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Strategi Pemasaran Produk berbasis AI Analisis Efektifitas dan Hambatan dengan Mix method Khana Amelia; Agus Wibowo
MANAJEMEN Vol. 5 No. 2 (2025): Oktober : MANAJEMEN (Jurnal Ilmiah Manajemen dan Kewirausahaan)
Publisher : LPPM Politeknik Pratama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/manajemen.v5i2.1020

Abstract

In the era of digital transformation, the implementation of Artificial Intelligence (AI) in product marketing strategies has become an essential need for companies to understand consumer behavior, personalize campaigns, and improve operational efficiency. Although AI offers a competitive advantage, there are still challenges in adopting this technology, such as limited competence, high costs, and data privacy issues. This study aims to analyze the effectiveness of AI utilization in product marketing strategies and to explore the obstacles encountered through a concurrent mixed-method approach. Quantitative data were collected through Likert-scale questionnaires distributed to 150 respondents from various business sectors, while qualitative data were obtained from semi-structured interviews with 15 business practitioners who use AI. The research findings show that 82% of respondents acknowledged an increase in marketing effectiveness through AI, with an average effectiveness score of 4.2 out of 5. Content personalization (77%) and predictive analytics (70%) were identified as dominant factors that enhance customer engagement and marketing decision-making. However, 35% of respondents pointed out high initial investment costs as the main obstacle. The qualitative findings reinforce that AI provides benefits in promotional personalization, customer service automation, and data analysis accuracy. Nevertheless, its adoption remains hindered by limitations in human resources, high development costs, data privacy issues, and suboptimal internal system integration. This study concludes that AI has significant potential to enhance product marketing effectiveness; however, its successful implementation greatly depends on the company’s internal readiness and compliance with existing regulations.