Relevant bibliographies by topics / AI-driven marketing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'AI-driven marketing'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "AI-driven marketing"

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Mursalin, Adi, Yuli Purbaningsih, Surya Fadjar Boediman, Meli Siagawati, and Rainier Hendrik Sitaniapessy. "UNDERSTANDING AI-DRIVEN INFLUENCER MARKETING." INTERNATIONAL JOURNAL OF HUMANITIES, SOCIAL SCIENCES AND BUSINESS (INJOSS) 2, no. 3 (2023): 443–55. http://dx.doi.org/10.54443/injoss.v2i3.90.

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AI-driven influencer marketing has emerged as a transformative approach in the digital advertising landscape. This research aimed to explore the efficiency and effectiveness of AI integration in influencer marketing campaigns, comparing it to traditional methods. By employing a mixed-methods approach, including qualitative interviews and quantitative data analysis, the study investigated the impact of AI on influencer selection, campaign performance, and ethical considerations. The findings revealed that AI-driven influencer selection processes significantly outperformed manual methods' accura
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Baruti, Blerta Haliti, Shpresa Mehmeti Bajrami, and Arta Hoti Arifaj. "Utilising artificial intelligence in digital marketing strategy: Opportunities and challenges for marketers." Corporate and Business Strategy Review 6, no. 2 (2025): 130–36. https://doi.org/10.22495/cbsrv6i2art13.

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This study explores the integration of artificial intelligence (AI) in digital marketing, focusing on its impact on marketing practices and the evolving roles of marketers. The research aims to provide insights for professionals and academics by examining how AI-driven content marketing strategies affect consumer behavior, specifically purchase intentions. It hypothesizes that these strategies positively influence purchase intentions through brand awareness, customer retention, and engagement. Using qualitative analysis, case studies, and partial least square structural equation modeling (PLS-
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Burra, Rajitha, Tai‑Ying Wu, Kirk Chang, and Chin‑Fei Huang. "AI‑Driven Customer Positioning and Perception: Strategies, Challenges and Insights." Digital Technologies Research and Applications 4, no. 2 (2025): 96–108. https://doi.org/10.54963/dtra.v4i2.1231.

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The current research aims to investigate the transformative impact of artificial intelligence (AI) on marketing. AI‑driven marketing strategies are widely utilized in practice but their influence on customers is not always clear, leaving a glaring knowledge gap. Drawing on the Technology Acceptance Model, the current research analyses how AI‑driven marketing strategies influence customer positioning and perception, with a particular emphasis on customer trust, engagement, and relevant ethical considerations. To maximize the ecological validity of data mining and analysis, we collect multi‑laye
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Talha    , Mr Mohd. "Optimizing Digital Marketing Campaigns using Artificial Intelligence (AI) and Social Media Analytics: A Comparative Study of Machine Learning Algorithms." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42691.

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The evolution of digital marketing has been significantly influenced by advancements in Artificial Intelligence (AI) and Social Media Analytics (SMA). Businesses are leveraging AI-driven solutions to enhance customer engagement, optimize ad spending, and improve campaign effectiveness. This paper presents a comparative study of various Machine Learning (ML) algorithms applied in digital marketing campaigns. The study explores their predictive accuracy, scalability, and efficiency in processing social media data to generate actionable insights. By examining AI-driven methodologies, this researc
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Verma, Shruti, and Dr Sabeeha Fatma. "How Personalization and AI Are Transforming Digital Marketing Campaigns." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42751.

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In the rapidly evolving digital marketing landscape, personalization and artificial intelligence (AI) have emerged as transformative forces, revolutionizing how brands engage with their audiences. This paper explores the integration of AI-driven personalization in digital marketing campaigns, highlighting its impact on consumer experience, brand loyalty, and overall campaign effectiveness. AI-powered algorithms leverage vast amounts of data to analyze consumer behavior, preferences, and purchasing patterns, enabling brands to deliver highly relevant content, product recommendations, and target
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Sharma, Animesh Kumar, and Rahul Sharma. "How artificial intelligence is transforming social media marketing: Analysing its potential and addressing concerns." Journal of Cultural Marketing Strategy 9, no. 1 (2024): 75. https://doi.org/10.69554/cwqb3739.

