How to extract keywords from Five9 Sales Call Transcripts using generative AI

Keyword Identification

Five9

How to Extract Keywords from Five9 Sales Call Transcripts Using Generative AI

As a sales team, you have a lot of customer conversations every day. Each conversation is a goldmine of insights that can help you improve your sales process, identify common customer pain points, and personalize your outreach. However, manually going through each transcript to find these insights is impractical and time-consuming. Fortunately, you can use generative AI to automatically extract keywords from your Five9 sales call transcripts. In this post, we’ll show you how.

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. You can use it to extract key information and themes from text. Keyword extraction 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 Five9 sales call transcripts include:

Identifying common customer pain points and objections
Improving sales pitch effectiveness and personalization
Identifying areas for sales team training and improvement
Conducting market research on customer needs and wants
Tracking the effectiveness of marketing campaigns and messaging

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

Accessing Your Five9 Sales Call Transcript Data and Identifying Preliminary Keywords

First, you need to identify the sales call transcript data that you want to work with. Five9 provides an API that allows you to easily access your sales call transcripts. You can export this data in CSV or JSON format, or query a list of transcripts from your data warehouse or BI tool.

For more information on the Five9 API see here: https://developer.five9.com/docs/

Next, it can be helpful (but not necessary) to identify common keywords that you may want to extract from your sales call transcripts. Generative AI tools can be used to both identify and measure frequency of keywords but also to suggest additional keywords you may not have been aware to look for. For example - you might find that recurring customer inquiries around pricing may provide insights into pricing strategy non-obvious to the initial sales inquiry.

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from your Five9 sales call transcripts. This will help you quickly identify common themes and insights from your customer conversations, and improve the effectiveness of your sales process.

Happy keyword extracting!

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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