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

Keyword Identification

LiveAgent

Introduction

As a company, you may have a lot of data from your LiveAgent support call transcripts that you would like to analyze. However, manually going through all of this information would be time-consuming and inefficient. In this post, we will show you how to use generative AI to automatically extract keywords from your LiveAgent support call transcripts.

What is Keyword Extraction?

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

Keyword extraction can be done manually or automated using machine learning algorithms. These algorithms learn to recognize patterns and features in the text that are associated with important words or phrases.

Example Use Cases

Some valuable use cases for extracting keywords from LiveAgent support call transcripts using NLP analysis and SaaS tools 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.

Instructions on Accessing Data and Identifying Preliminary Keywords

To extract keywords from LiveAgent support call transcripts, you will need access to the data. You can export the data in CSV format or query a list of transcripts from your data warehouse or BI tool.

Once you have access to the data, you can use generative AI to identify and measure the frequency of keywords. Generative AI tools can also suggest additional keywords that may not have been initially identified. For example, recurring customer inquiries around billing may provide insights into product improvement opportunities that were not initially identified.

After identifying preliminary keywords, you can use generative AI to automatically assess the sentiment of your LiveAgent support call transcripts. This will help improve the quality and consistency of your customer support, reduce churn, and improve 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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