How to classify Mixmax Sales Emails with generative AI

Text Classification

Mixmax

In today’s digital age, sales teams are inundated with emails from potential customers. Sorting, tagging, and prioritizing these emails can be a tedious and time-consuming task for sales reps. But, what if we told you that you could use generative AI to automatically classify Mixmax sales emails? In this post, we’ll show you how to use text classification to automate this process.

What is Text Classification?

Text classification is an NLP technique that uses machine learning algorithms to assign one or more predefined categories or labels to a given piece of text. The algorithms learn from a training set of labeled text data and use statistical models to identify patterns and features in the text that can be used to classify new, unseen text data. Text classification is used in a wide range of applications, including spam detection, sentiment analysis, and content tagging.

Example Use Cases

Use cases for classifying Mixmax sales emails include:

Automatically prioritize high-potential leads
Tag emails by product or service category
Identify and tag competitor mentions
Automatically route emails to the correct sales rep or team
Reduce manual tagging errors and improve response times

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

Finding your Input Data and Categories

The first step in using text classification to automate Mixmax sales emails is to identify the data you want to analyze. You can extract this data using the Mixmax API, export it 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 create a list of categories for classifying the emails. This might include product categories, lead quality, industry, or competitor mentions. You can also create custom tags that fit your specific sales process.

For example, you might want to use the following categories:

Lead Quality: High, Medium, Low
Product Category: Software, Hardware, Services
Competitor Mention: Yes, No

Once you have your data and categories, you can use generative AI to automatically classify your Mixmax sales emails. This will help you to reduce the time it takes to process sales emails, improve response times, and ensure that sales reps are focusing on high-potential leads.

By using text classification to automate Mixmax sales emails, you can streamline your sales process and improve overall efficiency. Best of all, you don’t need to be a data science expert to get started. With a few simple steps, you can use generative AI to simplify your sales workflow and improve your team’s performance.

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.

If you have feedback, please let us know!