Reimagining Brand Equity Measurement with AI: From Surveys to Behavioral and Unstructured Data

Abstract

This article reimagines brand equity measurement in light of advances in artificial intelligence, big data analytics, and unstructured digital traces. It highlights how dominant consumer based frameworks still rely on cross sectional surveys and aggregate indices that only partially capture the dynamic ways in which customers interact with brands across digital touchpoints. Drawing on a systematic review of peer reviewed studies, the article synthesises evidence on the emergence of behavioural and trace data sources, such as clickstreams, transactions, mobile usage, social media communication, and online reviews, as continuous signals of brand related behaviour and meaning. The review shows that artificial intelligence and machine learning methods are increasingly used to model complex customer journeys, yet are rarely embedded inside core brand equity metrics. The article concludes by outlining a research agenda that calls for hybrid measurement frameworks which integrate survey indicators with behaviour based and content based data to construct more dynamic, context sensitive, and actionable views of brand equity today