# Artificial Intelligence in E-commerce and Digital Marketing: A Systematic Review of Opportunities, Challenges, and Ethical Implications

Redeer Avdal Saleh, Subhi R M Zeebaree

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Redeer Avdal Saleh, Subhi R M Zeebaree. Artificial Intelligence in E-commerce and Digital Marketing: A Systematic Review of Opportunities, Challenges, and Ethical Implications. Asian Journal of Research in Computer Science, 2025, 18, pp.395 - 410. 10.9734/ajrcos/2025/v18i3601. hal-04987109

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# Artificial Intelligence in E-commerce and Digital Marketing: A Systematic Review of Opportunities, Challenges, and Ethical Implications

Redeer Avdal Saleh a* and Subhi R. M. Zeebaree b

## Authors' contributions

This work was carried out in collaboration between both authors. Both authors read and approved the final manuscript.

## Article Information

DOI: https://doi.org/10.9734/ajrcos/2025/v18i3601

**Open Peer Review History:**

This journal follows the Advanced Open Peer Review policy. Identity of the Reviewers, Editor(s) and additional Reviewers, peer review comments, different versions of the manuscript, comments of the editors, etc are available here: https://pr.sdiarticle5.com/review-history/131968

Received: 18/12/2024

Accepted: 22/02/2025

Published: 28/02/2025

## Systematic Review Article

## ABSTRACT

The transformative power of AI has only just begun to redefine how businesses function and relate to their customers within e-commerce and digital marketing. In fact, AI really does help firms adjust to changes in consumer preference and market fluctuations by improving operational efficiencies. Big data analytics, aided by artificial intelligence, really boosts the understanding of the customer journey-hence, optimizing and finally allowing for tailor-made marketing campaigns in real time. This leads to great growth for the business. The COVID-19 pandemic pushed companies into adopting AI-driven solutions in the quest for their resilience; this consequently led to an increase in the need for effective digital marketing strategies. E-commerce activities are integrated with

<footnote>a IT Department, College of Informatics, Akre University for Applied Sciences, Duhok, Iraq.</footnote>

<footnote>b Department of Energy Engineer, Technical College of Engineering, Duhok Polytechnic University, Duhok, Iraq.</footnote>

<footnote>*Corresponding author: E-mail: redeer.avdal@auas.edu.krd</footnote>
artificial intelligence in order to better understand consumer behavior, support market dynamics forecasting, and enhance risk management strategies. Hence, it becomes an indispensable aspect. It is relevant that ethical frameworks and further research address the problems of data privacy and scalability in order to optimize the intrinsic potential of AI. A focus on innovative applications of AI, alongside interdisciplinary collaboration, can empower an organization to develop genuinely inclusive and effective marketing strategies. Embracing the AI-driven initiatives, this will result in long-term relationship building with the customer for growth in a sustainable manner and maintaining competitiveness at an exponential pace in changing digitization.

Keywords: Artificial intelligence; digital marketing; marketing strategies; e-commerce; business.

# 1. INTRODUCTION

The COVID-19 pandemic has acted as a catalyst for a massive transformation in business operations, underpinning the critical need for digital progress in marketing to keep up with competitive advantage (Limna et al., 2022; Majedkan et al., 2020). Artificial intelligence-the replication of human cognitive functions within machines-induces big changes in e-commerce because of the possibility of data-driven decision-making (Sakhvidi & Saadat, 2024). It is of paramount importance in the development of marketing methodologies; hence, there is a need for comprehensive evaluations that chart the landscape of AI research in this area (Mariani et al., 2022). E-commerce rapid growth has changed business-consumer interactions, which therefore require big data analytics and AI to drive actionable insights in order to enhance customer journeys and operational efficiency (Nalla et al., 2024). Integrating AI with big data analytics really provides real-time insights, predictive analytics, and personalized recommendations, which substantially enhances customer experiences (Amelia et al., 2023). Although e-commerce has experienced tremendous growth, the existing literature remains quite fragmented, which further complicates an already challenging task: identifying precisely where the research gaps are located (Bawack et al., 2022). Globally, AI has become a part of business operations in transforming AI-driven automation into emulating human intelligence to make informed marketing decisions (Haleem et al., 2022). AI has emerged as a transformation force in digital marketing, making it very important for stakeholders to understand its implications (Ziakis & Vlachopoulou, 2023). AI allows for innovative marketing strategies and practices; hence, companies have to seek effective AI solutions (Verma et al., 2021). This shift to digital platforms makes the approach of CRM and sentiment analysis crucial in devising any marketing

