Marketing is the process of creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large (Kotler & Keller, 2016) and David Aaker describes marketing as a tool for building relationships with customers and creating a strong brand. According to David Aaker, marketing is about creating value for the consumer while enhancing the brand's equity, which is the perceived worth of a brand in the consumer's mind (Aaker, Building Strong Brands, Free Press, 1996). Modern marketing is fundamentally data-driven. It now leverages and applies advanced data analysis tools, techniques and technologies to the ever evolving influx of digital data to curate efficient marketing strategies with an aim of higher consumer satisfaction and conversion rates. Conventional segmentation techniques based on psychographics, and demographics typically classify the consumers over a static criteria, therefore ignoring the dynamic changes in consumer behaviour resulting from situational and contextual nudges. VDCS Framework aims to address this limitation by employing Shannon's Entropy to quantify and categorise consumer behaviour into high and low entropy groups's High Entropy Persons (HEP) and Low Entropy Persons (LEP). These categories capture both routine and exploratory behaviours, allowing for hyper-personalised engagement strategies that adapt in real time to consumer actions. This paper presents The Variability Driven Consumer Segmentation (VDCS) Framework as a novel method based on behavioural variability to forecast and improve real-time consumer segmentation.