Python Tutorial: Why use ML for marketing? Strategies and use cases

Want to learn more? Take the full course at https://learn.datacamp.com/courses/machine-learning-for-marketing-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi there! My name is Karolis and I lead the Analytics & Science team at Amazon. Welcome to the course of Machine Learning for Marketing! In this section, we will review examples of how machine learning is applied in optimizing marketing strategy, learn typical data formats, and share insights into the best practices. Let's start by describing the three different types of machine learning. The first one is supervised learning. These models use data about observations to predict a target variable. There are two kinds of supervised learning - classification and regression. In classification, we attempt to predict a categorical variable or a class. In the next chapter, we will build a classification model predicting whether a customer will churn. In the third chapter, we will build a regression model to predict customer purchases in the next month. The second machine learning type is unsupervised learning. Here, there is no target variable, and the models use different data points about observations to group them into similar clusters. A popular use case is customer segmentation. We will segment customers based on their product purchase history in the last chapter of this course. Finally, there is reinforcement learning which is outside of the scope of this course - the models in this space have agents that act on their own to maximize rewards defined by the environment. They are used in robotics and other advanced fields. Supervised learning models require two key data elements. The first one is the target variable which is what we want to predict. It could be predicting which customers will churn, or which customers will buy again. Another example is predicting how much the customers will spend in the next 30 days. The second data e