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

Keyword Identification

RingCentral

As a sales team, you know the importance of analyzing your sales call transcripts to identify trends, customer pain points, and opportunities for improvement. However, manually combing through every sales call transcript is time-consuming and inefficient. The good news is that you can use generative AI to automatically extract keywords from your RingCentral sales call transcripts, saving you time and providing valuable insights. In this post, we’ll show you how to do just that.

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, and has many applications, such as search engine optimization (SEO), content analysis, and topic modeling.

Keyword extraction can be performed manually, but it can also be automated using machine learning algorithms. These algorithms learn to recognize patterns and features in the text that are associated with important words or phrases, and can be trained on a labeled dataset of text.

You can use keyword extraction to analyze and summarize large amounts of text data to quickly identify the most important information and themes.

Example Use Cases

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

Identifying common pain points and objections raised by customers
Recognizing trends in customer needs and preferences
Improving sales pitch and messaging
Identifying opportunities for product development or improvement

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

Accessing the Data and Identifying Preliminary Keywords

To extract keywords from your RingCentral sales call transcripts, you first need to access the data. You can export the call transcripts in text format from RingCentral, or use a third-party tool to extract the data from your RingCentral account.

Once you have the data, it can be helpful to identify preliminary keywords that you want to extract from the transcripts. This can be done manually by reading through the transcripts and noting down the most common words and phrases. However, generative AI tools can also be used to automatically identify and measure the frequency of keywords, as well as suggest additional keywords that you may not have considered.

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract the keywords from your RingCentral sales call transcripts. There are many tools available for this, including MonkeyLearn, Google Cloud Natural Language API, and Microsoft Azure Text Analytics, and AirOps.

By using generative AI to extract keywords from your RingCentral sales call transcripts, you can quickly and easily identify important insights and trends, improving your sales pitch and messaging, and identifying product development opportunities. Give it a try!

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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