Abstract:
In the contemporary world, the amount of digital content directly re- leased by businesses is growing in all industry segments. Every company aims to be competitive and maximize the reach of each piece of content shared to spread as far as possible the boundaries of brand and produc- t/service awareness. Moreover, a particular glance goes on how people behave while interacting with digital information, and how communica- tion influences the market share inside a competition that is becoming more and more fierce.
The present thesis fits into this context, examining the case study of a famous Italian brand that operates inside the luxury automotive industry. It brings new theoretical and practical techniques that allow web marketers to boost visibility and content performances while promptly identifying marketing opportunities in the digital arena.
Three main types of optimization and analysis are addressed along with the chapters: SEO, UX, and CI. The first aims to increase website visibility and reach the broadest possible population. The section about SEO (Search Engine Optimization) mainly deals with its technical as- pects, but a more comprehensive explanation of the topic is given. In this section, the specifications of a custom tool developed for managing redi- rects are provided, accompanied by the results obtained by its use. UX (User Experience) aims to enhance the engagement factors of the gained user base. In this thesis, UX is approached from an analytical perspec- tive, focusing on hypotheses based on data and validated by A/B testing. This methodology allows to avoid the in-depth study of the topic, that would require several design architecture aspects that are out of the thesis scope. The latter chapter closes the treaty by transposing the knowledge gained from previous sections into the competitive environment. It holds and explains the concept of website popularity in relation to its com- petitors. Finally, we propose and discuss a CI (competitive intelligence) metric along with a technique to discover clouds of query keywords that semantically partitions the website content.
