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The main purpose of this chapter is to examine the attitudes and behaviors of digital consumers in omnichannel world. For this purpose, in order to understand the behavior of digital consumers, literature review has been made. The transformation of traditional consumers into digital consumers has been examined within the framework of marketing periods. What are the new types of consumers and demographic and psychographic characteristics of these, and how they are segmented in the market are important issues. Another important issue is how digital environmental factors affect digital consumers when shopping online. Purchasing processes of digital consumers and their behaviors, which channels they use and how they make purchasing decisions play an important role in understanding digital consumer behaviors. After the purchasing decision, it is examined whether digital consumers will transfer their experiences to other consumers, satisfaction and loyalty experiences, and whether they will buy again in digital environmental.

Neural networks, which provide the basis for deep learning, are a class of machine learning methods that are being applied to a diverse array of fields in business, health, technology, and research. In this chapter, we survey some of the key features of deep neural networks and aspects of their design and architecture. We give an overview of some of the different kinds of networks and their applications and highlight how these architectures are used for business applications such as recommender systems. We also provide a summary of some of the considerations needed for using neural network models and future directions in the field.