strategy (Kyaw et al., 2022). Technological changes are an uphill task for effective marketing strategies that will lead to better sales conversion and keeping competitive advantages (Purnomo, 2023). AI-driven customer interaction and brand engagement shapes the future of the fashion sector (Rathore, 2023). Recent advancements in e-commerce like S-Commerce and Sharing Commerce need proper research as most of the gaps exist in current literature (Attar et al., 2022). Global poverty, increased by conflicts, climate change, and the pandemic, needs new approaches that emphasize e-commerce and the collaboration of SMEs (Cordes & Marinova, n.d.). Fast-growing research in digital marketing and technology underlines that an integrated perspective on the impact of AI is imperative (Vlačić et al., 2021). The relationship between digital marketing strategies and consumer online purchasing behavior has to be considered in a wide context with regard to their effects (Antczak, 2024). AI restructures business-consumer relationships through accurate marketing strategies run by predictive models (Vidhya, 2023). Emphasizing the significance of digitalization in e-commerce, AI technologies enhance marketing strategies and decision-making processes (Apriani et al., 2024; Qurtubi et al., 2022). This technology employs techniques from machine learning to analyze facial data, enabling applications in industries like forensics and biometrics (Abdulrazaq et al., 2021). It also highlights the importance of integrating artificial intelligence (AI) with IoT for efficient resource management and real-time responses, calling for further research to explore new advancements (Armya et al., 2023). AI facilitates a more nuanced understanding of document relationships, leading to better grouping of documents based on meaning rather than just keywords (Haji et al., 2023). AI is a transformative technology that enhances efficiency and sustainability by processing complex data and automating tasks (Issa et al., 2024; Yasin, 2025). Introduces the concept of AI
and its relevance to marketing, focusing on Chat GPT as a tool for enhancing content strategies (Saputra et al., 2023).

## 2. BACKGROUND THEORY

Digital marketing has evolved with the integration of AI and ICT, significantly improving e-commerce efficiency through automated processes and personalized customer interactions. AI and big data analytics tailor customer experiences and enhance business outcomes. Predictive AI and ML technologies anticipate consumer trends, while disruptive AI technologies drive continuous innovation in marketing strategies.

### 2.1 Digital Marketing and ICT

The theoretical framework of digital marketing and service marketing underlines the role of information and communication technology (ICT). In fact, ICT has considerably changed business practices-before, during, and post-pandemic times-including enabling companies to readjust to new market realities and changing consumer behaviors (Limna et al., 2022).

### 2.2 AI Principles and Cognitive Processes

AI's ability to simulate intelligent processes and massive data processing capabilities is at the core. AI improves operational efficiency in many different sectors, e-commerce included, through automated processes, personalized customer interactions, and better market trend predictions (Sakhvidi & Saadat, 2024).

### 2.3 Technology Acceptance and Consumer Behavior

Discuss the foundational theories relevant to AI applications in marketing, including technology acceptance and the other psychological frameworks that help in understanding and influencing consumer behavior-integral in creating effective digital marketing strategies (Mariani et al., 2022).

### 2.4 Big Data and Analytics

Principles of AI and big data analytics. Combining these technologies can create tailored customer experiences and improve organizational outcomes. In e-commerce, huge

volumes of generated data demand advanced analytics to tap into it for driving business directions with informed decisions (Nalla et al., 2024).

## 2.5 Big Data and Customer Journey Optimization

Big data and AI technologies are functioning together to optimize the customer journey in e-commerce. In this regard, particular AI techniques applied can contribute to a better level of customer satisfaction and loyalty, rendering personalized experiences and timely recommendations (Amelia et al., 2023).

## 2.6 Predictive Analytics and Consumer Behavior

Artificial Intelligence (AI) and Machine Learning (ML) technologies increase the understanding and prediction of customer behavior through the use of data. These technologies encourage the development of predictive models that can predict consumer needs and trends (Haleem et al., 2022).

## 2.7 AI Applications and Marketing Strategies

A theoretical framework allows the understanding of the interaction between applications of artificial intelligence and marketing strategies. Data-informed decision-making and immediate analytical capabilities are among the major principles in formulating successful marketing initiatives (Ziakis & Vlachopoulou, 2023).

## 2.8 Disruptive Technologies in Marketing

Discuss how disruptive technologies like AI impact business operations and customer expectations. The evolving nature of these technologies drives continuous innovation and adaptation in marketing strategies (Verma et al., 2021).

