How to extract keywords from CallRail Sales Call Transcripts using generative AI

Keyword Identification

CallRail

As a data analyst, you know that valuable insights can be gleaned from sales call transcripts. However, manually analyzing every conversation would be time-consuming and inefficient. Fortunately, there is a cost-effective way to identify important insights using natural language processing (NLP) and generative AI. In this article, we will show you how to extract keywords from CallRail sales call transcripts to improve your sales and customer service.

What is Keyword Extraction?

Keyword extraction is an NLP technique that involves identifying the most important words or phrases in a piece of text. It is commonly used in SEO, content analysis, and topic modeling. Keyword extraction can be done manually or using machine learning algorithms. Machine learning algorithms can learn to recognize patterns and features in the text that are associated with important keywords or phrases.

Example Use Cases

The combination of NLP analysis, data type, and SaaS tool used in this article can be useful for:

Identifying common topics and themes in sales calls
Improving sales and customer service processes
Identifying areas for training and improvement

Teams that may find these use cases helpful include: sales, customer service, marketing, and product.

Accessing and identifying preliminary keywords in CallRail sales call transcripts

To access the data, you need to first export the CallRail sales call transcripts in a CSV format. Then, you can use a generative AI tool to identify preliminary keywords that are frequently mentioned in the transcripts. These keywords can be used to create categories or tags for the conversations, making it easier to search through them later.

One way to identify preliminary keywords is to use a keyword extraction tool like RAKE (Rapid Automatic Keyword Extraction). RAKE identifies keywords by analyzing the frequency of words and their co-occurrence in the text. It also considers word proximity and keyword relevance to the text's content.

Once you have identified the preliminary keywords, you can use a generative AI tool to automatically extract the most relevant keywords and phrases from the CallRail sales call transcripts. This will help you quickly identify common topics and themes in the conversations, and improve your sales and customer service processes.

Conclusion

Extracting keywords from CallRail sales call transcripts using generative AI can provide valuable insights into your sales and customer service processes. By identifying common topics and themes, you can improve your sales and customer service processes, identify areas for improvement, and train your team more effectively. With the right tools and techniques, you can make the most of your sales call data and improve your business outcomes.

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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