ANALYSIS OF THE EFFECTIVENESS OF USING ARTIFICIAL INTELLIGENCE IN PERSONALIZING MARKETING STRATEGIES

ABSTRACT

Artificial intelligence technologies have the potential to revolutionize consumer marketing as it exists today. At the level of individual business entities, marketing campaigns that previously required months to develop content, analyze data, and target customers can now be launched in weeks or even days, often with large-scale personalization and automated testing. In this context, studying the capabilities, technologies, and areas of application of artificial intelligence in the marketing sphere is a pressing issue. The purpose of this article is to analyze the effectiveness of using artificial intelligence to personalize marketing strategies. Particular attention is paid to the key areas of application of intelligent analysis technologies in the personalization of marketing offers and the tools that allow them to be implemented in practice. An overview of practical examples of the use of artificial intelligence in personalization at enterprises in various industries and the effects obtained as a result is also provided.

АННОТАЦИЯ

Технологии искусственного интеллекта позволяют революционизировать потребительский маркетинг в том виде, в котором он на сегодняшний день существует. На уровне отдельных субъектов хозяйствования маркетинговые кампании, которые раньше требовали месяцев на разработку контента, анализ данных и таргетирование клиентов, теперь можно запускать за несколько недель или даже дней, часто с масштабной персонализацией и автоматизированным тестированием. В данном контексте изучение возможностей, технологий и сфер приложения искусственного интеллекта в маркетинговой сфере представляет собой актуальную задачу. Цель статьи заключается в проведении анализа эффективности использования искусственного интеллекта для персонализации маркетинговых стратегий. Отдельное внимание уделено ключевым сферам применения технологий интеллектуального анализа в персонализации маркетинговых предложений и инструментам, позволяющим их реализовать на практике. Представлен обзор практических примеров использования искусственного интеллекта в персонализации на предприятиях различных отраслей и полученные в результаты эффекты.

Keywords: marketing, personalization, artificial intelligence, data, analysis, messages, consumer

Ключевые слова: маркетинг, персонализация, искусственный интеллект, данные, анализ, сообщения, потребитель

Introduction

Of all the functions of modern business entities, marketing is likely to benefit the most from the application of artificial intelligence (AI) technologies. The core tasks of marketing – understanding customer needs, selecting appropriate products and services, and persuading people to buy – are precisely the areas that AI can significantly improve. In this context, the results of an analysis conducted by McKinsey in 2023 based on more than 400 advanced AI use cases are noteworthy, which showed that marketing is the area where AI brings the greatest benefit [1]. These findings are confirmed by the dynamics of the global AI in marketing market, which, according to expert estimates, will grow from US$13.76 billion in 2024 to US$93.98 billion by 2032, with a compound annual growth rate of 22.5% over the forecast period [2] (see Figure 1).

Figure 1. Dynamics of the global AI market in marketing, billion dollars [2]

One area where AI is showing great results is in the personalization of marketing strategies. The challenge facing brands today is to clearly communicate with a huge number of consumers who speak different languages, come from multiple cultures and socioeconomic backgrounds, and make purchasing decisions based on highly personal preferences. Reaching all of these consumers at scale and doing so in an authentic way is no easy task. As a McKinsey study found, 71% of consumers expected companies to provide personalized experiences, and 76% were disappointed when they did not [3]. However, when marketers get it right, they can create significant value.

In the pre-digital era, marketing personalization was manual, based on general demographic data and direct feedback, and was often inaccurate. The digital revolution brought with it a flood of consumer data, but despite early algorithms, true personalization remained challenging. The advent of AI and machine learning has revolutionized the field. Marketers have moved away from broad categorizations and toward individual behavior, moving from reactive to predictive strategies. In fact, the shift from intuition to AI-driven methods highlights the transformative role of technology in modern marketing.

Given the enormous potential of these innovations, it is critical for marketers to understand the types of AI marketing applications available today and how they may evolve. Also critical is choosing the most appropriate analytics model to create and scale highly relevant messages with customized tone, imagery, copy, and experience at high volume and speed.

Materials and methods

Thus, the relevance and high scientific and practical significance of the issues under consideration predetermined the choice of the topic of this article.

Research results

The possibilities of using machine learning algorithms to predict and adapt to user behavior in real time are considered in their publications by Sergeev N.A., Akhmetgareeva A.A., Semennikov A.V., Shaban A.P., Tupitsyna M.D., Nisreen Ameen, Gagan Deep Sharma, Shlomo Tarba , Amar Rao , Ritika Chopra.

The prospects for enhancing the basic stack of marketing technologies, which with the help of AI will help ensure that every physical and virtual touchpoint is designed for direct communication with a person, are within the scope of scientific interests of Sinyaeva P.A., Lopatkina D.S., Tikhonyuk N.E., Chalova T.V., Chih-Wen Wu, Abel Monfort , Pooja Mehta , Charles Jebarajakirthy.

