Machine Learning for Marketing

This course will explore the conceptual aspects of applying machine learning to problems in marketing, discuss case studies of machine learning used in the marketing sector, and explore practical implementations of techniques on real-world data from that industry.

The field of marketing has been steadily becoming more quantitative for some decades now, and so is well-poised to benefit from the adoption of ML models and techniques. AI is also extensively used in marketing to better understand and target customers and to provide more personalized experiences across sales channels.

In this course, Machine Learning for Marketing, you’ll explore machine learning techniques currently used by marketing teams across industries. First, you will look at what a Gartner report has to say about transformative technologies in marketing and you will explore some examples and cases of where ML is already being used in marketing - for customer segmentation, for price optimization, and for personalized experiences. Then, you'll also get an intuitive understanding of how recommendations systems work using content-based filtering and collaborative filtering.

Next, you will explore two ML case studies from research papers - the first one discusses how goal-based customer segmentation can be used in the banking industry to assess the creditworthiness of customers. The second case study will focus on dynamic pricing in public transportation to increase ticket sales and revenue.

Finally, you will get hands-on coding and see how you can use the k-means clustering algorithm to segment customers using marketing data.

When you are finished with this course you will have the awareness of how machine learning can be applied in marketing and get hands-on experience working with marketing data.