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

Keyword Identification

Kayako

Are you looking for a way to quickly identify important insights from your Kayako support call transcripts? With the help of generative AI, you can automatically extract keywords from your transcripts to uncover valuable insights that can improve the quality and efficiency of your customer support.

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. By using generative AI, you can automate this process and extract keywords from large amounts of text data quickly and efficiently.

Keyword extraction has many applications, such as search engine optimization (SEO), content analysis, and topic modeling. In the context of customer support, it can help you 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 extracting keywords from Kayako support call transcripts helpful include:

Customer support
Customer success
Product
Marketing
Operations

Some example use cases for extracting keywords from Kayako support call transcripts include:

Identifying recurring issues and trends
Improving response times by quickly identifying the nature of the issue
Personalizing customer interactions by understanding customer pain points
Identifying areas for training and improvement for support team members

Accessing Your Data and Identifying Preliminary Keywords

The first step in extracting keywords from your Kayako support call transcripts is identifying the data you want to work with. You can extract this data using the Kayako API, export it in CSV format, or query a list of transcripts from your data warehouse or BI tool.

It can also be helpful to identify preliminary keywords that you may want to extract from your support call transcripts. Generative AI tools can be used to both identify and measure the frequency of keywords, as well as suggest additional keywords you may not have been aware of. For example, you might find that recurring issues related to billing may provide insights into product improvement opportunities that were not initially apparent.

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from your Kayako support call transcripts. This will help you quickly identify important insights from your transcripts that can improve the quality and efficiency of your customer support.

For more information on the Kayako API, see here: https://developer.kayako.com/api/v1/docs/

By using generative AI to extract keywords from your Kayako support call transcripts, you can uncover valuable insights that can help you improve the quality and efficiency of your customer support. Try it out today!

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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