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Artificial intelligence (AI) has revolutionised social media marketing, offering both significant advantages and potential drawbacks. This paper delves into the dual facets of AI-driven social media marketing techniques, investigating both their advantageous and detrimental impacts. It shows that by leveraging AI, businesses can achieve unprecedented levels of personalisation, engagement and efficiency in their marketing strategies, leading to enhanced customer experiences and increased brand loyalty. AI algorithms analyse vast amounts of data to identify trends and predict consumer behaviour,
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Oluwafemi, Ifeoluwa Oreofe, Tosin Clement, Oluwasanmi Segun Adanigbo, Toluwase Peter Gbenle, and Bolaji Iyanu Adekunle. "A Review of Ethical Considerations in AI-Driven Marketing Analytics: Privacy, Transparency, and Consumer Trust." International Journal of Multidisciplinary Research and Growth Evaluation 2, no. 2 (2021): 428–35. https://doi.org/10.54660/.ijmrge.2021.2.2.428-435.

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This paper provides a comprehensive review of the ethical considerations in AI-driven marketing analytics, focusing on three critical aspects: privacy, transparency, and consumer trust. As artificial intelligence (AI) technologies increasingly shape marketing strategies, concerns surrounding the use of consumer data, the transparency of algorithms, and the impact on consumer trust have gained significant attention. The paper explores the challenges and ethical dilemmas associated with AI-powered marketing tools, such as the risks to consumer privacy, the opacity of decision-making processes in
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William Yoo. "The Impact of Artificial Intelligence on Marketing Strategies." International Journal of Science and Research Archive 13, no. 1 (2024): 3211–23. http://dx.doi.org/10.30574/ijsra.2024.13.1.2042.

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The research project examines the impact of Artificial Intelligence (AI) on marketing strategies across various industries, focusing on how AI-driven technologies enhance customer engagement and optimize marketing campaigns. The objective of this study is to explore the diverse effects of AI on marketing practices and to identify the challenges and ethical considerations that arise from its implementation. The research was conducted through a qualitative methodology that conducted interviews with 18 marketing professionals to gather information about their experiences and perceptions regarding
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Xie, Lijuan. "Research on the Transformation of Enterprise Marketing Strategy Driven by Artificial Intelligence." Pacific International Journal 8, no. 2 (2025): 147–53. https://doi.org/10.55014/pij.v8i2.802.

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This study explores the transformation of enterprise marketing strategies driven by artificial intelligence (AI) through two case studies: Beijing Nanshe Technology Co., Ltd. and Juran Design Home. Both companies, operating in different sectors, have embraced AI technologies to optimize their marketing practices, enhance customer engagement, and improve operational efficiency. Nanshe Tech, a leader in smart office devices, integrated AI to develop predictive models for customer targeting and lifecycle management, leading to improved conversion rates and a shift towards a subscription-based bus
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Tyagi, Mukund, Dr Nikhil Sirohi, Dr Pallavi Tyagi, and Kajal Yadav. "Future Trends of B2C Marketing with Artificial Intelligence." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51309.

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Artificial Intelligence (AI) is fundamentally transforming business-to-consumer (B2C) marketing by enhancing the ability of brands to understand, engage, and retain customers. This paper investigates the emerging trends shaping the future of B2C marketing through the integration of advanced AI technologies. Key developments such as generative AI, conversational interfaces, emotion AI, and ethical personalization are enabling hyper-personalized content, real-time customer interactions, and deeper emotional engagement. These innovations are not only optimizing marketing strategies but also resha
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Dissertations / Theses on the topic "AI-driven marketing"

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Savola, Tommi, Tyko Tuohimaa, and Sebastian Berg. "AI-Enhanced Marketing Management – Factors Influencing Adoption in SMEs." Thesis, Högskolan i Jönköping, Internationella Handelshögskolan, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-39908.

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Recent developments and hype around artificial intelligence (AI) have arisen as result of two main factors: increase in computational power and data. Although marketing is considered as one of the main business applications within AI today, there is a lack of literature combining the disciplines. Marketing management tools, which utilise AI in supporting decision making are referred to as knowledge-driven marketing management support systems (MMSS). These systems provide besides quantitative analysis, further qualitative facets into marketing management. Despite the willingness of many SMEs to
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Books on the topic "AI-driven marketing"

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Zaidi, Neha, Mohit Maurya, Simon Grima, and Pallavi Tyagi. Building AI Driven Marketing Capabilities. Apress, 2024. http://dx.doi.org/10.1007/978-1-4842-9810-7.

