The impact of cultural factors on digital marketing strategies with Machine learning and honey bee Algorithm (HBA) - Muhammad Khan; Masood Ahmad; Rakhmonov Dilshodjon Alidjonovich; Kalonov Mukhiddin Bakhritdinovich; Kurbanbekova Mohichehra Turobjonovna; Imomov Jamshidxon Odilovich (Cogent Business & Management)
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.