How to classify Intercom Support Call Transcripts with generative AI

Text Classification

Intercom

As a company providing customer support through Intercom, it is important to classify support call transcripts accurately and quickly. Manually tagging each call for category and urgency level can be time-consuming and prone to errors, leading to negative customer experiences. In this post, we will show you how to use generative AI to automatically classify Intercom support call transcripts for faster and more efficient processing.

What is Text Classification?

Text classification, also known as text categorization, is a natural language processing technique that involves using machine learning algorithms to automatically assign predefined categories or labels to a given text document. The algorithms learn from a labeled training set of 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 various applications, including spam detection, sentiment analysis, and content filtering. It has become a crucial tool for industries that rely on large amounts of text data, helping to automate tasks and extract valuable insights from the data.

Example Use Cases

Intercom support call transcripts can be classified for the following use cases:

Automatically classify calls by urgency level
Automatically classify calls by category and subcategory
Identify and classify spam calls
Automatically prioritize urgent calls
Reduce average resolution time

Teams that might find these use cases helpful include customer support, customer success, product, operations, and finance.

Finding Your Input Data and Categories

You first need to identify the data that you want to work with, which are the Intercom support call transcripts. You can extract this data using the Intercom API, export it in CSV format, query a list of call transcripts from your data warehouse or BI tool, or copy and paste with an example transcript.

For more information on the Intercom API, see here: https://developers.intercom.com/building-apps/docs/getting-started-with-the-intercom-api

Next, you need to find or create your list of categories for classifying the call transcripts, which might include categories like:

Technical issues
Billing and payment issues
Product information and features
Customer feedback and suggestions
Shipping and delivery issues
Account management
General inquiries
Return and exchange requests
Training and education
Sales and marketing

Once you have your data and categories, you can use generative AI to automatically classify your Intercom support call transcripts. This will help you to reduce the time it takes to process support calls and ensure that calls are routed to the correct point of contact.

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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