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

Keyword Identification

Gong

As a sales team, you have a wealth of information from your customer interactions that can help you improve your sales process and close more deals. However, manually reviewing every sales call transcript would be time-consuming and impractical. Using generative AI to extract keywords from Gong sales call transcripts can help you quickly identify important insights and improve your sales process.

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 data, such as sales call transcripts.

Generative AI algorithms can be trained to recognize patterns and features in the text that are associated with important words or phrases, and can be used to automate keyword extraction from large amounts of text data.

Example Use Cases

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

Identifying common pain points and objections
Improving sales pitches and messaging
Tracking and analyzing sales performance
Identifying areas for sales team training and development
Improving customer retention and satisfaction

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

Accessing and Analyzing Gong Sales Call Transcripts

It's important to note that keyword extraction is just one part of analyzing your sales call transcripts. It's also important to analyze the sentiment, tone, and context of the conversations in order to fully understand the insights and take actionable steps to improve your sales process.

Step 1: Identify Preliminary Keywords

Before you start extracting keywords from your sales call transcripts, it can be helpful to identify common keywords that you may want to extract. This will help you create a more accurate and effective model.

You can start by manually reviewing your sales call transcripts and identifying keywords and phrases that are frequently mentioned. You can also use generative AI tools to automatically identify keywords and measure their frequency in your transcripts.

Once you have your preliminary keywords identified, you can create a model to automatically extract keywords from your sales call transcripts.

Once you have your keywords extracted, you can analyze the insights and take actionable steps to improve your sales process. For example, if you notice that a specific pain point is frequently mentioned in your sales calls, you can adjust your messaging to address this pain point and improve your close rates.

By leveraging generative AI to extract keywords from your Gong sales call transcripts, you can quickly identify important insights and improve your sales process.

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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