How to extract keywords from Help Scout Support Call Transcripts using generative AI

Keyword Identification

Help Scout

As a data analyst, you understand the value of insights gained from customer support conversations. However, manually combing through transcripts is inefficient and impractical. Fortunately, there is a cost-effective solution to extract valuable insights from your Help Scout support call transcripts using AI. In this post, we will guide you on using generative AI to automatically extract keywords from Help Scout support call transcripts.

What is Keyword Extraction?

Keyword extraction is a natural language processing (NLP) technique that identifies the most important or relevant words or phrases in a piece of text. It can be used to extract key information and themes from text, such as search engine optimization (SEO), content analysis, and topic modeling. Keyword extraction can be performed manually, but it is more efficient when automated using machine learning algorithms. These algorithms learn to recognize patterns and features in the text associated with important words or phrases and can be trained on a labeled dataset of text.

Using keyword extraction, you can analyze and summarize large amounts of text data to quickly identify critical information and themes.

Example Use Cases

Keyword extraction from Help Scout support call transcripts can be useful for various teams, such as:

Customer support
Customer success
Product
Marketing
Operations

Some examples of how keyword extraction can be applied include:

Categorizing and prioritizing support calls
Identifying common issues and trends
Improving response times
Personalizing customer interactions
Identifying areas for training and improvement

Accessing the Data and Identifying Preliminary Keywords

To extract keywords from Help Scout support call transcripts, you need to first identify the data you want to work with. You can extract the data using the Help Scout API, export it in CSV format, query a list of transcripts from your data warehouse or BI tool, or copy and paste a transcript.

To improve the accuracy of your keyword extraction, it's helpful to identify common keywords that may appear in your support call transcripts. Generative AI tools can help identify and measure the frequency of keywords and suggest additional keywords you may not have thought of. For example, recurring customer inquiries around billing may provide insights into product improvement opportunities not obvious in the initial support inquiry.

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from your transcripts. This will help you identify critical information and themes in your transcripts and improve the quality and consistency of your customer support. By doing so, you can reduce churn and improve the efficiency of your support team.

For more information on the Help Scout API, see here: https://developer.helpscout.com/

By using keyword extraction on your Help Scout support call transcripts, you can gain valuable insights that can improve your customer support and drive business growth. We hope this post has been helpful in guiding you through this process.

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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