## 3. LITERATURE REVIEW

Singh, (2021), discussed the ways in which Artificial Intelligence (AI) and Machine Learning (ML) impact e-commerce in terms of improving the experiences of customers and raising operational efficiency. AI will enhance customer relationship management, logistics, and marketing, which will result in more purchases
and increased customer satisfaction. AI systems bring forth concerns of privacy and ethics in using the data. A holistic view must balance the benefits and drawbacks of artificial intelligence in e-commerce.

Sohaib et al., (2022), tried to find the importance of SMMAs in the e-commerce context of China using the stimulus-organism-response (S-O-R) framework. The findings show that effective SMMAs lead positively to improved relationship quality, which consecutively results in more consumer loyalty and repurchase intention. More importantly, this study they indicated the possibility of improvement in both AI-driven customized marketing strategies and data analysis for better SMMAs to improve organizational performance in the e-commerce business.

Singh et al., (2024), examined online shopping assistant adoption in e-commerce, with a view toward the role that AI plays in personalizing customer shopping experiences. OSAs can enhance the shopping experience through natural language processing and providing tailored suggestions to answer customer queries. Various factors, such as trust in and ease of use and privacy, influence consumers' acceptance of OSAs in their digital marketing strategies. This shows how AI could help increase customer satisfaction and build an ongoing relationship with customers in e-commerce.

Reddy et al., (2024), investigated the influence of big data and AI on e-commerce marketing, underlining that these technologies allow companies to address each customer with adaptability based on their behavior. AI-driven algorithms support companies in analyzing large data, identifying complicated patterns, and in real time, delivering relevant content that increases engagement and conversion rates. Proper personalization enhances customer satisfaction and loyalty, which means business success within a highly competitive landscape. Ethical implications and privacy issues related to the process of data gathering are further discussed, putting emphasis on having a balanced approach toward building consumer confidence.

Goriparthi, (2020), discussed the role that artificial intelligence may play in the optimization of e-commerce through personalized recommendation based on big data analysis. It underlines the effectiveness of AI-driven recommendation frameworks since they result in

huge gains with respect to precision, interactions with users, and conversion rates. This research also underlines important ethical issues of data privacy and algorithmic bias while applying AI technologies to foster customer loyalty and drive sales expansion in a competitive landscape.

Khrais, (2020), discussed the role of artificial intelligence in e-commerce and digital marketing, covering its efficiency and quality. AI helps businesses understand customer preferences, which enables them to create hyper-personalized marketing strategies. Explainable AI guarantees transparency in the decision-making processes driven by AI and enhances the level of customer trust and satisfaction. Despite the loss of jobs, the deeper contributions AI makes in smoothing operations and improving interactions with consumers place it at the forefront of change in the growth of the digital marketing field in e-commerce.

Mahapatra et al., (2022), investigated the effect of AI on customer behavior in e-commerce and digital marketing. This research underlines those areas of change brought about by digital developments, more so the integration of AI and machine learning technologies. Similarly, the study tries to explain how AI enhances the customer experience through personalized recommendations, targeted advertising, and seamless shopping experiences. This paper concluded that AI enhances the effectiveness of digital marketing strategies by better understanding and anticipating customer needs for a more engaging and efficient online shopping environment.

Putha et al., (2024), investigated the impact of AI-driven personalization in e-commerce, underlining how advanced data analytics and machine-learning models have transformed the landscape of digital marketing strategies. The discussion spans methodologies such as collaborative filtering and content-based approaches, which enhance consumer experiences and stimulate sales. Empirical case studies from prominent platforms demonstrate practical applications, yielding quantifiable enhancements in customer satisfaction and organizational performance. Furthermore, the manuscript examines obstacles such as data privacy concerns and algorithmic bias, proposing future directions for AI ethics and technological integration to optimize personalization techniques within the digital marketplace.
Cao, (2021), investigated the significance of artificial intelligence in e-commerce and digital marketing. This study depicts that AI enhances customer service through intelligent robotics, fastens the processing speed, and analyzes consumer behavior in order to develop personalized shopping. Moreover, through intelligent store design, it automates the product showcasing, hence increasing efficiency. Adaptive pricing models and targeted marketing campaigns are also supported by AI, which ultimately drives engagement and fosters loyalty among customers in the new e-commerce landscape.

Rusthollkarhu et al., (2022), reviewed the role of artificial intelligence in enhancing B2B customer journey management in e-commerce and digital marketing, highlighting four key management areas: analyze, design, engage, and guide. AI-empowered tools can enhance these activities at pre-purchase, purchase, and post-purchase stages. AI functions drive managerial decisions, efficiency in operations, insight into data, simplification of tasks, and collaboration between humans and machines. The results provide actionable insights for marketers on the application of artificial intelligence tools to improve customer engagement and satisfaction, hence informing B2B marketing management.

