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

Keyword Identification

ExecVision

As a data analyst, you know that there are valuable insights to be gained from analyzing sales call transcripts. However, manually combing through each transcript is a daunting task. In this post, we’ll show you how to use generative AI to automatically extract keywords from ExecVision sales call transcripts, providing you with the insights necessary to improve your sales team’s performance.

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 in a range of applications including search engine optimization (SEO), content analysis, and topic modeling.

Generative AI algorithms can be trained on a labeled dataset of text to recognize patterns and features in the text associated with important words or phrases. This technique automates the keyword extraction process, saving time and increasing accuracy.

Example Use Cases

Use cases for extracting keywords from ExecVision sales call transcripts include:

Identifying common customer pain points and objections
Measuring the effectiveness of sales techniques
Identifying trends in customer behavior
Improving sales team training and coaching
Identifying areas for product improvement

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

Accessing the Data and Identifying Preliminary Keywords

The first step in analyzing ExecVision sales call transcripts is accessing the data. You can export the transcripts in CSV format from ExecVision, or query a list of transcripts from your data warehouse or BI tool.

Once you have your data, it can be helpful to identify preliminary keywords. This can be done manually by reviewing a sample of transcripts or using generative AI tools to identify and measure the frequency of keywords. Generative AI can also suggest additional keywords you may not have been aware of, helping you gain deeper insights from your data.

With your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from your sales call transcripts. This will provide you with valuable insights into your customers' needs and behaviors, enabling you to improve your sales team’s performance and drive revenue growth.

Conclusion

Keyword extraction using generative AI is a powerful tool for analyzing large amounts of text data. In the case of ExecVision sales call transcripts, it can provide valuable insights into customer behavior and product improvement opportunities. By following the steps outlined in this post, you can start extracting keywords from your sales call transcripts and gain a competitive edge in your industry.

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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