Nowadays, when it comes to selling a product online, two of the most significant factors are the pricing strategy and the investments in advertising. When determining the price of a product, it is essential to strike a balance. The price should neither be set too low, as this would result in a reduced revenue, nor too high, as it may deter potential buyers. The amount of money we invest in advertising should be balanced to let people know our offer without overspending. These two aspects are usually handled disjointedly by humans, but this may lead to suboptimal solutions. In this work, we focus on the adoption of online learning algorithms to solve the task of finding the optimal price for a product and understand how to advertise it properly. We face various aspects of pricing and advertising, offering theoretical frameworks to address the associated challenges. We start discussing pricing methods, with emphasis on the problem of learning in the presence of temporal dynamics. Then, we discuss the theoretical aspects of advertising, with a particular focus on marketing mix models. Finally, we bring together the problems of pricing and advertising, presenting a unified view.