Academic Journal
The impact of cultural factors on digital marketing strategies with Machine learning and honey bee Algorithm (HBA).
| Τίτλος: | The impact of cultural factors on digital marketing strategies with Machine learning and honey bee Algorithm (HBA). |
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| Συγγραφείς: | Khan, Muhammad1 (AUTHOR) muhammadkhan@awkum.edu.pk, Ahmad, Masood2 (AUTHOR), Alidjonovich, Rakhmonov Dilshodjon3 (AUTHOR), Bakhritdinovich, Kalonov Mukhiddin4 (AUTHOR), Turobjonovna, Kurbanbekova Mohichehra5 (AUTHOR), Odilovich, Imomov Jamshidxon6 (AUTHOR) |
| Πηγή: | Cogent Business & Management. Dec2025, Vol. 12 Issue 1, p1-30. 30p. |
| Θεματικοί όροι: | *Bees algorithm, *Internet marketing, *Market segmentation, *Sentiment analysis, *Marketing effectiveness, *Consumer behavior, Machine learning, Sociocultural factors |
| Γεωγραφικοί όροι: | Pakistan |
| Περίληψη: | This paper analyses the impact of cultural factors on digital marketing strategies in Pakistan. Improvement of machine learning (ML) techniques combined with the Honey Bee Algorithm (HBA) has been incorporated for better solutions. Cultural differences play a vital role as consumers behave differently based on some cultural differences, demanding sensitive marketing strategies. Experimental results demonstrate that machine learning models effectively capture cultural preferences, and HBA significantly enhances marketing effectiveness, leading to a 20% increase in engagement and a 15% improvement in click-through rates (CTR). Natural Language Processing (NLP) methods are used to gain cultural insights from consumer data, while clustering algorithms segment the market based on these factors. Predictive models are applied to understand consumer behavior patterns, and HBA is utilized to optimize key marketing parameters, including content personalization and ad placement. Compared to traditional marketing approaches, our data-driven methodology results in a 25% overall improvement in consumer interactions and campaign effectiveness. The proposed framework is validated through extensive experimentation, incorporating culturally tailored A/B testing, religion-related holidays' optimization, and personalized segmentation. These findings underscore the importance of integrating cultural insights into digital marketing strategies to enhance consumer engagement and optimize campaign performance. [ABSTRACT FROM AUTHOR] |
| Copyright of Cogent Business & Management is the property of Taylor & Francis Ltd 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.) | |
| Βάση Δεδομένων: | Business Source Index |
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| Items | – Name: Title Label: Title Group: Ti Data: The impact of cultural factors on digital marketing strategies with Machine learning and honey bee Algorithm (HBA). – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Khan%2C+Muhammad%22">Khan, Muhammad</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> muhammadkhan@awkum.edu.pk</i><br /><searchLink fieldCode="AR" term="%22Ahmad%2C+Masood%22">Ahmad, Masood</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Alidjonovich%2C+Rakhmonov+Dilshodjon%22">Alidjonovich, Rakhmonov Dilshodjon</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Bakhritdinovich%2C+Kalonov+Mukhiddin%22">Bakhritdinovich, Kalonov Mukhiddin</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Turobjonovna%2C+Kurbanbekova+Mohichehra%22">Turobjonovna, Kurbanbekova Mohichehra</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Odilovich%2C+Imomov+Jamshidxon%22">Odilovich, Imomov Jamshidxon</searchLink><relatesTo>6</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Cogent+Business+%26+Management%22">Cogent Business & Management</searchLink>. Dec2025, Vol. 12 Issue 1, p1-30. 30p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Bees+algorithm%22">Bees algorithm</searchLink><br />*<searchLink fieldCode="DE" term="%22Internet+marketing%22">Internet marketing</searchLink><br />*<searchLink fieldCode="DE" term="%22Market+segmentation%22">Market segmentation</searchLink><br />*<searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Marketing+effectiveness%22">Marketing effectiveness</searchLink><br />*<searchLink fieldCode="DE" term="%22Consumer+behavior%22">Consumer behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Sociocultural+factors%22">Sociocultural factors</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Pakistan%22">Pakistan</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper analyses the impact of cultural factors on digital marketing strategies in Pakistan. Improvement of machine learning (ML) techniques combined with the Honey Bee Algorithm (HBA) has been incorporated for better solutions. Cultural differences play a vital role as consumers behave differently based on some cultural differences, demanding sensitive marketing strategies. Experimental results demonstrate that machine learning models effectively capture cultural preferences, and HBA significantly enhances marketing effectiveness, leading to a 20% increase in engagement and a 15% improvement in click-through rates (CTR). Natural Language Processing (NLP) methods are used to gain cultural insights from consumer data, while clustering algorithms segment the market based on these factors. Predictive models are applied to understand consumer behavior patterns, and HBA is utilized to optimize key marketing parameters, including content personalization and ad placement. Compared to traditional marketing approaches, our data-driven methodology results in a 25% overall improvement in consumer interactions and campaign effectiveness. The proposed framework is validated through extensive experimentation, incorporating culturally tailored A/B testing, religion-related holidays' optimization, and personalized segmentation. These findings underscore the importance of integrating cultural insights into digital marketing strategies to enhance consumer engagement and optimize campaign performance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Cogent Business & Management is the property of Taylor & Francis Ltd 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.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/23311975.2025.2486590 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 30 StartPage: 1 Subjects: – SubjectFull: Bees algorithm Type: general – SubjectFull: Internet marketing Type: general – SubjectFull: Market segmentation Type: general – SubjectFull: Sentiment analysis Type: general – SubjectFull: Marketing effectiveness Type: general – SubjectFull: Consumer behavior Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Sociocultural factors Type: general – SubjectFull: Pakistan Type: general Titles: – TitleFull: The impact of cultural factors on digital marketing strategies with Machine learning and honey bee Algorithm (HBA). Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Khan, Muhammad – PersonEntity: Name: NameFull: Ahmad, Masood – PersonEntity: Name: NameFull: Alidjonovich, Rakhmonov Dilshodjon – PersonEntity: Name: NameFull: Bakhritdinovich, Kalonov Mukhiddin – PersonEntity: Name: NameFull: Turobjonovna, Kurbanbekova Mohichehra – PersonEntity: Name: NameFull: Odilovich, Imomov Jamshidxon IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 23311975 Numbering: – Type: volume Value: 12 – Type: issue Value: 1 Titles: – TitleFull: Cogent Business & Management Type: main |
| ResultId | 1 |