How to analyze sentiment of CallRail Sales Call Transcripts with generative AI

Sentiment Analysis

CallRail

How to Analyze Sentiment of CallRail Sales Call Transcripts with Generative AI

The success of any sales team depends on how well they can understand and meet the needs of their customers. One of the most effective ways to do this is by analyzing customer conversations. This is where sentiment analysis comes in handy. In this post, we will show you how to use generative AI to perform sentiment analysis on CallRail sales call transcripts.

What is Sentiment Analysis?

Sentiment analysis is a natural language processing (NLP) technique that involves using machine learning algorithms to identify and extract emotions or opinions expressed in a given piece of text. The algorithms are trained on a labeled dataset of text samples, where each sample is labeled with its corresponding sentiment (positive, negative, or neutral). The model learns to recognize patterns and features in the text that are associated with different emotions and uses these patterns to predict the sentiment of new, unseen text.

Sentiment analysis has many applications, such as customer feedback analysis, social media monitoring, and market research. It's a powerful tool for organizations that want to understand how people feel about their products or services or to track public opinion on different issues. It can help automate tasks and extract valuable insights from large amounts of text data.

Example Use Cases

Some use cases for performing sentiment analysis on CallRail sales call transcripts include:

Automatically detect and classify the sentiment of sales calls
Quickly identify areas for improvement in sales processes
Assess the effectiveness of sales pitches and techniques
Improve sales performance and customer satisfaction

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

Accessing your Data and Confirming your Sentiment Scale

You first need to identify the data that you want to work with. Here, we are looking at CallRail sales call transcripts. You can extract this data using the CallRail API, export it in CSV format, query a list of calls from your data warehouse or BI tool, or copy and paste with an example sales call.

For more information on the CallRail API see here: https://apidocs.callrail.com/

Next, you need to confirm the sentiment scale you will use for assessing customer sentiment. Typically - sentiment is measured on a scale of -1 (most negative) to 1 (most positive). You also may assign sentiment ratings.

Here is an example of a sentiment rating scale:

Very Positive
Positive
Neutral
Negative
Very Negative

Once you have your data and sentiment scale, you can use generative AI to automatically assess the sentiment of your CallRail sales call transcripts. This will help you improve the quality and consistency of your sales team. This can help you both increase revenue and improve the efficiency of your sales team.

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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