How to extract keywords from SalesLoft Sales Emails using generative AI

Keyword Identification

SalesLoft

SalesLoft is a powerful sales engagement platform that allows you to automate your sales outreach, track your team's performance, and optimize your sales process. However, with the sheer volume of sales emails that your team sends and receives, it can be challenging to identify the most important insights and trends. In this post, we'll show you how to use generative AI to automatically extract keywords from SalesLoft sales emails.

What is Keyword Extraction?

Keyword extraction is an NLP technique that involves identifying 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 and has many applications, such as SEO, content analysis, and topic modeling.

You can use keyword extraction to analyze and summarize large amounts of sales data to quickly identify the most important information and trends.

Example Use Cases

Use cases for extracting keywords from SalesLoft sales emails include:

Identifying common sales objections and addressing them proactively
Identifying top-performing sales reps and replicating their outreach strategies
Identifying key pain points and addressing them in your product or service
Improving your email open and response rates

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

Accessing Your SalesLoft Sales Emails

You can extract your SalesLoft sales emails using the SalesLoft API or export them in CSV format. You can also use a data warehouse or BI tool to query a list of sales emails.

For more information on the SalesLoft API see here: https://developers.salesloft.com/

Identifying Preliminary Keywords

Before extracting keywords from your sales emails, it can be helpful to identify preliminary keywords that you may want to extract. These can be keywords that are relevant to your product or service or specific to your sales process.

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 objections from prospects may provide insights into product improvement opportunities non-obvious to the initial sales inquiry.

By using generative AI to automatically extract keywords from your SalesLoft sales emails, you can save time and effort while gaining valuable insights into your sales 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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