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K, Hemachandran, Debdutta Choudhury, Raul Villamarin Rodriguez, Jorge A. Wise, and Revathi T. Predictive Analytics and Generative AI for Data-Driven Marketing Strategies. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003472544.

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Kilpatrick, Bruce, Pierre Kobel, and Pranvera Këllezi, eds. Antitrust in Data Driven Markets & Legal Framework for Influencers, Native Advertising and Control over the Use of AI in Marketing. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-07422-6.

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Masengu, Chiwaridzo, and Ruzive. AI-Driven Marketing Research and Data Analytics. IGI Global, 2024.

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Masengu, Chiwaridzo, and Ruzive. AI-Driven Marketing Research and Data Analytics. IGI Global, 2024.

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Masengu, Chiwaridzo, and Ruzive. AI-Driven Marketing Research and Data Analytics. IGI Global, 2024.

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Masengu, Chiwaridzo, and Ruzive. AI-Driven Marketing Research and Data Analytics. IGI Global, 2024.

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Zaidi, Neha, and Mohit Maurya. Building AI Driven Marketing Capabilities: Understand Customer Needs and Deliver Value Through AI. Apress L. P., 2024.

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Këllezi, Pranvera, Bruce Kilpatrick, and Pierre Kobel. Antitrust in Data Driven Markets and Legal Framework for Influencers, Native Advertising and Control over the Use of AI in Marketing. Springer International Publishing AG, 2022.

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Antitrust in Data Driven Markets and Legal Framework for Influencers, Native Advertising and Control over the Use of AI in Marketing. Springer International Publishing AG, 2023.

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Book chapters on the topic "AI-driven marketing"

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Lefkeli, Deniz, and Zeynep Gürhan-Canli. "AI-Driven Branding." In AI in Marketing. Routledge, 2025. https://doi.org/10.4324/9781003468806-7.

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Nguyen, Mai, Ying Chen, Thanh Huong Nguyen, Sara Shawky Habashi, Sara Quach, and Park Thaichon. "Artificial intelligence (AI)-driven services." In Artificial Intelligence for Marketing Management. Routledge, 2022. http://dx.doi.org/10.4324/9781003280392-8.

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Zaidi, Neha, Mohit Maurya, Simon Grima, and Pallavi Tyagi. "Correction to: Building AI Driven Marketing Capabilities." In Building AI Driven Marketing Capabilities. Apress, 2024. http://dx.doi.org/10.1007/978-1-4842-9810-7_17.

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Zaidi, Neha, Mohit Maurya, Simon Grima, and Pallavi Tyagi. "Applying AI for Product Life Cycle Management." In Building AI Driven Marketing Capabilities. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9810-7_2.

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Zaidi, Neha, Mohit Maurya, Simon Grima, and Pallavi Tyagi. "Analyzing Customer Satisfaction of Hotel Booking Applications: A Sentimental Analysis Approach." In Building AI Driven Marketing Capabilities. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9810-7_6.

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Zaidi, Neha, Mohit Maurya, Simon Grima, and Pallavi Tyagi. "Empowering Customer Experience with AI Tools and Technologies." In Building AI Driven Marketing Capabilities. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9810-7_3.

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Zaidi, Neha, Mohit Maurya, Simon Grima, and Pallavi Tyagi. "Using Social Media for Improving Customer Engagement." In Building AI Driven Marketing Capabilities. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9810-7_1.

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Zaidi, Neha, Mohit Maurya, Simon Grima, and Pallavi Tyagi. "Role of Artificial Intelligence for Value Chain Creation in Healthcare Marketing." In Building AI Driven Marketing Capabilities. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9810-7_8.

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Zaidi, Neha, Mohit Maurya, Simon Grima, and Pallavi Tyagi. "AI-Based Decisive Model for Customer Segmentation in the Fashion Industry." In Building AI Driven Marketing Capabilities. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9810-7_5.

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Zaidi, Neha, Mohit Maurya, Simon Grima, and Pallavi Tyagi. "Developing a Marketing Strategy While Maintaining Focus on Customer Value." In Building AI Driven Marketing Capabilities. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9810-7_14.

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Conference papers on the topic "AI-driven marketing"

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Hemalatha, Raavi, Godi Amulya, and C. H. S. N. Sai Lalitha. "AI-Driven Customer Segmentation for Personalized Marketing." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877505.

