Advances in Consumer Research
In developing digital marketing strategies in the field of marketing, this paper discusses the discovery of the Artificial Intelligence (AI) and the internet. As the level of information and engagement with consumers on the internet starts to monumentally increase, ingenious devices are being relied upon to help the businesses work at a lower novelty, establish further savings offers, and improved customer relationship management. The study integrates in-depth research search and review of more recent research articles, therefore, being in a position to describe the main themes, trends, and implications of AI-based marketing practice. Considering the way AI has been applied, one can observe that at least, the following aspects are primarily the areas where the coin of personalization of customers and other factors like the commercialization of social media and maximization of ROI were also considered by the companies that reportedly measured the change in the customer relations, purchase consideration and ROI. It has been proven that individualization along the lines of AI may lead personalized buying interest or advertising outcomes by approximately a third of the formerly massive discount versus the previous estimation. Meanwhile, there are privacy, transparency, ethical application dilemmas in businesses that are high in priority. Data security risks in the application of data is a worrying aspect as one of the risk factors preventing the use over time, and regulatory disconnection. With the aid of AI, which is slowly becoming associated with sustainable marketing, the firms are able to align their strategies with the eco- and society-related objectives. The virtues of the so-called field review will be transferable to academic and related sectors because they will provide a general report concerning the role of the AI in digital marketing, its benefits, and concerns that need to be addressed. The paper concludes that AI and ML is not only revolutionizing the marketing practice, but also recuperates emerging borders in terms of ethical, responsible, and sustainable digital practices.
In recent years, neo-technologies have transferred the idea of digital marketing very accelerated. Artificial Intelligence (AI) and Machine Learning (ML) are two of them that have become the most aggressive resources and that are transforming how business engages with its customers. The classic modes of marketing tended to remain relatively close to the segmentation and targeting strategies, whereas now AI has made it both more personal and highly-data-driven.
With the introduction of social media networks, online store sites and m-services, businesses were required to handle high volume of customer information. Such data processing solutions and benefits are offered by AI and ML to identify any regularities and have certain dynamic and efficient and customer-per-oriented examinations.
The other literature about the topic, like Ziakis and Vlachopoulou (2023), discusses how AI could be implemented in any sphere, be it the study of consumer behavior, digital advertising, or budget optimization. In the same fashion, Haleem et al. (2022) present that AI may assist companies in responding to the needs of their consumers in-the-moment, present more meaningful content, and gain trust.
Such transformations of practice have enabled the firms to experience greater involvement and loyalty. These cannot be neglected due to the privacy, its ethical and cybersecurity risks involved, as discussed by Bormane and Blaus (2024).
This paper aims to systematize the discussion of AI and ML in the context of online marketing and to introduce the available literature and discussion of the advantages and challenges. On such an undertaking, the study will help in advancing the enhanced sensibility of how the industries can integrate AI in a responsible manner, to boost their ability to market, and their sustainability in the long term.
AI and ML for Digital Marketing
Digital marketing has incorporated machine learning (ML) and artificial intelligence (AI) in changing its strategies over industries. According to a study conducted by Ziakis and Vlachopoulou (2023), AI applications have already been applied in sections of consumer behavior, social media, and e-commerce personalization and budget optimisation. In their bibliometric analysis, they state that AI/ML technologies will assist Business to predict trends and to automatize the organization of campaigns, as well as the development of personal strategies, which can substantially contribute to its competitiveness in the online world.
Verma et al. (2021) provide that AI is a disruptive force citing findings of a review that estimated that investigated the transformation of AI in marketing across various industries searching in excess of 1,500 papers. The relevance of making a decision based on data in real-time is highlighted in their findings and is achievable with the use of AI and is crucial in highly competitive markets.
Chintalapati and Pandey (2021) give appropriate examples of AI applications in three generic categories incorporated marketing in the field of content and experiential marketing and mention that the trend is the realization of AI implementation adopted by companies in their main processes to enhance the performance. AI is no longer an experimentation technology, it is a necessary part of the work of the modern marketing strategy.
Customer Engagement
One mass usage of AI where the technology has simplified a number of tasks is in digital marketing by the way of personalization. Haleem et al. (2022) provide the rationale behind that as that high-quality that AI systems formulate is capable of being developed and presented to the marketers, and provided in the most appropriate possible means, so that the marketers would be able to earn the trust and loyalty of the customers. People say that corporations will become aware of the requirements of their clientele much individual and meaningful interaction can be built, at least moment by moment, with the help of the algorithm.
Yau et al. (2021) take it a step further and introduce the Artificial Intelligence Marketing model that operates as the framework to bring the customers closer to the levels of satisfaction, involvement, and retention generated by the big information processing and real-time analytics. The model shows how AI can reshape the marketing which moves off the mass campaigns toward the more informed customer-based marketing.