Tolstoy et al., (2022), discussed the determinants of international performance for e-commerce SMEs and highlights the role of OMCs in achieving success. It stresses the necessity of marketing ambidexterity: a balance between market-driven and market-driving approaches. The study also shows how digital tools, including AI, may help improve customer engagement, optimize business processes, and adapt marketing strategies in light of the competitive digital landscape. The authors note that SMEs have to continually develop OMCs in order to be able to succeed internationally.

Chen et al., (2022), examined the role of Artificial Intelligence in e-commerce firms' performance by exploring how AI capabilities contribute to firm performance through dimensions related to creativity, artificial intelligence management (AIM), and AI-driven decision making (AIDDM). Identifying through a resource-based view framework, the study explained how firms use AI for better customer service through enhanced product recommendations and increased operational efficiencies. The findings are presenting the critical role that artificial

intelligence plays in analyzing consumer data, predicting market trends, and developing digital marketing strategies that have contributed to better business outcomes and competitive advantages in the e-commerce industry.

Sabharwal et al., (2022), investigated the link between digital advertising (DA) and artificial intelligence (AI), and their joint effect on marketing strategy. It highlighted how AI, through data collection and analysis of consumer behavior, enhances e-commerce and digital marketing by predicting customer behavior and improving engagement. AI technologies allow for real-time solutions, enabling organizations to respond swiftly to market demands and thus gain a competitive advantage. The results revealed that the successful incorporation of artificial intelligence (AI) into marketing approaches not only improves operational effectiveness but also enhances the processes of acquiring and retaining customers, thereby increasing profitability within an ever-evolving digital environment.

Andrzejak, (2023), examined the application of artificial intelligence (AI) in advancing e-commerce and digital marketing, with a particular emphasis on the Polish market. It highlights the potential of AI-powered tools, including machine learning and natural language processing, for bringing efficiency in marketing and user experiences and optimizing online advertising. Challenges have also been pointed out in skilled IT personnel and the management of customer interactions through AI-driven systems like chatbots. Still, this integration of AI in digital marketing is perceived as transformative-bringing operational improvements and economic gains for businesses.

Yaiprasert & Hidayanto, (2023), used ensemble machine learning techniques to improve digital marketing strategies in the food delivery sector. The research applied artificial intelligence to analyze data coming from customers in terms of their preference and behavior prediction. Methodologically, the combination of decision trees, naïve Bayes, and the nearest neighbor algorithm showed a high accuracy of prediction. The results show that integrating AI-driven models increases marketing efficiency by automating tasks such as customer segmentation and content optimization, which aligns marketing strategies with customer needs. This demonstrates the transformative potential of AI in e-commerce and digital marketing.
Hindarto, (2023), discussed the transformative effects experienced in business dynamics brought about by the integration of e-commerce, underlining the integration of advanced technologies like artificial intelligence in enhancing operations and digital marketing strategies. Al helps in optimizing sales strategies, managing customer data in a better way, and providing personalized marketing campaigns that influence consumer preferences. It allows organizations to analyze customer information and understand purchasing behavior in order to drive sales in the competitive e-commerce market. Further, Al simplifies the procedures of transactions and enhances customer experiences, making customers more loyal and satisfied.

Tang et al., (2023), examined the use of artificial intelligence in improving inventory forecasting in cross-border e-commerce. They find that AI methodologies, particularly Extreme Gradient Boosting (XGBoost), outperform traditional inventory forecasting methods in terms of accuracy and practical efficiency. With this technological improvement, companies can regulate their inventory, reduce waste, and coordinate marketing strategies to fit consumer preferences. The findings of the study substantiate the Logistics 4.0 movement and call for e-commerce enterprises to implement advanced technological innovation in order to maintain competitiveness in the fast-changing marketplace.

Wang et al., (2023), appraised the Technology Acceptance Model for the role of Artificial Intelligence in e-commerce and digital marketing. It flags how AI has transformed online shopping through better customer engagement, personalization of experiences, and facilitation of business processes. Technologies such as chatbots and recommendation systems enhance efficiency, reduce search times, and predict market demands. The role of AI in improving service quality and competitiveness also lies in catering to evolving customer preferences, especially during the COVID-19 pandemic.

