How to extract keywords from HappyFox Support Tickets & Chats using generative AI

Keyword Identification

HappyFox

As a support team member, you know that every conversation with a customer is an opportunity to learn something new. However, going through each support ticket manually is a daunting task. Thankfully, there is a simple and cost-effective way to identify valuable insights from your HappyFox support tickets and chats using AI. In this post, we will show you how to use generative AI to automatically extract keywords from HappyFox support tickets and chats.

What is Keyword Extraction?

Keyword extraction is a natural language processing (NLP) technique that involves identifying the most important or relevant words or phrases in a piece of text. It can be used to extract key information and themes from text for various applications such as search engine optimization (SEO), content analysis, and topic modeling.

Keyword extraction can be done manually, but it is more efficient to use machine learning algorithms. These algorithms learn to recognize patterns and features in the text that are associated with important words or phrases and can be trained on a labeled dataset of text.

You can use keyword extraction to analyze and summarize large amounts of text data to quickly identify the most important information and themes related to your business.

Example Use Cases

Some of the use cases for extracting keywords from HappyFox support tickets and chats include:

Categorizing and prioritizing support tickets
Identifying common issues and trends
Improving response times
Personalizing customer interactions
Identifying areas for training and improvement

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

Finding your Input Data and Identifying Preliminary Keywords

The first step is to identify the data that you want to work with. Here, we are looking at HappyFox support tickets and chats. You can extract this data using the HappyFox API, export it in CSV format, query a list of tickets from your data warehouse, or copy and paste with an example ticket.

For more information on the HappyFox API, see here: https://support.happyfox.com/docs/api/

It can be helpful (but not necessary) to identify common keywords that you may want to extract from your support tickets. Generative AI tools can be used to both identify and measure the frequency of keywords and suggest additional keywords you may not have been aware of. For example, you might find that recurring customer inquiries around billing may provide insights into product improvement opportunities not obvious from the initial support inquiry.

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from your HappyFox support tickets and chats. This will help you improve the quality and consistency of your customer support and reduce churn while improving the efficiency of your support team.

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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