How to classify Boomerang Sales Emails with generative AI

Text Classification

Boomerang

Sales teams often rely on email campaigns to reach out to potential customers. However, it can be challenging to manually sort through the responses to these campaigns, especially when dealing with large volumes of data. In this article, we’ll explore how to use generative AI to automatically classify Boomerang sales emails, streamlining your sales process.

What is Text Classification?

Text classification is an NLP technique that involves using machine learning algorithms to automatically assign predefined categories or labels to a given piece of text. It is commonly used for spam detection in emails and sentiment analysis in social media posts and reviews.

Text classification works by training algorithms on labeled text data and using statistical models to identify patterns and features in the text that can be used to classify new, unseen text data. The result is more efficient and accurate tagging of your data.

Example Use Cases

Use cases for classifying Boomerang sales emails include:

Automatically classify emails by level of interest or engagement
Automatically classify emails by specific products or services mentioned
Identify and classify spam emails
Automatically prioritize high-value leads
Assist sales teams in identifying areas of interest for follow-up communication

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

Finding Your Input Data and Categories

First, you need to identify the data that you want to work with. Here, we are looking at Boomerang sales emails. You can export this data from Boomerang in CSV format, query a list of emails from your data warehouse or BI tool, or copy and paste with an example email.

Next, you need to find or create your list of categories for classifying the emails. This might include product categories, level of interest, or other relevant criteria.

Common examples of sales email categories include:

Product or service inquiries
Price or quote requests
Customer feedback and suggestions
General inquiries
Follow-up communication
Unsubscribes

Once you have your data and categories, you can use generative AI to automatically classify your Boomerang sales emails. This will help you to reduce the time it takes to process sales emails and ensure that leads are prioritized and routed to the correct sales teams.

With text classification, you can streamline your sales process and improve customer experiences by quickly and accurately categorizing Boomerang sales emails.

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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