Hicham et al., (2023), discussed the critical role that artificial intelligence plays in e-commerce and digital marketing, specifically its ability to analyze consumer behavior, enhance customer experiences, and improve marketing strategies. The ability of AI to process large amounts of data enables it to target more precisely and create tailored campaigns. Additionally, the integration

of AI technologies like predictive analytics, chatbots, and automated content generation increases the efficiency of marketing activities. It also, however, discusses some data privacy problems and other ethical issues involving human supervision and strict adherence to the data protection laws.

Alqurashi et al., (2023), the role played by Artificial Intelligence in augmenting e-commerce and digital marketing. It demonstrates its ability in increasing personalized content marketing; a study of 485 participants confirmed that using AI increased customer engagement and satisfaction. The use of artificial intelligence in data analysis has allowed organizations to understand consumer preferences, predict behaviors, and design tailored marketing strategies-thereby providing them with a competitive advantage. This manuscript highlights the importance of strategic integration of AI channels and resource allocation to AI capabilities in improving marketing strategies in the digital landscape.

Udayana et al., (2024), examined the impact of digital marketing and e-commerce on the performance and sustainability of small and medium enterprises (SMEs) in Indonesia. The findings indicated that both approaches notably enhanced marketing efficacy and the sustainability of businesses. E-commerce emerged as a vital component in facilitating communication and marketing efforts, with the cost-effectiveness and quantifiable effects of digital marketing strategies being apparent. Furthermore, the research proposed that artificial intelligence could bolster e-commerce by allowing for personalized marketing, enhancing customer interaction, and streamlining inventory management, thereby assisting small and medium-sized enterprises in their endeavors towards digital transformation.