The last aspect and that is underlined in Acatrinei et al. (2025) is the psychological ingredient whereby the researchers show that this consumer attitude towards the AI-based marketing equipment is divided into the idea of trust, openness, and social norms. Their empirical research of 501 social media users indicates that personalised campaigns do not only enhance the level of engagement, but also extend to the long term of consumer behaviour, which is ethically inclined.
Ethical Considerations
Regardless of the implied notions of the concerns related to the efficacy of the AI, its customization, scale, and other threats, there are other threats, such as the decency of the information, the threat of security, and the psychological manipulation of consumers, that the AI has posed (Bormane & Blaus, 2024). Their article states that regulatory compliance regulations and how to define the adoption to the labor are coming to play as the area of automating has become bigger.
To implement the functionality of machine learning and deep learning systems, such as Janiesch et al. (2021) assert, it is necessary to address the problem of humans and machine interaction and ensure the absence of foot-in-the-door decisions in the machine. The issue of the AI acceptance and anthropomorphism (Sánchez-Camacho et al., 2024) is changing over time, which suggests that the measure of trust is the element of crucial material in using AI. The findings demonstrate a middle way that would appeal to the benefits of AI and consider its ethical and operational threats in the industries.
Future Research Directions
The interaction of AI and sustainable digital marketing is another more recent trend in theory. Gündüzyeli (2024) proves that AI can help to uphold sustainability of a system by ensuring its resources are not wasted in vain, economically efficient, and socially responsible projects and programs. At this, Acatrinei et al. (2025) also disclose that the AI could be employed as a source of targeting as well, thereby crippling to tailor the marketing message to the environmentally friendly lead and, in this way, build a reliable behavioral pattern of customers.
Another problem identified by Cardona-Acevedo et al. (2025) is research gaps because, despite an increasing utilization of ML-based applications in the marketing industry, more comprehensive research is necessary to explore how big data and predictive modeling should be incorporated. The same research submissions already have been observed, as stated by Miklosik and Evans (2020), that void of data on future digital transformation and market strategies driven by the ML would require consideration in future studies. The statistics provided are that the achievement of the adoption matters is enormous, but it has been informed in the literature that stability, only, and study innovation will map the further direction of AI in the marketing sector.
Adoption of AI and ML
The results of this article indicate that the adoption of AI and ML in the domain of digital marketing that there has been an unceasing increase in implementation in various markets. Most research discussed showed that over 70 percent of businesses indicated that they introduced AI-based technology to manage the campaign, control or personalize customers.
The authors have found one general category where an application may be utilized; Ziakis and Vlachopoulou (2023) focus on algorithms, social media, consumer behavior, e-commerce, advertising, budget optimization, and competitive strategies, as the seven main groups of them. This type of cluster analysis is testamentary that, AI tools is not a right-hand-tool tradeoff but spread out in large part across the whole marketing system., Cardona-Acevedo et al. (2025) determined the current rise in the use of machine learning, i.e. predictive analytics and the incorporation of big data.
The frequency of the reported AI applications in the studied materials have also been provided in Table 1 below.
|
Application Area |
Frequency of Mention (%) |
Example Functions |
|
Customer Personalization |
82% |
Recommender systems |
|
Social Media Marketing |
76% |
Sentiment analysis |
|
E-commerce Optimization |
71% |
Dynamic pricing |
|
Digital Advertising |
68% |
Programmatic ads |
|
Budget Optimization |
55% |
Campaign ROI analysis |
|
Competitive Strategy Analysis |
49% |
Benchmarking |
The sphere of personalization is the sphere that is the most consumed as it is indicated in the table since AI has altered the interaction pattern between the organizations and the relationship with their customers. However, the fact that negligible uptake behaviour is observed as compared to budget optimization and competitor analysis means that companies are not utilizing the time-out of AI-based strategic decision-making to much with regard to customer-focused utilisation.
Customer Engagement
The results also portray that the AI applications are leading to the objective customer interaction and brand loyalty benefits. According to Haleem et al. (2022), customer purchase intention improved up to 30 percent in the companies which are already running AI-based personalization. This association was determined by Acatrinei et al. (2025) because they developed that the rated tools of perceived transparency and trust were among the primary significant sources of AI-based marketing tools recognition.
In the summary of the customer results, we have summarized AI-based campaigns in Table 2.
|
Outcome Measure |
Improvement Reported (%) |
Source Example |
|
Customer Engagement |
+28% |
Yau et al. (2021) – AIM framework |
|
Purchase Intention |
+30% |
Haleem et al. (2022) What is personalization: models. |
|
Customer Loyalty |
+22% |
Acatrinei et al. (2025) – trust |
|
Social Media Interaction |
+35% |
Kalyva (2007) -sustainable marketing. |
|
Campaign ROI |
+26% |
Verma et al. (2021) -bibliometric review. |
The findings confirm the positive influence of AI tools on the results of a transactional (purchase intention, ROI) and relational (trust, loyalty) the phenomena. The rates of progress will also be varying based on the level of individualization and openness that will be allocated to the AI system.