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Alshar, Moath Mahmoud, Ameet Sao, Manish Sharma, Sunil Kadyan, Vuda Sreenivasa Rao, and B. Anitha Vijayalakshmi. "Leveraging AI to Personalize HR Marketing Campaigns: A Data-Driven Approach." In 2025 3rd International Conference on Intelligent Systems, Advanced Computing and Communication (ISACC). IEEE, 2025. https://doi.org/10.1109/isacc65211.2025.10969174.

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Anute, Nilesh, Pallavi Deshpande, Priyanka Pawar, Nilesh Vitthal Limbore, Yuvraj Lahoti, and Mahendra Yadav. "Analyzing the Effectiveness of AI-driven Customer Interactions and Conversational Marketing." In 2024 Second International Conference Computational and Characterization Techniques in Engineering & Sciences (IC3TES). IEEE, 2024. https://doi.org/10.1109/ic3tes62412.2024.10877519.

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Waiker, Vinod, Malik Bader Alazzam, Sarika Keswani, Sharmeen Izzat Hassan, Anushree Aserkar, and R. Saravanakumar. "Optimizing Third-Party Product Marketing Strategies Using AI-Driven Consumer Analytics." In 2025 International Conference on Advances in Modern Age Technologies for Health and Engineering Science (AMATHE). IEEE, 2025. https://doi.org/10.1109/amathe65477.2025.11081270.

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Singh, Sakshi, and Darshan Kaur. "AI-Driven Automation in Social Media Marketing: Leveraging GANs for Content Creation." In 2024 Third International Conference on Artificial Intelligence, Computational Electronics and Communication System (AICECS). IEEE, 2024. https://doi.org/10.1109/aicecs63354.2024.10957230.

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Ganeshkumar, M., S. Karunakaran, R. Muzhumathi, and R. Suguna. "AI-Driven Marketing Insights: Harnessing Machine Learning for Enhanced Consumer Engagement and Personalization." In 2024 First International Conference on Software, Systems and Information Technology (SSITCON). IEEE, 2024. https://doi.org/10.1109/ssitcon62437.2024.10796558.

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Lekshmi, R. S., V. Sheela Mary, G. Arasuraja, V. Krishnamoorthy, S. Kaliappan, and R. Selvameena. "AI-Driven HR Optimization Strategies in Finance and Marketing: Methodological Framework and Applications." In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910901.

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Gupta, Muskan, S. Ammani, Mohan Ranga Rao Dontineni, B. R. Kumar, Monika Gupta, and Siva Koteswara Rao Katta. "The Future of Hr Marketing Ai-Driven Approaches to Talent Acquisition and Management." In 2024 International Conference on Intelligent Computing and Emerging Communication Technologies (ICEC). IEEE, 2024. https://doi.org/10.1109/icec59683.2024.10837377.

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Raj, Vijilius Helena, Shubhra, Taqi Mohammed Khattab Al-Rubaye, Dharmapuri Siri, N. Sirisha, and Amit Dutt. "Enhancing Strategic Marketing with AI-Driven Insights into Dynamic Preferences and Decision Patterns." In 2025 International Conference on Intelligent Control, Computing and Communications (IC3). IEEE, 2025. https://doi.org/10.1109/ic363308.2025.10957545.

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Eruvaram, Vamsi Krishna, Ashfaqul Hasan M.D, Sathyanathan P, Monisha R, Muthulekshmi M, and Dhineshkumar R. "IBM Watson Advertising Revolutionizes Brand Engagement By Delivering Personalized AI-Driven Marketing Experiences." In 2025 11th International Conference on Communication and Signal Processing (ICCSP). IEEE, 2025. https://doi.org/10.1109/iccsp64183.2025.11089251.

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Reports on the topic "AI-driven marketing"

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Pasupuleti, Murali Krishna. AI-Driven Marketing Innovations: Personalization and Ethics in the Digital Era. National Education Services, 2025. https://doi.org/10.62311/nesx/rr625.

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Abstract: This article explores the transformative impact of artificial intelligence (AI) on digital marketing, focusing on strategies for delivering personalized content and ensuring ethical advertising. By leveraging AI, marketers can now analyze consumer behavior with precision, enabling targeted content, automated ad placement, and real-time adjustments that enhance user engagement and conversions. The Article examines foundational AI techniques, such as recommendation engines, predictive analytics, and natural language processing, which drive personalization at scale. Additionally, it add
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Journal articles
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