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ADVANCED METHODS FOR TARGET AUDIENCE IDENTIFICATION: ENHANCING MARKETING STRATEGIES THROUGH MACHINE LEARNING AND DATA ANALYTICS.

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Název: ADVANCED METHODS FOR TARGET AUDIENCE IDENTIFICATION: ENHANCING MARKETING STRATEGIES THROUGH MACHINE LEARNING AND DATA ANALYTICS.
Alternate Title: ZAAWANSOWANE METODY IDENTYFIKACJI ODBIORCÓW DOCELOWYCH: ULEPSZANIE STRATEGII MARKETINGOWYCH POPRZEZ UCZENIE MASZYNOWE I ANALIZĘ DANYCH. (Polish)
Autoři: BOGACKI, SYLWESTER, SMUTEK, TOMASZ, CHMIELOWSKA-MARMUCKA, AGNIESZKA, RYMARCZYK, PAWEŁ, RUTKOWSKI, MAREK
Zdroj: Journal of Modern Science; 2024 Special Issue, Vol. 57, p417-435, 19p
Témata: CONSUMER behavior, DETECTION algorithms, DATA analytics, MACHINE learning, MARKETING strategy
Abstract (English): Purpose: This article presents a novel approach that leverages advanced data analytics and machine learning techniques to enhance marketing strategies. By precisely targeting and segmenting audience groups based on their descriptive profiles, the study aims to significantly improve the efficacy of marketing campaigns. Methods: The study employs several clustering and community detection algorithms, including Louvain Community, Greedy Modularity, and Label Propagation. These methods are applied to diverse datasets to identify distinct groups within the audience that exhibit specific behavioral and preference patterns. The approach emphasizes data-driven decision-making, which involves making decisions based on the analysis of data rather than intuition or observation, to optimize marketing outcomes. Results: demonstrate that employing advanced clustering techniques can significantly refine the segmentation process, leading to more targeted marketing efforts. These methods successfully identified nuanced sub-groups within the datasets, which corresponded closely with customer behaviors and preferences variations, thereby allowing for more tailored marketing strategies. Discussion: The study's findings underscore the imperative for marketers to embrace sophisticated analytical techniques. Machine learning has the potential to transform marketing strategies by providing deeper insights into customer segmentation. This research highlights the importance of staying ahead of the curve in the face of the complexities of consumer markets and evolving business environments. [ABSTRACT FROM AUTHOR]
Abstract (Polish): Cel: Artykuł bada zastosowanie zaawansowanych technik analityki danych i metod uczenia maszynowego w celu poprawy strategii marketingowych poprzez dokładne targetowanie i segmentację grup odbiorców na podstawie ich opisów profilowych. Metody: Badanie wykorzystuje kilka algorytmów klasteryzacji i wykrywania wspólnot, w tym Louvain Community, Greedy Modularity i Label Propagation. Metody te stosowane są do różnorodnych zbiorów danych, aby zidentyfikować wyraźne grupy wśród odbiorców, które wykazują specyficzne wzorce zachowań i preferencji. Podejście to kładzie nacisk na podejmowanie decyzji opartych na danych w celu optymalizacji wyników marketingowych. Wyniki: Wyniki pokazują, że zastosowanie zaawansowanych technik klasteryzacji może znacząco usprawnić proces segmentacji, prowadząc do bardziej ukierunkowanych wysiłków marketingowych. Metody te z powodzeniem zidentyfikowały subtelne sub-grupy w zbiorach danych, które ściśle odpowiadają różnicom w zachowaniach i preferencjach klientów, co pozwala na bardziej spersonalizowane strategie marketingowe. Omówienie: Wyniki podkreślają potencjał uczenia maszynowego w transformacji strategii marketingowych, dostarczając głębszych wglądów w segmentację klientów. Badanie podkreśla znaczenie przyjęcia zaawansowanych technik analitycznych, aby nadążyć za złożonościami rynków konsumenckich i ewoluującymi środowiskami biznesowymi. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Modern Science is the property of Alcide De Gasperi University of Euroregional Economy and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Databáze: Complementary Index
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Abstrakt:Purpose: This article presents a novel approach that leverages advanced data analytics and machine learning techniques to enhance marketing strategies. By precisely targeting and segmenting audience groups based on their descriptive profiles, the study aims to significantly improve the efficacy of marketing campaigns. Methods: The study employs several clustering and community detection algorithms, including Louvain Community, Greedy Modularity, and Label Propagation. These methods are applied to diverse datasets to identify distinct groups within the audience that exhibit specific behavioral and preference patterns. The approach emphasizes data-driven decision-making, which involves making decisions based on the analysis of data rather than intuition or observation, to optimize marketing outcomes. Results: demonstrate that employing advanced clustering techniques can significantly refine the segmentation process, leading to more targeted marketing efforts. These methods successfully identified nuanced sub-groups within the datasets, which corresponded closely with customer behaviors and preferences variations, thereby allowing for more tailored marketing strategies. Discussion: The study's findings underscore the imperative for marketers to embrace sophisticated analytical techniques. Machine learning has the potential to transform marketing strategies by providing deeper insights into customer segmentation. This research highlights the importance of staying ahead of the curve in the face of the complexities of consumer markets and evolving business environments. [ABSTRACT FROM AUTHOR]
ISSN:17342031
DOI:10.13166/jms/191180