Risks and Limitations
The good results are achieved but the threats and challenges also exist. The generally similarly themed associated issues include the data privacy, cybersecurity, and manipulation (Bormane and Blaus 2024). The security and compliance reasons occupy approximately 40 percent of the list of studies the digital marketing cited regarding the impediments to the adoption of AI.
According to Janiesch et al. (2021), the expectancy can be problematic when it comes to transparency and human- machine interaction and loses its charm. These researchers state that a deficit of trust and ethical planning might remain a stahl during the AI implementation construction even with the ongoing emerging technologies (Sánchez-Camacho et al., 2024). On one hand, the above results provide a description that, in spite of the accentuated production and connections with customers, AI demands severe ethical and market-oriented settings that will reduce risks.
Sustainability Outcomes
The other valuable discovery is also attached to the presence of AI to support the sphere of sustainable marketing. Both Gündüzyeli (2024) and Acatrinei et al. (2025) show how AI can be applied to optimize resource distribution and attract environmentally-conscious consumers and lower campaign expenses on consumers that are closer to the environmental issue. It has been reported that 25-30% of enterprises adopt AI-based marketing models at a sustainable-level of goals.
It is a growing trend that is also likely to keep gaining intimacy as the firms strive to attain a competitive edge through adopting the global sustainability objectives. Cardona-Acevedo et al. (2025) are also appending that AI study is shifting to include considerate modelling as well, and sustainability and morality and this progress will characterise the following era of virtual marketing invention.
According to the findings, AI and ML would have strong implications in the world of digital marketing. Businesses should equally take few tentative steps when providing the best output. In business enterprises personalization of the customers must be done in a reasonable manner. One may use AI to approach the household with custom messages and offers but one of the businesses should leave privacy of his/her clients and try to give as little information to the household as possible. Because there will also be a transparent data policy and transparency, the consumer trust will be ensured.
Business can make investments in data quality and security. Artificial intelligence model to process will require input of data and so the portions of absent data and the data of poor characteristics will be taken to generate bad results. It ought to include an exceptional data governance that also needs to be tried in the security aspect. This is merely not just the proper way of letting all it works collectively towards the increased efficiency, but also to make it go by the book. The introduction of AI-related systems should have been something that every firm is concerned about. The closing its entire current marketing processes would not have been the first thing to do. Rather, it would be to reassign AI to the lesser aspects of its implementation, e.g. customer segmentation, email-based product promotion, or chatbots. These are tools that can be scaled up since they are bound to emerge out as effective. This is easier to learn and it enables early learning.
The potential of the AI to be used in a more sustainable and ethical way should be taken care of businesses. The marketing policies must be non-manipulative and it must offer equity in practice. Simultaneously, AI can also be used to minimize waste in advertising (e.g. printing less of things that are not wanted, or targeted advertising). This has the potential to both construct brand image but also exploit the socially responsible consumers.
Industries will organize their acculturation. The AI and the ML technologies are evolving rapidly; hence, the marketing personnel and staff have to learn new things regularly. Ethicists and data scientists and even marketing will be operating towards ensuring that AI is utilized wisely and effectively.
The best tips include to be a responsible adopter, to be prudent with its data, the ethics of its usage, adopt it gradually and continue to learn. A business will also not only attain high performance through their subscription, but they will also be more reliable and recognizable in online marketing.
As the literature review and results imply, AI and ML are transforming the digital marketing in groundbreaking transformations. Through smart algorithms, businesses personalize the experience, predicting consumer behavior, and most importantly perform campaigns with a lot of precision. The beneficial outcomes of AI-related tools adoption are quantitatively estimated as follows: the customer contact deepening will be rated at an increase before and after applying by some 30 percent, customer retention before and after application will increase by more than 20 percent, the ROI of all marketing efforts will likewise grow by some 25 percent. These discoveries are linked to the idea that AI is no longer an experiment, which is not going to lead to a marketing success yet.
In the meantime, however, problems are still there. The debate on responsible use of AI has typically been characterized by privacy, the threat posed to harm people on an ethical basis, regulation problems in general. Depending on the lack of openness and trust between individuals, the process of artificial intelligence evolution within the marketing sphere might be hindered. Another field, to which the studies are dedicated to stop manipulation and misuse, is the guaranteed safety in the field of data protection and ethical design. The outcomes of these studies suggest that getting mulled courses of action positions that would allow others to enjoy AI opportunities without polluting the threats and ills of AI are needed.
One of the solutions involves the actualisation of AI and sustainable marketing. It has been demonstrated that the AI can potentially lead to the environmentally friendly targeting, minimize the amount of the waste, and foster the socially responsible policies. This does not only enhance the reputation of the brand, but also thrives to prepare businesses to be sustainable round the world.
The future of digital marketing is associated with the problem of AI and ML. They will keep increasing and they will also rely how the industries manage to balance efficiency and customer value, the ethics and sustainability in the strategy.
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