In 2018, we’ve seen the rise of data-driven strategies in a wide variety of businesses, ranging from front end development to ecommerce to, in fact, finance. The usage of data driven marketing strategies has become quite popular nowadays and, after the recent Cambridge Analytica scandal, data science as a whole has definitely reached its maximum level of acknowledgement worldwide. With this being said, let’s analyse how data is driving the world of finance, fintech and general technology.

Tailored Marketing Strategies

Everyone is definitely familiar with interfaces like Adroll and other forms of retargeting, relying on keyword-driven setups or simple cookies, but fewer may be familiar with data points and data selections. These two terms are referring to numerical values that are leading to models which are describing how a single user is perceiving pieces of content on a page, or throughout an entire site. With this being said, having these “guidelines” when setting up a marketing strategy is definitely something which could set the bar higher.

The GDPR Variable

With data acquisition and, in particular, after the Cambridge Analytica scandal, there has been a growing interest in data science applied to digital marketing and its regulation: it’s not possible, in fact, to anonymously gather data from users without letting them know. This has become the priority when GDPR was being planned given the fact that the Cambridge Analytica scandal has set the bar extremely high in regards to data acquisition.

Some Examples

In finance, data is used massively within lending and loans. With examples like UK Bridging Loans, which offers development finance using an innovative fintech portal, it’s easy to understand how the entire data driven architecture has been set into place. To start off, when approving/declining any form of finance, the tool recognizes values which are provided by the company itself, leading to a faster elaboration of the application itself, since it’s done automatically. It’s very important to state how machine learning is the foundation of the entire process, which is then guided by a generally JS based architecture.