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Personalisation (British: personalisation; American: personalization) refers to the process of tailoring products, services, content, or experiences to individual users or groups based on data about their preferences, behaviour, context, or demographics. It encompasses a range of techniques from simple rule-based adjustments to advanced machine-learning-driven recommendations.

Applications span digital services (news feeds, streaming, e-commerce), marketing (targeted advertising, email segmentation), user interfaces (adaptive layouts, accessibility), and physical experiences (retail, smart environments). Core technologies include recommender systems, collaborative and content-based filtering, natural language processing, predictive analytics, and real-time contextual signals such as location or device.

Benefits attributed to personalisation include increased relevance, higher engagement and conversion rates, improved user satisfaction, and more efficient resource allocation. Organisations use personalisation to improve retention, optimise content delivery, and personalise customer journeys at scale.

Challenges and criticisms involve privacy risks, data security, potential reinforcement of bias, loss of serendipity, and the creation of filter bubbles. Legal and ethical considerations are shaped by data-protection regulations (for example GDPR), industry standards, and expectations of transparency and user control. Best practices encourage minimising data collection, offering clear consent and opt-out mechanisms, auditing algorithms for fairness, and providing explanations of personalised decisions.

Ongoing research focuses on privacy-preserving techniques (differential privacy, federated learning), fairness-aware models, and approaches that balance personal relevance with diversity and user autonomy.