How to extract keywords from Aircall Sales Call Transcripts using generative AI

Keyword Identification

Aircall

If you're looking to optimize sales performance, analyzing sales call transcripts is key. However, going through each transcript manually can be time-consuming and inefficient. In this post, we'll show you how to extract important keywords from Aircall Sales Call Transcripts using generative AI.

What is Keyword Extraction?

Keyword extraction is a natural language processing (NLP) technique that helps identify the most important words or phrases in a piece of text. Machine learning algorithms can be used to learn patterns and features in the text associated with important keywords or phrases. This technique can be used to analyze and summarize large amounts of text data to quickly identify the most important information and themes.

Example Use Cases

Use cases for extracting keywords from Aircall Sales Call Transcripts include:

Identifying common customer pain points
Improving sales pitch and messaging
Training sales reps on effective communication
Improving product features and offerings
Identifying areas for improvement in the sales process

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

Accessing and Analyzing the Data

The first step is to access the Aircall Sales Call Transcripts data. You can do this by exporting the transcripts in CSV format, querying a list of calls from your data warehouse or BI tool, or using the Aircall API. For more information on the Aircall API, see here: https://developer.aircall.io/

Next, you can use generative AI tools to identify the most important keywords in the transcripts. One tool you could use is the Python package GPT-3, which can be trained on your specific data to identify relevant keywords. there are also several out the box, less technically burdensome options like AirOps with pre-trained models for Aircall Sales Call Transcripts.

Once you have identified the most important keywords, you can use them to improve sales performance by adjusting messaging, training sales reps, and improving product features and offerings. By analyzing the transcripts, you can also identify areas for improvement in the sales process and optimize sales performance for better results.

Conclusion

Using generative AI to extract keywords from Aircall Sales Call Transcripts can help optimize sales performance and improve customer satisfaction. By identifying common pain points and areas for improvement, you can adjust messaging, train sales reps, and improve product offerings for better results. By using this technique, your sales team can increase efficiency and effectiveness, leading to improved sales performance overall.

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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