How to classify Groove Support Tickets & Chats with generative AI

Text Classification

Groove

How to Classify Groove Support Tickets & Chats with Generative AI

As a user of Groove, you understand the importance of quickly addressing support tickets and chats. However, manual classification can be a time-consuming and error-prone task for agents. In this post, we will show you how to use generative AI to automatically classify your Groove support tickets and chats, reducing response times and improving customer experience.

What is Text Classification?

Text classification is a natural language processing (NLP) technique that involves using machine learning algorithms to automatically assign predefined categories or labels to text data. The algorithms learn from labeled data and use statistical models to identify patterns and features in the text that can be used to classify new, unseen text data.

Text classification is used in a variety of applications, including spam detection, sentiment analysis, and support ticket classification. It has become an essential tool for many industries that rely on large amounts of text data, helping to automate tasks and extract valuable insights.

Example Use Cases

Here are some examples of how text classification can be used to classify Groove support tickets and chats:

Automatically classify tickets by urgency level, such as low, medium, or high
Automatically classify tickets by category and subcategory, such as technical support, billing, or sales
Identify and classify spam tickets or chats
Automatically prioritize urgent tickets or chats
Reduce average resolution time for support tickets and chats

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

Finding Your Input Data and Categories

The first step is to identify the data you want to classify. For Groove support tickets and chats, you can extract this data using the Groove API, export it in CSV format, or query a list of tickets and chats from your data warehouse or BI tool.

Next, you need to find or create your list of categories for classifying the tickets and chats. This might include ticket categories, ticket subcategories, or urgency levels.

Common examples of support ticket categories include:

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

Once you have your data and categories, you can use generative AI to automatically classify your Groove support tickets and chats. This will help you to reduce the time it takes to process support tickets and chats and ensure that they are routed to the correct point of contact.

Conclusion

Text classification is a powerful tool for automating tasks and extracting insights from text data. By using generative AI to automatically classify your Groove support tickets and chats, you can improve response times and customer experience. We hope this post has been helpful in explaining how to use text classification to classify Groove support tickets and chats.

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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