THE APPLICATION OF FRACTAL THEORY IN MARKETING: WHAT CAN WE DO?

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60076658%3A12310%2F25%3A43910089" target="_blank" >RIV/60076658:12310/25:43910089 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.worldscientific.com/doi/10.1142/S0218348X25300065" target="_blank" >https://www.worldscientific.com/doi/10.1142/S0218348X25300065</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1142/S0218348X25300065" target="_blank" >10.1142/S0218348X25300065</a>

Jazyk výsledku

angličtina

Název v původním jazyce

THE APPLICATION OF FRACTAL THEORY IN MARKETING: WHAT CAN WE DO?

Popis výsledku v původním jazyce

Fractal theory has emerged as a powerful mathematical tool for analyzing complex, nonlinear, and self-similar patterns across various business and engineering domains. This review explores the role of fractals in enhancing decision-making and predictive capabilities within five key application areas: consumer segmentation, demand forecasting, inventory optimization, financial market prediction, and logistics and distribution planning. We highlight how fractal-based methods - such as fractal dimension, multifractal analysis, and entropy measures - can be integrated with machine learning to improve pattern recognition, uncertainty quantification, and system adaptability. Specific attention is given to explainability, data granularity, and the synergy between fractal modeling and AI frameworks. Key challenges and limitations, including model interpretability and computational complexity, are also discussed, along with future research directions aimed at making fractal analytics more actionable in business environments.

Název v anglickém jazyce

THE APPLICATION OF FRACTAL THEORY IN MARKETING: WHAT CAN WE DO?

Popis výsledku anglicky

Fractal theory has emerged as a powerful mathematical tool for analyzing complex, nonlinear, and self-similar patterns across various business and engineering domains. This review explores the role of fractals in enhancing decision-making and predictive capabilities within five key application areas: consumer segmentation, demand forecasting, inventory optimization, financial market prediction, and logistics and distribution planning. We highlight how fractal-based methods - such as fractal dimension, multifractal analysis, and entropy measures - can be integrated with machine learning to improve pattern recognition, uncertainty quantification, and system adaptability. Specific attention is given to explainability, data granularity, and the synergy between fractal modeling and AI frameworks. Key challenges and limitations, including model interpretability and computational complexity, are also discussed, along with future research directions aimed at making fractal analytics more actionable in business environments.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY

ISSN

0218-348X

e-ISSN

1793-6543

Svazek periodika

33

Číslo periodika v rámci svazku

07

Stát vydavatele periodika

SG - Singapurská republika

Počet stran výsledku

12

Strana od-do

nestránkováno

Kód UT WoS článku

001520189700001

EID výsledku v databázi Scopus

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