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Content marketing is a crucial aspect of digital marketing in modern firms. By generating content that is interesting and engaging, companies have the two-fold advantage of promoting their products in a relatable way, while increasing familiarity and engagement with the brand. As data scientists at Credit Suisse, we value our content teams because their voice is the bank's voice. We strive to provide them with the best tools to increase their articles' success. With the help of machine learning, we have created digital products that allow them to improve articles before publication, recommend them to the most interested readers, and track their performance. The chapter begins with a brief introduction to content marketing, followed by an overview of our data, a review of the business challenges we have encountered, and the machine learning solutions we have developed in order to provide the best data insights to our internal and external stakeholders. We close the chapter with a brief summary of our work.

Purpose — Ranking is a natural task for a search engine; a search engine result page is the most common example. This chapter aims at illustrating the motivations and the concepts of rank correlation in a practical way for the researchers active in the different domains of search engines.

Methodology/approach — To this end, this chapter provides a survey according to a topic-oriented basis of the search engine evaluation literature specifically devoted to or based on rank correlation; the chapter explains and illustrates how statistics is the only approach to rank correlation.

Findings/research limitations/implications — The chapter introduces the pros and cons of rank correlation measures through a light-weight formal description and a number of concrete examples to find the measure that better fit a context.

Practical implications — This chapter provides a blueprint for the application of rank correlation within scientific experimentation or item/service recommendation.

Social implications — Rank correlation analyses impact on the success or failure of a search engine in performing the tasks for which it has been designed and hence on the people's daily life activities.

Originality/value of paper — This chapter places rank correlation within a scientific research perspective and in particular connects to and complements documentation on search engine evaluation.

Today's marketing is dominated by decision-making based on artificial intelligence and machine learning. This study focuses on one semi- and unsupervised machine learning technique, generative adversarial networks (GANs). GANs are a type of deep learning architecture capable of generating new data similar to the training data that were used to train it, and thus, it is designed to learn a generative model that can produce new samples. GANs have been used in multiple marketing areas, especially in creating images and video and providing customized consumer contents. Through providing a holistic picture of GANs, including its advantage, disadvantage, ethical considerations, and its current application, the study attempts to provide business some strategical orientations, including formulating strong marketing positioning, creating consumer lifetime values, and delivering desired marketing tactics in product, promotion, pricing, and distribution channel. Through using GANs, marketers will create unique experiences for consumers, build strategic focus, and gain competitive advantages. This study is an original endeavor in discussing GANs in marketing, offering fresh insights in this research topic.