How to classify Talkdesk Sales Call Transcripts with generative AI

Text Classification

Talkdesk

As a company that relies on sales calls to drive revenue, it's crucial to collect and analyze data from those calls to improve your sales strategy and tactics. However, manually analyzing and categorizing sales call transcripts can be a time-consuming and error-prone task. In this post, we'll show you how to use generative AI to automatically classify Talkdesk sales call transcripts, saving you time and improving the accuracy of your data analysis.

What is Text Classification?

Text classification, also known as text categorization, is a natural language processing (NLP) technique that involves using machine learning algorithms to automatically assign one or more predefined categories or labels to a given piece of text. The 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 used in a wide range of applications, from spam detection in emails to sentiment analysis in social media posts and reviews. In the case of Talkdesk sales call transcripts, it can help you to automatically categorize calls based on their content, making it easier to identify trends and areas for improvement.

Example Use Cases

Use cases for classifying Talkdesk sales call transcripts include:

Automatically categorize calls by product or service
Identify calls that require follow-up or further investigation
Automatically categorize calls by outcome (e.g., sale, no sale)
Identify calls that require coaching or additional training for sales reps
Track customer sentiment or satisfaction levels over time

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

Finding Your Input Data and Categories

To classify Talkdesk sales call transcripts, you first need to identify the data that you want to work with. You can extract call recordings and transcripts from Talkdesk using their API or by exporting them in CSV format.

Once you have your data, you need to find or create your list of categories for classifying the calls. This might include product or service categories, call outcomes, or customer sentiment levels.

Common examples of product or service categories include:

Software
Hardware
Consulting services
Training and education
Support and maintenance
Other (for calls that don't fit into any specific category)

You can also create categories based on call outcomes, such as:

Sale
No sale
Lead
Follow-up required
Other (for calls with outcomes that don't fit into any specific category)

Once you have your data and categories, you can use generative AI to automatically classify your Talkdesk sales call transcripts. This will help you to save time and ensure that your data analysis is accurate and consistent.

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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