Machine Learning

What is machine learning?

We’ve been living with Machine Learning (ML) technology for decades. Not to be confused with the recent rise of Large Language Models (LLM) like ChatGPT, ML is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed to do so. The difference? ML is like teaching a computer to learn from experience; in comparison, LLMs are super-smart students that specialize in understanding and using human language. 

How does machine learning work?

ML algorithms use historical data as inputs to predict new output values. The more data an algorithm is trained on, the better it will be able to make predictions. In the context of performance marketing, ML is used to automate tasks, improve targeting, and optimize campaigns. 

Types of machine learning:

There are three main types of ML algorithms:

• Supervised learning: In supervised learning, the algorithm is trained on a set of labeled data, where each input has a known output. The algorithm then learns to predict the output for new inputs.
• Unsupervised learning: In unsupervised learning, the algorithm is trained on a set of unlabeled data, where the outputs are unknown. The algorithm then learns to find patterns and relationships in the data.
• Reinforcement learning: In reinforcement learning, the algorithm learns from its own experiences. The algorithm is rewarded for taking actions that lead to desired outcomes and penalized for taking actions that lead to undesired outcomes.

In addition, there are many different types of ML models, such as:

• Linear regression: Used to predict continuous values, such as sales or customer lifetime value.
• Logistic regression: LUsed to predict binary outcomes, such as whether or not a customer will churn.
• Decision trees: Used to classify data into different categories.
• Random forests: An ensemble learning method that combines the predictions of multiple decision trees to improve accuracy.
• Support vector machines: Used to classify data into different categories and find patterns in data.

How to measure machine learning:

The performance of ML algorithms is typically measured using metrics such as accuracy, precision, and recall. These metrics measure how well the algorithm is able to make correct predictions.

Why is machine learning important to marketers?

ML is important to marketers because it can lead to increased efficiency, improved results, and higher ROI. For example, ML algorithms can be used to:

• Identify patterns in customer behavior and preferences
• Predict customer churn
• Segment customers into different groups
• Target ads to specific audiences
• Optimize bidding strategies
• Measure the effectiveness of campaigns

Who needs to know what machine learning is:

• Performance marketer
• Digital marketer
• Marketing manager
• Brand manager
• Product manager
• Data scientist
• Business analyst
• Machine learning engineer
• Paid search specialist
• Display advertising specialist
• Email marketer
• CEO

Use machine learning in a sentence: “The advertising industry has been using machine learning to understand audiences by building and organizing large scale, statistically representative audiences since the early years of the internet. We can use ML for lots of different tasks from determining bid prices to optimizing campaigns.”