Data-driven marketing is a process where marketers gain insights and trends through in-depth analysis of numbers. It's more than just a dashboard of numbers; it's the insight represented in those numbers. Methods such as machine learning, A/B testing, etc. are statistics and mathematics-based formulas, not science fiction.

It's important to note that there are misconceptions surrounding data-driven marketing. Some may undervalue its importance and view it as simply a collection of numbers, while others may overcomplicate it by linking it to buzzwords like big data, machine learning, or artificial intelligence.

Examples of data-driven marketing and branding include analyzing the performance of ads on social media platforms, using A/B testing to determine the effectiveness of branding changes, and utilizing machine learning to optimize website design.

Tools like Google Analytics, Facebook's pixel, Sisense and Looker can aid in data-driven marketing and accelerate the process of gaining insights and informing the direction of brand improvements.

Being data-driven is a cyclical process, leading to exponential improvement. As a brand generates more data, it can be used to improve and accelerate the brand, which in turn increases user engagement and generates even more data. This process is ongoing, allowing for constant improvement and optimization of the brand.