## 4. DISCUSSION AND COMPARISON

A thorough review of the literature examining the impact that artificial intelligence has on e-commerce and digital marketing identified some of the major themes relating to opportunities and challenges of adopting AI technologies. A big finding from the research was that artificial intelligence can greatly contribute to improved customer experiences and satisfaction. Much of the research has put a strong emphasis on how AI-enabled customer relationship management and personalized marketing strategies are likely to bring increased engagement and satisfaction
<table><caption>Table 1. Summary of the literature review on details</caption><thead><tr><td>#</td><td>Study</td><td>Focus Area</td><td>Key AI Techniques</td><td>Findings</td><td>Advantages</td><td>Disadvantages</td><td>Ethical Considerations</td></tr></thead><tbody><tr><td>1</td><td>Singh, (2021)</td><td>AI transforming e-commerce</td><td>Machine learning, CRM optimization</td><td>Enhanced operational efficiency and marketing precision.</td><td>Improved satisfaction, better logistics</td><td>Data misuse</td><td>Balancing benefits and risks in AI applications</td></tr><tr><td>2</td><td>Sohaib et al., (2022)</td><td>Social media marketing activities (SMMAs) in China's e-commerce</td><td>Tailored marketing, data analytics</td><td>Efficient SMMAs enhance relationship quality, boosting loyalty and repurchase intentions.</td><td>Improved consumer loyalty, better business performance</td><td>Dependence on technology, data accuracy challenges</td><td>Privacy concerns, responsible AI deployment</td></tr><tr><td>3</td><td>Singh et al., (2024)</td><td>Online shopping assistants</td><td>NLP, AI recommendation systems</td><td>Enhanced interactions and acceptance of AI-driven assistants.</td><td>Tailored recommendations, better customer trust</td><td>Data sensitivity issues</td><td>Trust, ease of use, and privacy concerns</td></tr><tr><td>4</td><td>Reddy et al., (2024)</td><td>AI in personalization</td><td>AI-driven algorithms</td><td>Improved engagement and real-time marketing relevance.</td><td>Higher conversion rates, consumer loyalty</td><td>Privacy issues</td><td>Ethical balance in personalization practices</td></tr><tr><td>5</td><td>Goriparthi, (2020)</td><td>Personalized e-commerce recommendations</td><td>Collaborative filtering, content-based filtering</td><td>AI-enhanced systems outperform traditional methods, increasing accuracy and engagement.</td><td>Improved user engagement, higher conversion rates</td><td>Potential data misuse</td><td>Data privacy, algorithmic bias</td></tr><tr><td>6</td><td>Khrais, (2020)</td><td>AI in e-commerce and digital marketing</td><td>Explainable AI (XAI)</td><td>XAI builds transparency, improving customer trust while enabling hyper-personalized strategies.</td><td>Increased trust, personalized marketing</td><td>Job displacement</td><td>Transparency in decision-making processes</td></tr><tr><td>7</td><td>Mahapatra et al., (2022)</td><td>AI impact on customer</td><td>Personalized recommendations</td><td>Improved targeting and customer</td><td>Effective marketing strategies, seamless</td><td>Requires substantial data</td><td>Ethical data collection and</td></tr></tbody></table>
<table><thead><tr><td>#</td><td>Study</td><td>Focus Area</td><td>Key AI Techniques</td><td>Findings</td><td>Advantages</td><td>Disadvantages</td><td>Ethical Considerations</td></tr></thead><tbody><tr><td></td><td></td><td>behavior</td><td></td><td>satisfaction on platforms like Amazon and Flipkart.</td><td>shopping</td><td>infrastructure</td><td>usage</td></tr><tr><td>8</td><td>Putha et al., (2024)</td><td>AI-driven personalization in e-commerce</td><td>Collaborative filtering, content-based methods</td><td>AI improves recommendations and enhances customer satisfaction.</td><td>Measurable improvements in business performance</td><td>Algorithmic bias</td><td>Data privacy, ethical AI deployment</td></tr><tr><td>9</td><td>Cao, (2021)</td><td>AI in customer service and store design</td><td>Intelligent robots, recommendation engines</td><td>Streamlined shopping experiences and enhanced engagement.</td><td>Operational efficiency, dynamic pricing</td><td>Resource-intensive</td><td>Managing algorithmic fairness</td></tr><tr><td>10</td><td>Rusthollkarhu et al., (2022)</td><td>B2B customer journey management</td><td>AI tools for journey phases</td><td>Enhanced decision-making and efficiency in B2B marketing.</td><td>Automation, actionable insights</td><td>Skill gap for AI tools implementation</td><td>Ethical use in human-AI collaboration</td></tr><tr><td>11</td><td>Tolstoy et al., (2022)</td><td>SME online marketing capabilities</td><td>Ambidextrous marketing</td><td>Highlighted the role of tech for engagement and process optimization.</td><td>Competitive advantage, process refinement</td><td>Technological dependency</td><td>Need for continuous skill upgrading</td></tr><tr><td>12</td><td>Chen et al., (2022)</td><td>AI in firm performance</td><td>AI management, decision-making</td><td>Enhanced customer service and operational efficiency.</td><td>Competitive advantage, market trend prediction</td><td>High dependency on AI technologies</td><td>Data protection, fair AI management practices</td></tr><tr><td>13</td><td>Sabharwal et al., (2022)</td><td>AI and digital advertising</td><td>Real-time data analysis</td><td>Improved customer acquisition and engagement.</td><td>Profitability, quick market adaptation</td><td>Risk of over-targeting consumers</td><td>Privacy concerns, ethical marketing</td></tr><tr><td>14</td><td>Andrzejak, (2023)</td><td>AI in Polish e-commerce</td><td>Machine learning, NLP</td><td>Improved efficiency and competitive advantages for businesses.</td><td>Cost reduction, intelligent automation</td><td>Skilled workforce requirement</td><td>Customer interaction and bias management</td></tr><tr><td>15</td><td>Yaiprasert &amp; Hidayanto, (2023)</td><td>AI in food delivery marketing strategies</td><td>Ensemble machine learning</td><td>High accuracy in customer predictions, better segmentation.</td><td>Efficient marketing campaigns, improved engagement</td><td>Resource-intensive</td><td>Ethical AI deployment, privacy concerns</td></tr></tbody></table>
<table><thead><tr><td>#</td><td>Study</td><td>Focus Area</td><td>Key AI Techniques</td><td>Findings</td><td>Advantages</td><td>Disadvantages</td><td>Ethical Considerations</td></tr></thead><tbody><tr><td>16</td><td>Hindarto, (2023)</td><td>E-commerce optimization</td><td>Personalized marketing campaigns</td><td>Effective targeting, improved sales growth.</td><td>Enhanced loyalty, simplified processes</td><td>Transaction process dependencies</td><td>Adherence to privacy regulations</td></tr><tr><td>17</td><td>Tang et al., (2023)</td><td>AI in inventory forecasting</td><td>XGBoost</td><td>Improved inventory accuracy, reduced waste.</td><td>Efficient logistics, alignment with consumer demand</td><td>Over-reliance on AI</td><td>Transparency in AI-driven strategies</td></tr><tr><td>18</td><td>Wang et al., (2023)</td><td>AI and consumer behavior</td><td>Chatbots, recommendation systems</td><td>Enhanced efficiency and consumer engagement.</td><td>Better shopping experience, reduced search times</td><td>Ethical dilemmas in AI usage</td><td>Data handling and consumer trust issues</td></tr><tr><td>19</td><td>Hicham et al., (2023)</td><td>AI in consumer behavior and marketing</td><td>Predictive analytics, chatbots</td><td>Improved targeting and marketing efficiency.</td><td>Real-time campaign adjustments</td><td>Algorithmic fairness issues</td><td>Regulation compliance, human oversight in AI</td></tr><tr><td>20</td><td>Alqurashi et al., (2023)</td><td>AI in personalized content marketing</td><td>Data analysis, AI-driven prediction</td><td>Improved engagement, satisfaction, and competitive advantage.</td><td>Enhanced strategies, deeper consumer insights</td><td>Investment in AI technologies</td><td>Ethical AI use, trust-building</td></tr><tr><td>21</td><td>Udayana et al., (2024)</td><td>Digital marketing for SMEs</td><td>Not specified explicitly</td><td>Positive impact on marketing and sustainability</td><td>Affordable, impactful strategies</td><td>Limited focus on AI implications</td><td>Inferred role of AI in e-commerce</td></tr></tbody></table>
rates. Similarly, other studies proved the positive impact artificial intelligence has on customer engagement by way of tailor-made content marketing. Personalization was one of the recurring themes, demonstrating the effectiveness of artificial intelligence in delivering personalized shopping experiences and relevant content to customers. This ability to personalize interactions has been instrumental in improving conversion rates and fostering customer loyalty. Another important finding related to the application of artificial intelligence in improving operational efficiency. Various studies showed that AI methods, including Extreme Gradient Boosting (XGBoost), outperformed traditional inventory forecasting methods, which helped improve inventory management and reduce waste. Similarly, improvement in operational efficiency by analyzing real-time data and quickly adapting to market demands was illustrated. However, the integration of artificial intelligence is also associated with various challenges, mostly related to data privacy and ethical concerns. Several research studies have raised these challenges and urged ethical practices in AI and the strictest adherence to data protection rules. What is more, the risks of algorithmic bias and the need for explainable artificial intelligence were underlined-referring to the necessity of transparency and human oversight in decision-making supported by AI. It also underlined the requirement of professional IT staff and challenges in AI-driven systems. The review pointed out that effective human-AI collaboration could be a way to maximize the benefits of AI technologies in e-commerce and digital marketing. In general, these papers outline that AI is the transformative force in the digital landscape, enabling sweeping competitive advantages to be realized by way of enhanced customer experiences, personalized marketing, and operational efficiencies. They also emphasize the critical need to address ethical and privacy concerns to ensure responsible and sustainable AI integration.

