Marketing in the age of hyper-personalisation: Lessons from analytics-driven brand success

This paper investigates how hyper-personalisation could revolutionise the marketing process with the aid of marketing analytics. As consumption data become more accessible, and as analytics advances, it is now possible for brands to create very specific campaigns. In this paper, best practice case studies of hyper-personalisation in some of the leading brands are critiqued with emphases on their methods, effectiveness and sustained value for enhancing customer engagement and loyalty. The principal findings are based on the examination of campaigns for diverse sectors such as retail, technology and entertainment, and cover customer categorisation, prediction and real-time personalisation. A literature review is provided, and several case studies are discussed to identify and understand the hyper-personalisation process. The paper outlines trends, patterns and lessons in the use of distributed data management technologies. It assesses the implications of hyperpersonalisation on brand—consumer intercourse, and proposes that any personalised endeavour should be harmonised with customers’ expectations and the norms of acceptable behaviour. This analysis also draws attention to how marketing analytics is important for precision marketing and for building consumer connections. Hence, the study emphasises key activities that foster hyper-personalisation success, including the use of data and information about the customer, immediate response, and emphasising value addition. Moreover, the paper expands on the issues arising from hyper-personalisation: data privacy and ethical issues, and interactions with technologies. The insights presented here aim to help marketers to better harness hyper-personalisation for competitive advantage and, at the same time, build accountability and strengthen trust among consumers. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.