HappyFox support call transcripts, you need to extract data from HappyFox's API or export it in CSV format. Once you have the data, you need to identify the categories you want to classify the transcripts into. Examples of categories include:

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

Once you have the data and categories, you can use generative AI to automatically classify the transcripts. This will save you time and ensure that support calls are routed to the correct point of contact.

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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