How to extract keywords from Chorus.ai Sales Call Transcripts using generative AI

Keyword Identification

Chorus.ai

How to Extract Keywords from Chorus.ai Sales Call Transcripts using Generative AI

As a sales team, you have access to valuable insights from your sales calls. However, manually combing through the transcripts of each call can be time-consuming and inefficient. With generative AI, you can automatically extract important keywords from your Chorus.ai sales call transcripts, saving time and resources. In this post, we'll show you how to use keyword extraction to analyze your sales calls and gain valuable insights.

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 to extract key information and themes from text, such as search engine optimization (SEO), content analysis, and topic modeling.

Automated keyword extraction uses machine learning algorithms that learn to recognize patterns and features in the text that are associated with important words or phrases. These algorithms can be trained on a labeled dataset of text, making the process more efficient and accurate.

Example Use Cases

Some examples of how you can use keyword extraction on Chorus.ai sales call transcripts include:

Identifying key topics and themes discussed during sales calls
Measuring the frequency of certain keywords or phrases
Improving sales training and coaching
Identifying areas of improvement in the sales process
Personalizing customer interactions and improving customer experience

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

Accessing and Analyzing the Data

To access your Chorus.ai sales call transcripts, you can use the Chorus.ai API, export the transcripts in CSV format, or query them from your data warehouse or BI tool. Once you have the transcripts, you can use generative AI to identify and extract keywords automatically.

First, identify the keywords that you want to extract. You can use generative AI tools to help identify common keywords and phrases that may be relevant to your sales calls. These tools can also suggest additional keywords that you may not have thought of, providing even more valuable insights.

Next, use generative AI to automatically extract the identified keywords from your sales call transcripts. This will help you quickly analyze and summarize the important information and themes discussed during your sales calls.

By using keyword extraction to analyze your Chorus.ai sales call transcripts, you can gain valuable insights that can help improve your sales process and ultimately drive more revenue for your business.

Conclusion

Keyword extraction using generative AI can provide valuable insights from your Chorus.ai sales call transcripts. By identifying key topics and themes discussed during your sales calls, you can improve your sales training and coaching, personalize customer interactions, and identify areas of improvement in your sales process. With the right tools and techniques, keyword extraction can be a powerful way to gain insights from your sales calls and drive more revenue for your business.

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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