Big Data-Driven Customer Analytics for Consumer Behavior Prediction and Sustainable Business Growth

Abstract

In this research, we examine how big data analytics actually transforms raw consumer information into a roadmap for sustainable business growth. Our primary objective was to move past the usual focus on just selling more and instead look at how predictive models help a company stay resilient over the long haul. We used a systematic review methodology, pulling from a decade of secondary data and peer-reviewed studies to see how tools like machine learning are changing the game. We found that while high-tech algorithms like Random Forests are great at cutting down on wasted resources by sensing demand early, there’s still a huge trust gap because of how invasive some data collection feels. One of our major findings is that the most successful businesses are those that treat data as a way to build a more efficient, human-centric model rather than just a way to spike quarterly profits. Regarding policy, we argue that regulators and managers need to focus more on algorithmic accountability to make sure these black box systems aren't just automating old biases. Ultimately, we conclude that for a business to truly grow in a sustainable way, it has to balance its data hunger with a real commitment to consumer privacy and ethical transparency.