How to extract keywords from Outreach Sales Emails using generative AI

Keyword Identification

Outreach

Outreach sales emails are a critical part of the sales process. They help you connect with potential customers and move them through the sales funnel. However, analyzing each email manually for insights and trends can be time-consuming and impractical. Fortunately, you can use generative AI to automatically extract keywords from your outreach sales emails. In this post, we’ll show you how to extract keywords from outreach sales emails using generative AI.

What is Keyword Extraction?

Keyword extraction is a natural language processing (NLP) technique that involves identifying the most important or relevant words or phrases in a piece of text. It helps you extract key information, themes, and topics from large amounts of text data. Keyword extraction has many applications, such as search engine optimization (SEO), content analysis, and topic modeling.

You can use keyword extraction to analyze and summarize large amounts of text data to quickly identify the most important information and themes. This can help you improve the quality and efficiency of your outreach sales emails.

Example Use Cases

Use cases for extracting keywords from outreach sales emails include:

Identifying common pain points and objections from potential customers
Measuring the effectiveness of different outreach email campaigns
Improving the personalization and relevance of outreach emails
Identifying areas for improvement in sales messaging and strategy

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

Finding Your Input Data and Identifying Preliminary Keywords

You first need to identify the outreach sales emails that you want to work with. You can extract this data from your CRM or email marketing tool. For example, you can export your outreach sales emails in CSV format and upload them to your generative AI tool.

Next, it can be helpful to identify common keywords that you may want to extract from your outreach sales emails. Generative AI tools can be used to both identify and measure frequency of keywords but also to suggest additional keywords you may not have been aware to look for. For example, you might find that recurring pain points or objections from potential customers provide insights into product improvement opportunities non-obvious to the initial outreach email.

Once you have your data and preliminary keywords identified, you can use generative AI to automatically extract keywords from your outreach sales emails. This will help you quickly identify the most important information and themes from your outreach sales emails. This can help you improve the quality and efficiency of your sales process.

Conclusion

Extracting keywords from outreach sales emails using generative AI can help you quickly identify the most important information and themes from your outreach sales emails. This can help you improve the quality and efficiency of your sales process. By following the steps outlined in this post, you can start using generative AI to extract keywords from your outreach sales emails today.

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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