what marketers need to know

artificial intelligence Marketing continues to be a hot topic within the industry. Market potential for AI in marketing to grow $107.5 billion by 2028up from $15.84 billion in 2021.

As the role of technology 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.

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What is machine learning?

3 Common Ways Marketers Use Machine Learning

What is Deep Learning?

3 Common Ways Marketers Use Deep Learning

Difference Between Machine Learning and Deep Learning

Speech recognition is an example of machine learning. Machine learning can translate speech into text; Software applications can convert live voice and speech recordings to text files.

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

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

3 Common Ways Marketers Use Machine Learning

Here are some of the ways in which machine learning is often applied in marketing strategies.

1. Predictive Recommendations

Predictive recommendation machines rely on data to predict what content or services users will enjoy. A famous example is Netflix’s AI system that makes recommendations based on movies and shows a user has already watched.

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 that 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 subscriber’s behavior indicates that he or she may unsubscribe from a music stream. In that case, the service may offer a special deal—such as a temporarily discounted subscription rate—to keep them from churning.

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

3. Lead Scoring

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

Teams can use the lead scoring model to automatically identify and prioritize the most promising leads, thus increasing 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 in order to train a model. This discipline uses neural networks to learn a specific task.

Neural networks consist of interconnected neurons that process data in the human brain and in computers.

3 Common Ways Marketers Use Deep Learning

Here are some of the ways marketers are using deep learning in their strategies.

1. Partition

Deep learning models can find patterns in the data to initiate advanced segmentation. This allows marketers to easily and quickly identify the target audience for a campaign and forecast potential leads.

2. Hyper-personalization

Deep learning could develop personalization engines that help marketers streamline the process of delivering hyper-personalized content.

Examples of hyper-personalized content are websites that show content depending on who is browsing or push notifications for customers who leave without making a purchase.

3. Predicting customer behavior

Marketers can use deep learning to predict customer actions by tracking how customers move through a brand’s website and how often they make purchases.

In doing so, AI can tell companies which products and services are in demand and should be the focus of future campaigns.

Difference Between Machine Learning and Deep Learning

Machine learning is a subset of artificial intelligence, while deep learning is a subset of machine learning.

Machine learning means that computers learn from data using algorithms to learn and perform tasks without being programmed – in other words, without human intervention. And deep learning uses algorithms and neural networks to train a model.

The image below shows the relationship between Artificial Intelligence, Machine Learning and Deep Learning.

Circle graph depicting machine learning is a subset of AI and deep learning is a subset of machine learning.

Machine learning can train even on small data sets, while deep learning requires large amounts of data.

Deep learning is better at learning through its environment and from past mistakes, but machine learning requires more human intervention to learn and correct itself.

Here are some other important differences between machine learning and deep learning:

  • Machine learning requires less training but can result in less accuracy.
  • Deep learning requires high training and high accuracy results.
  • Machine learning draws direct, linear correlations.
  • Deep learning builds complex, non-linear correlations.

As artificial intelligence is integrated into various industries and our daily lives, marketers must understand its fundamentals and learn how to leverage it for their brands.

Both deep learning and machine learning will create new possibilities in marketing by streamlining tedious processes and predicting audience behavior.

AI can help marketers improve their strategies and ensure they are always on trend with consumers.

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