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AUTOMATION OF RETAIL MARKETING OPERATIONS WITH AI: METHODS FOR ENHANCING EFFICIENCY AND COMPETITIVENESS
I.B. Tretiakov, MBA
The Kellogg School of Management at Northwestern University (USA, Evanston)
DOI:10.24412/2500-1000-2024-11-4-244-248
Abstract. This article explores the role of automating marketing operations in retail through the implementation of artificial intelligence (AI). It examines methods of integrating AI technologies into retail business processes. Key stages of AI implementation are described, including data preparation, technology selection, and integration with business processes. Metrics for assessing the effectiveness of AI automation and its significance for business are provided. Special attention is given to improving customer experience and reducing marketing costs through personalization and precise targeting. The advantages of these methods for enhancing efficiency and competitiveness are analyzed.
Keywords: automation, marketing, retail, artificial intelligence (AI), customer experience, personalization, competitiveness.
In modern retail market, where competition is growing and customer needs are becoming increasingly dynamic, the use of artificial intelligence (AI) to automate marketing operations is of particular importance. This technology allows retailers to improve the efficiency of marketing processes. It also helps strengthen their competitive advantages by offering personalized solutions based on data. The implementation of AI technologies into the marketing strategy help companies to adapt to changing conditions and respond faster to consumer demand. This aspect is important in the context of increasing demands for quality and speed of service.
Automating marketing operations using AI opens up a wide range of opportunities for retailers, from audience analysis and segmentation to predicting customer behavior. With the help of machine learning (ML) algorithms and data analysis, retailers can optimize their advertising campaigns, improve targeting, and increase customer engagement. These technologies also allow them to track customer preferences and predict demand, reducing risks and increasing business profitability.
The deployment of AI in retail marketing operations is predicated on a number of theoretical
concepts, including predictive analytics and ML. These technologies facilitate the automation of decision-making processes and enhance the accuracy and efficiency of operations. Retailers can expeditiously adapt to fluctuations in demand, anticipate customer needs, and proffer them tailored solutions. The goal of this study is to examine the implementation of automation through AI technologies in retail marketing operations.
The benefits of AI automation for increased efficiency and competitiveness
Various marketing operations in retail using AI provides many benefits that allow companies not only to significantly improve efficiency and optimize costs, but also to strengthen their position in the competitive market. The key effects of using AI in marketing are related to increased accuracy of data analysis, improved customer experience through personalization, and the ability to quickly and flexibly respond to changes in demand and customer preferences. Such innovations often lead to increased demand among specialists. According to 2024 statistics [1], the global AI market in retail will continue to grow steadily, as it indicates the growing role of AI technologies in achieving competitive advantages and strengthening market positions (fig. 1).
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2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 Fig. 1. Global trends in AI market size within the retail sector, billion dollars
One of the most significant benefits of implementing AI is the increased accuracy of marketing strategies and the reduction of costs for advertising and other marketing activities. With the help of ML algorithms and big data analysis, companies can more accurately identify the target audience and their preferences, which allows them to significantly increase the profitability of marketing campaigns [2]. These algorithms are able to analyze many parameters, such as demographic data, purchase history, customer prefer-
ences, and behavioral patterns. This data allows them to optimize campaigns by targeting advertising materials to specific audience segments, which reduces overall marketing costs. According to 2023 statistics [3], Retailers using AI and ML technologies are outperforming their competitors. In 2023 and 2024, companies that have implemented these technologies have achieved double-digit sales growth compared to previous years (fig. 2).
Fig. 2. Impact of AI and ML use on retail performance between 2022 and 2024 worldwide, growth rate
in %
Additionally, automation eliminates many manual processes in marketing operations, reducing labor costs and minimizing the likelihood of errors. Automated systems can independently select the optimal channels and time for advertising distribution, as well as monitor key performance indicators in real time, which allows marketing teams to make corrective decisions faster.
Innovative AI solutions play a key role in creating personalized offers that increase customer engagement and satisfaction. Modern algorithms
allow analyzing customer behavior in real time and offering each buyer the most relevant products or services. Personalized recommendations, loyalty programs, and targeted advertising create a sense of individual approach for the customer and increase the likelihood of repeat purchases [4].
It also enables retailers to cross-sell and upsell, encouraging customers to purchase additional products and increasing the average check. This is achieved by recommending products that
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logically complement existing purchases or offerings based on the customer's personal preferences and interests. This approach strengthens trust and commitment to the brand, promoting their long-term loyalty.
In a highly competitive environment with rapidly changing consumer preferences, a company's ability to quickly respond to changes is significant. Different AI technologies provide retailers with tools to monitor and analyze the market in real time. It allows them not only to anticipate changes in demand but also to adapt marketing strategies in advance. Predictive analytics allows to identify changes in customer needs in advance and adapt advertising campaigns, avoiding losses on ineffective offers [5].
The company's competitiveness is also enhanced by flexibility and speed of decision-making. Innovative AI tools make it possible to automatically adjust prices and offers depending on market conditions, competitors' behavior, and demand levels. As a result, companies using AI can implement new strategies faster and respond more effectively to competitive actions, ensuring a sustainable position in the market [6].
