Deep Learning vs. Machine Learning: What Marketers Need to Know

By esantiago@hubspot.com (Erica Santiago)

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Artificial intelligence continues to be a hot topic within the marketing industry. The market for AI in marketing will likely grow to $107.5 billion by 2028, up from $15.84 billion in 2021.

As the technology’s role in marketing expands, you’ve probably heard the terms “deep learning” and “machine learning,” — but what do these terms mean? Here’s what marketers need to know about deep learning and machine learning.

What is Machine Learning?

3 Common Ways Marketers Use Machine Learning

What is Deep Learning?

3 Common Ways Marketers Use Deep Learning

The Difference Between Machine Learning and Deep Learning

An example of machine learning is speech recognition. Machine learning can translate speech into text; software applications can convert live voice and speech recordings into text files.

Voice search, voice dialing, and appliance control are all examples of machine learning in speech recognition.

So if you’ve ever listened to your favorite song by saying, “Alexa, play ____,” you can thank machine learning for the capability.

3 Common Ways Marketers Use Machine Learning

Here are some ways machine learning is often implemented in marketing strategies.

1. Predictive Recommendations

Predictive recommendation machines rely on data to predict what content or services a user would enjoy. A well-known example is Netflix’s AI system that recommends movies and shows based on what a user has already watched.

The AI reportedly saves Netflix $1 billion annually through decreased churn and higher retention.

2. Churn Prediction

Some companies use machine learning to predict when a customer is about to churn so the company can take action before the customer leaves.

They achieve this by examining demographics, past user actions, and other data to predict future behavior.

For example, if a customer’s behavior indicates they may end their subscription to a music stream. In that case, the service may offer an exclusive deal — such as a temporarily discounted subscription rate — to keep them from churning.

This type of machine learning helps companies keep high retention rates, which leads to increased revenue.

3. Lead Scoring

Leading scoring predicts which leads are likely to convert into customers. This form of machine learning helps sales teams avoid manually sorting and reviewing thousands of leads every month.

Teams can use a lead scoring model to automatically identify and prioritize the most promising, thus boosting productivity while reducing costs.

What is Deep Learning?

Deep learning is a discipline of machine learning that uses algorithms and data to mimic the human brain to train a model. This discipline uses neural networks to learn a specific task.

The neural networks comprise interconnected neurons that process data in the human brain and computers.