However, despite the active interest of scientists in the issue under consideration, its novelty and the rapid development of digital innovations still leave a wide range of unresolved issues. For example, the possibilities of combining machine learning, natural language processing and generative AI for personalizing marketing are insufficiently studied. In addition, AI-supported predictive analytics methods for forecasting the results of a marketing campaign, recognizing patterns by studying previously implemented strategies, deserve special attention.

Thus, the purpose of the article is to conduct an analysis of the effectiveness of using AI to personalize marketing strategies.

First of all, it should be noted that AI refers to a wide range of technologies such as natural language processing, machine learning, deep learning, computer vision, and many others. Machine learning has a significant impact on digital marketing due to its ability to analyze data and provide analytical tools. Thus, a personalized marketing strategy can be defined as adapting marketing efforts to individual consumer needs. Typically, this process involves collecting data on user behavior, preferences, and interactions, as well as contextual data such as location, time of day, and device used [4].

Often, data collection involves combining information collected by a company with third-party data sets. This data is then analyzed by AI algorithms that identify patterns and trends in user behavior. Typically, AI also groups users into segments based on similar characteristics and behavior in a process known as audience segmentation. By analyzing these segments, AI then recommends products, services, or content that match the preferences and demographics of consumers. It can also display specific content on a website or app to different users based on their unique profiles.

Some key areas of application of AI in personalization include:

1. Personalized product recommendations
2. AI-powered chatbots
3. Intelligent content
4. Ad targeting
5. Dynamic pricing
6. Predictive Personalization

Today, a wide range of tools has been developed that allow personalizing marketing using AI. From this wide range, it seems appropriate to highlight the most proven and effective approaches.

1. Generate creative on-demand at scale. AI gives marketing teams the ability to create emails, graphics, and ads at unprecedented speed and scale. For example, generative AI can reduce content creation time from weeks to hours. AI tools like Adobe Firefly, OpenAI ’s DALL-E, and generative AI-enabled platforms like Figma and Canva make this more accessible than ever.

2. 360-degree customer view. Generative AI is revolutionizing data synthesis by increasing the scale, speed, and quality of processes like metadata tagging. For example, L’Oréal saved 120,000 hours of manual labor and improved SEO by using SiteCore’s generative AI to automate the tagging of 200,000 names across 36 brands and over 500 websites [5].

3. Real-time decision engines: AI doesn’t just analyze data, it makes it actionable. With decision engines based on reinforcement learning, retailers can test different ad variations to identify the most compelling combinations of creative, messaging, offers, and contextual parameters such as frequency, day of the week, and time of day for each customer.

To better target promotions and AI- generated content, marketers should use a technology stack that brings everything together. Specifically, to lay a solid foundation for growth through personalization, marketers should ensure that the five elements in Figure 2 leverage the latest technology innovations and integrate seamlessly with each other.

Figure 2. Technology stack for large-scale personalization in marketing [6]

Based on the study of modern scientific publications and expert surveys, in Table 1 the author systematizes various AI technologies that have already found their application in the personalization of marketing strategies and the results obtained thanks to them.

Table 1.

 Practical examples of using AI technologies for personalized marketing (compiled by the author)

Conclusions

In summary, AI-powered personalization is changing the face of marketing, offering unprecedented opportunities to analyze and engage with consumers. The potential for these technologies is enormous, but companies must find a balance between technological innovation, transparency, and ethical standards. To achieve maximum impact, they must also focus on operational efficiency, eliminate redundant systems, and establish robust governance. This can help unite disparate tools into a single engine for more relevant and personalized customer engagement, driving real growth.

References:

1. Semennikov A.V. The Impact of Artificial Intelligence on Marketing - Changes and Prospects // Economics and Management: Problems, Solutions. 2024. Vol. 5. No. 7 (148). Pp. 100-106.
2. Mehran A., Hossein B. Kh. N. Investigating the Acceptance Factors of Marketing Systems Based on Artificial Intelligence in Small Industrial Companies // Human Behavior and Emerging Technologies. 2025. Vol. 20. No. 1. Pp. 65-74.
3. Yishu L., Weixiong C. Optimization of Brand Marketing Strategy of Intelligent Technology under the Background of Artificial Intelligence // Mobile Information Systems. 2021. Vol. 1. No.19. Pp. 10-19.
4. Algalieva G.S., Shalkarbek A. Artificial Intelligence as a Factor of Transformation in PR, Marketing and Media Space // Russian School of Public Relations. 2024. No. 33. Pp. 10-27.
5. Tikhonyuk N.E., Chalova T.V. Classification of generative artificial intelligence technologies in digital marketing // Economy and Entrepreneurship. 2024. No. 7 (168). P. 1352-1355.
6. Sanjeev V., Vartika S. 8-T Framework for Artificial Intelligence-Driven Branding: A Strategic Typology // International Journal of Consumer Studies. 2024. Vol.49. No. 1. Pp. 29-36.

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