## 5. EXTRACTED STATISTICS

The discussed focus areas in the studies have identified several aspects of AI in e-commerce and related fields. The most frequently quoted one is personal recommendations: 10 references emphasize the use of AI in tailoring shopping experiences for each individual consumer. Digital marketing strategies, at 9 mentions, also center on the integration of AI into campaign optimization and targeted marketing. Customer

engagement was mentioned 8 times, with a focus on the role of AI in improving interaction and retention. Conversion rates, in 6 studies, looked at how AI helps to increase sales, while ethical considerations-also 6 in number-looked at data privacy, algorithmic bias, and the ethical use of AI. Customer satisfaction, mentioned 5 times, focused on how the experiences of users are enhanced through personalized and efficient services. AI in relation to inventory management was discussed in 3 mentions, though less frequently. Explainable AI and dynamic pricing, each mentioned twice, referred to the transparency of decision-making processes and market-driven pricing strategies, respectively. Business sustainability, also mentioned twice, looked at how AI could support sustainable practices for SMEs. Other topics, each mentioned once, included AI's role in B2B marketing management, cross-border e-commerce, and online shopping assistants (OSAs), as seen in Fig. 1.

The most common AI techniques applied in all these applications are Collaborative Filtering and Content-Based Filtering, each being mentioned six times, as they are very well known for producing personalized recommendations. Hybrid Models, mentioned three times, improve the accuracy of recommendation by combining different techniques. Machine Learning, with its five mentions, is a generic category covering a large number of models for analyzing data and automating tasks. Predictive Analytics, mentioned three times, is applied to the forecast of consumer behavior and new trends. Natural Language Processing (NLP), also three, makes it possible for chatbots and virtual assistants to understand and quickly reply to user queries. And finally, Specialized Models, such as XGBoost, Decision Trees, Naïve Bayes, Nearest Neighbor, and ensemble methods were each mentioned once in specific use cases like inventory management and optimization in digital marketing, as shown in Fig. 2.

