Variability Driven Consumer Segmentation (VDCS) Framework A Novel and Dynamic Consumer Segmentation Framework Using Real-Time Data

Ishaan Kapur () and Varsha Jain ()
Additional contact information
Ishaan Kapur: MICA
Varsha Jain: ESSCA School of Mangement

A chapter in Marketing in a Digital World, 2026, pp 303-320 from Springer

Abstract: Abstract 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.

Keywords: Consumer segmentation; Real-time marketing; Behavioural analytics; Dynamic consumer profiling; Entropy-based segmentation; Hyper-personalised marketing; Predictive analytics (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-95-6505-4_15

Ordering information: This item can be ordered from
http://www.springer.com/9789819565054

DOI: 10.1007/978-981-95-6505-4_15

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().