How to extract keywords from HappyFox Support Call Transcripts using generative AI

Keyword Identification

HappyFox

Introduction

If you're looking to gain valuable insights from your support team's conversations with customers, you'll need to analyze a large volume of support tickets. However, manually combing through each ticket is inefficient and impractical. Luckily, with the help of generative AI, you can automatically extract keywords from your HappyFox support call transcripts to identify important insights. In this article, we'll explain how this process works and provide instructions on how to get started.

What is Keyword Extraction?

Keyword extraction is an NLP technique that involves identifying the most important or relevant words or phrases in a piece of text. This can be done manually or with the help of 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.

Keyword extraction can be used for a variety of applications, such as SEO, content analysis, and topic modeling. In the case of HappyFox support call transcripts, keyword extraction can help categorize and prioritize support tickets, identify common issues and trends, improve response times, personalize customer interactions, and identify areas for training and improvement.

Example Use Cases

Teams that might find these use cases helpful include customer support, customer success, product, marketing, and operations. Here are some specific examples of how keyword extraction can be used:

Categorizing and prioritizing support tickets: By identifying the most important keywords, you can quickly categorize and prioritize support tickets based on their urgency and severity.
Identifying common issues and trends: By analyzing the most frequent keywords, you can identify common issues and trends that can help you improve your products and services.
Improving response times: By categorizing and prioritizing support tickets, you can improve your response times and ensure that urgent tickets are addressed first.
Personalizing customer interactions: By analyzing the keywords used by customers, you can personalize your interactions and provide tailored solutions.
Identifying areas for training and improvement: By analyzing the most common issues, you can identify areas where your support team may need additional training or where your products or services may need improvement.

Instructions

To extract keywords from HappyFox support call transcripts, you'll need to follow these steps:

Identify the data you want to work with: You can extract this data using the HappyFox API, export it in CSV format, query a list of tickets from your data warehouse or BI tool, or copy and paste with an example ticket.
Identify preliminary keywords: While not necessary, it can be helpful to identify common keywords that you may want to extract from your support tickets. Generative AI tools can help you identify and measure the frequency of keywords and suggest additional keywords you may not have been aware of.
Use generative AI to automatically extract keywords: Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from your HappyFox support call transcripts. This will help you quickly identify important insights and improve the efficiency of your support team.

By following these steps, you can gain valuable insights from your HappyFox support call transcripts without having to manually analyze each ticket. This will help you improve the quality and consistency of your customer support and ultimately reduce churn.

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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