A literature review of different studies showed very important findings about the role that AI has played in e-commerce and digital marketing. The enhanced customer experience and satisfaction due to AI are cited in numerous studies, with nine such instances. Improved marketing strategies are also frequently noted, again, nine times. The ones referring to increased customer engagement appear seven times, and personalization appears in eight instances. Anticipating consumer behavior was a key
finding in six independent studies, with data privacy and ethical concerns flagged in seven instances. Operational efficiency was improved in five research studies, with trust and transparency mentioned in four. Competitive advantage was another important benefit, recorded four times. AI-driven recommendation systems and real-time solutions were each mentioned twice, while customer data management was mentioned three times. One

time, there were mentions of improved inventory management and efficacy in marketing, along with business sustainability; twice, there were mentions of addressing algorithmic bias and problems with skilled IT workers and systems. This summary shows the transformative potential in e-commerce and digital marketing through AI, but most importantly, it underlines how imperative it is to address issues regarding ethics and privacy, as shown in Fig. 3.

<figure><img src="image_3.png" /><figcaption>Fig. 1. Statistical representation about the focus areas</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 2. Statistical representation about the AI techniques used</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 3. Statistical representation about the frequency for key findings</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. Statistical representation about the frequency for ethical considerations</figcaption></figure>

Ethical issues in most studies point to some important themes. The most frequently noted is data privacy, which appears in six studies. This underlines the critical importance of protecting consumer information in AI-driven e-commerce and digital marketing applications. Another crucial issue is algorithmic bias, pointed out in

three studies, indicating that fairness and impartiality in AI algorithms are needed in order not to incur undesired discriminations. There are two references to transparency in decision-making processes and two allusions to the ethical deployment of AI, through which transparent and intelligible AI mechanisms are
needed. Other concerns related to trust, ease of
use, ethical collection of data, managing
algorithmic fairness, and using human-Al
collaboration ethically appear less frequent but
are always important. Ensuring the compliance
with privacy regulations and improvement in data
protection and management methodologies,
dealing with issues of customer engagement and
bias, and maintaining transparency in Al-driven
approaches is fundamentally important in gaining
consumer trust and for the sustainable business
operation. The diverse ethical implications show
that it is multi-dimensional and intricate in
integrating Al into e-commerce and digital
marketing; hence, there is a need for a balanced
approach that considers both the benefits and
possible risks, as shown in Fig. 4.

6. RECOMMENDATION

* Investment in e-CRM systems and advanced payment solutions is crucial for improved management of customer data and enabling more personalized marketing strategies.

* Businesses can use social media platforms to interact with their target audience and build their brand.

* AI-driven analytics helps in predicting customer needs and preferences, ensuring a seamless shopping experience.

* Building customer trust faces challenges related to data privacy and scalability.

* Integration of AI in marketing strategies contributes to precise targeting of advertisements and messaging, increasing customer engagement and conversion rates.

* Emerging technologies such as voice assistants enable new avenues for interacting with customers.

* Responsible AI implementation requires ethical AI practices and collaboration with providers.

* Addressing cultural imbalances and considering AI's ethical implications can help create more inclusive marketing strategies.

7. CONCLUSION

Artificial intelligence has a huge, transformative
impact on e-commerce and digital marketing,
bringing about great opportunities to innovate
these sectors by optimizing operational
efficiencies, enriching customer experiences, and

forecasting market trends. The pandemic has
placed in evidence the need for companies to
adopt digital marketing strategies, using AI in
overcoming challenges and ensuring strength in
the future. It enables big data analytics, driven by
AI, to optimize customer journeys and drive
personalized marketing campaigns, addressing
data privacy and scalability issues in the process
to bring about substantive business growth. E-
commerce operations and marketing strategies
still have a lot of catching up to do in terms of
integration of AI; on the other hand, research in
this regard and interdisciplinary collaboration
continue to unfold fully. With the commitment to
ethical practice, investment in AI education, and
embracing state-of-the-art technologies like voice
assistants, organizations can possibly create
more inclusive, efficient, and innovative
marketing strategies. Future research should
also delve deeper into the impact of AI on
consumer behavior and the dynamic digital
landscape, which will allow businesses to
maintain competitiveness and a customer-centric
approach. The adoption of AI-driven strategies
will empower organizations to cultivate enduring
customer relationships, attain sustainable
growth, and respond effectively to swiftly
evolving market dynamics.

DISCLAIMER (ARTIFICIAL INTELLIGENCE)

Author(s) hereby declare that NO generative AI
technologies such as Large Language Models
(ChatGPT, COPILOT, etc.) and text-to-image
generators have been used during the writing or
editing of this manuscript.

COMPETING INTERESTS

Authors have declared that no competing
interests exist.

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