Netflix Recommender System: Algorithmic Personalization as the Core Organizational Resource

Abstract

This case study explores how Netflix, Inc. transformed from a DVD-by-mail business into a global, algorithm-driven entertainment platform. It argues that the Netflix Recommender System (NRS), the company’s personalization algorithm, functions not merely as a technological feature but as a core organisational resource that drives growth, innovation, and sustained competitive advantage. Rather than serving only as a recommendation tool, the system has become central to subscriber retention, customer engagement, and strategic decision-making throughout the organisation. It also guides large-scale content investment decisions worth billions of dollars annually and creates a durable competitive moat built on nearly two decades of unique behavioural data that competitors cannot easily replicate. The case is analysed using The Digital Transformation Playbook Five-Domain Digital Transformation Framework, Dynamic Capabilities Theory, and the Business Model Innovation model developed by Alexander Osterwalder and Yves Pigneur (2010). Applied phase by phase, these frameworks explain how algorithmic personalisation evolved from a simple DVD-selection interface into the central mechanism shaping Netflix’s value creation, customer relationships, operations, and revenue model. They further demonstrate how digital capabilities can be transformed into scalable strategic assets. The case also highlights an important theoretical tension. While the algorithm generates significant competitive and financial value, it simultaneously influences large-scale cultural consumption by shaping what audiences watch, discover, and prefer. This raises broader ethical concerns regarding transparency, accountability, consumer autonomy, and the growing power of firms whose primary strategic asset is a taste-making mechanism capable of directing consumer attention and cultural trends at scale.