How to classify ExecVision Sales Call Transcripts with generative AI

Text Classification

ExecVision

As a data analyst, you know that analyzing sales call transcripts can be a daunting task. Manual classification is time-consuming, and the results may not always be accurate. Thankfully, generative AI can help you automate the process of classifying sales call transcripts, making it easier for you to extract valuable insights from the data. In this post, we’ll show you how.

What is Text Classification?

Text classification is a technique that involves using machine learning algorithms to assign predefined categories or labels to a given piece of text. These algorithms learn from a training set of labeled text data and use statistical models to identify patterns and features in the text that can be used to classify new, unseen text data.

Text classification is widely used in many industries that rely on large amounts of text data, including customer support, product development, and marketing. It helps automate tasks and extract valuable insights from the data.

Example Use Cases

Here are some use cases for classifying ExecVision sales call transcripts:

Automatically classify calls by product or service
Automatically classify calls by customer segment
Identify and classify calls with customer objections
Automatically prioritize calls based on their importance
Identify trends in customer feedback

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

Accessing Your Data and Identifying Categories

The first step is to identify the data you want to work with. In this case, we’re looking at ExecVision sales call transcripts. You can extract this data using the ExecVision API, export it in CSV format, or query a list of calls from your data warehouse or BI tool.

Next, you need to find or create your list of categories for classifying the calls. This might include product or service categories, customer segments, or call importance levels.

Common examples of product categories include:

Software
Hardware
Services

Common examples of customer segments include:

Small business
Enterprise
Non-profit

Once you have your data and categories, you can use generative AI to automatically classify your ExecVision sales call transcripts. This will help you to extract valuable insights from the data and make better decisions based on the trends you identify.

Conclusion

Text classification with generative AI is a powerful tool for extracting insights from large amounts of text data. By automating the process of classifying sales call transcripts, you can save time and improve the accuracy of your analysis. Whether you’re working in sales, customer support, product development, or marketing, text classification can help you make better decisions and improve the customer experience.

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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