Data-Driven Economy and AI in Decision-Making Process: A Retail case study

Over the past few decades, the world has undergone global changes that have affected all spheres of human activity, changed approaches to decision-making, increased the accuracy of predictions, and revealed patterns in the consequences of decisions made. Previously unavailable ways to reduce uncertainty in decision-making are now a reality: predictive demand algorithms in retail (Walmart), recommendation algorithms in OTT (Netflix), supply chain optimization algorithms (UPS), and others. Research by consulting companies shows that data-driven organizations retain customers better (McKinsey, 2017), are more profitable (McKinsey, 2022), achieve revenue goals 58% more often, and exceed them 162% more often than lagging competitors (Forester Consulting, 2021). This thesis aims to delve deeper into the issues of data-driven economics and data-driven organizations and the case of using AI technologies for data analysis in the field of marketing in a retail company to improve the efficiency of decision-making in the context of time-limited promotional campaigns.