Machine learning for marketers and sales professionals

I'm Dr. Jose Mendoza from NYU. Today, we're going to explore a critical component of artificial intelligence: machine learning, specifically in the context of marketing and sales. Machine learning or ML is not just a trendy topic, it is a transformative tool that can fundamentally change how we understand our customers, optimize our marketing strategies, and drive business growth.

In this session, we'll discuss the basics of ML, examine the different types of learning, and examine how these concepts are applied in real-world marketing and sales scenarios. Whether you are just starting with ML or looking to deepen your understanding, I aim to make these concepts accessible and show you how they can be practically applied in your work. We'll also look at specific examples of case studies to see how companies successfully leverage ML to improve their operations and customer interactions. By the end of this talk, you should have a solid grasp of what ML is, how it works, and how to integrate it into your strategies.

What exactly is machine learning? At its core, ML involves training algorithms on data to make predictions or decisions without being explicitly programmed for every possible scenario. Think of it this way, in traditional programming, you write specific instructions for the computer to follow. In ML, you provide a computer with data and let it figure out the best way to achieve the desired outcome. It learns from the data, identifies patterns, and makes decisions based on what it has learned. There are three primary types of learning in ML: supervised, unsupervised, and reinforcement learning. Each has its unique approach and applications. In supervised learning, the model is trained on labeled data, which means the data contains the correct answers. The model learns to map inputs to the correct outputs and can make predictions of new, unseen data. This is particularly useful for tasks like predicting customer churn or segmenting customers. Unsupervised learning, on the other hand, doesn't use labeled data, Instead, it tries to find hidden patterns or groupings in the data. This is valuable for tasks like clustering customers based on their behavior or performing market basket analysis. Finally, reinforcement learning is all about learning through trial and error. The model learns by interacting with an environment and receiving feedback in the form of rewards or penalties. This approach is used in dynamic changing environments such as optimizing ad bids in real time. Understanding these different types of learning will give us a solid foundation as we delve into how ML is applied in marketing and sales.