The integration of AI allows marketing teams to focus on strategic tasks. It frees companies from routine tasks such as monitoring metrics and selecting target audiences. Automated systems for data analysis, forecasting and monitoring allow marketers to spend more time developing new ideas and strategy.
Automating marketing operations with AI provides retailers with unique opportunities to improve efficiency and competitiveness. Increased targeting accuracy, improved customer experience through personalization, flexibility and speed of adaptation to market changes, and increased productivity of marketing teams make AI a powerful tool that can significantly strengthen a company's position in a highly competitive environment. These benefits create the basis for strategic growth and allow retailers to confidently respond to new challenges, ensuring high profitability and customer retention in the long term.
Methods for implementing and optimizing AI in marketing operations
Implementing AI into retail marketing operations requires a systematic approach and preliminary analysis to achieve high efficiency and maximize return on investment. In practice, the AI
implementation process is divided into several key stages: assessing the current state of marketing processes, selecting suitable AI tools, integrating solutions into existing systems, and optimizing the algorithms to achieve better results. The stages of AI implementation may vary depending on the scale of the company, but most successful projects go through a number of stages.
Data assessment and preparation. Marketing operations rely on huge amounts of information about customers, transactions, user behavior, and other aspects. Before implementing AI, specialists need to analyze the quality of the data and, if necessary, clean, standardize, and structure it. Data processing and management tools such as Apache Hadoop and Spark help in preparing data for AI analysis.
Selecting AI technologies. Depending on the company's goals, specific AI tools and algorithms are selected. For marketing operations, ML algorithms, neural networks, natural language processing (NLP), and predictive analytics are most often used. In this regard, neural networks are used for product recommendations, and NLP is used for analyzing reviews and customer interactions.
Integrating AI into business processes. Implementing AI requires integration with existing CRM systems, marketing campaign management platforms, and other business tools. Application programming interfaces allow you to connect AI solutions to various systems, providing a unified infrastructure for data processing and decision making. At this stage, you can use ready-made AI solutions from large providers (e.g. Google AI, Amazon AI) or develop company's own systems.
Continuous optimization and improvement.
Once AI is implemented, it is necessary to regularly evaluate its effectiveness, adjust algorithms, and train models on new data. Metrics such as forecast accuracy, percentage of deviation from targets, and customer engagement help evaluate the success of AI use and identify areas for optimization. This allows you to improve the effectiveness of marketing operations and adapt the system to new conditions.
To evaluate the effectiveness of AI implementation in marketing operations, several key metrics are used to help track how well AI contributes to achieving business goals (table 1).
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Table 1. Performance metrics and method Is [7, 8]
Metric Description Business value Example data to track
Conversion rate Percentage of users who completed a target action (e.g., purchase) influenced by AI. Increased sales and profitability of marketing activities. Conversion rate, sales metrics.
Marketing cost reduction Reduction in marketing expenses through optimized targeting and elimination of unnecessary costs. Enhanced ROI and efficiency of the marketing budget. Percentage of cost reduction, campaign efficiency analysis.
Customer engagement Frequency of users' interactions with AI-suggested content or ads. Higher customer loyalty and likelihood of repeat purchases. Click rates, interaction time.
Customer acquisition cost (CPA) Indicator reflecting marketing costs needed to acquire a single customer. Optimized budget for customer acquisition and retention. Cost per customer, comparison of CPA before and after AI implementation.
Effective implementation and optimization of and strengthening the competitive position of AI in marketing operations requires a compre- companies. Modern AI technologies such as ML, hensive approach and systematic work with data. predictive analytics and NLP allow retailers to Selecting the right technologies, competent inte- more accurately segment audiences, offer per-gration, and regular optimization allow retailers sonalized solutions and optimize marketing to improve the performance of marketing opera- budgets, which reduces costs and increases return tions, as well as maintain a high level of competi- on investment. A strategic approach to the im-tiveness. plementation of AI, covering the selection of ap-
Conclusion propriate technologies, training of personnel and
The integration of AI into retail marketing op- regular optimization of algorithms, helps to cre-erations is not only a technological improvement, ate marketing processes that can quickly adapt to but also a strategic tool for increasing efficiency changes in the market and customer preferences.
References
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- Экономические науки -
АВТОМАТИЗАЦИЯ МАРКЕТИНГОВЫХ ОПЕРАЦИЙ В РИТЕЙЛЕ С ПОМОЩЬЮ ИИ: СПОСОБЫ ПОВЫШЕНИЯ ЭФФЕКТИВНОСТИ И КОНКУРЕНТОСПОСОБНОСТИ
И.Б. Третьяков, магистр
Школа менеджмента Келлога при Северо-Западном университете (США, г. Эванстон)
Аннотация. В данной статье исследуется роль автоматизации маркетинговых операций в ритейле с помощью внедрения искусственного интеллекта (ИИ). Рассматриваются методы внедрения технологий ИИ в бизнес-процессы ритейла. Описываются ключевые этапы внедрения ИИ, включая подготовку данных, выбор технологий и интеграцию с бизнес-процессами. Приводятся метрики для оценки эффективности ИИ автоматизации и значимости для бизнеса. Особое внимание уделяется улучшению клиентского опыта и снижению затрат на маркетинг за счет персонализации и точного таргетинга. Анализируются преимущества данных методов для повышения эффективности и конкурентоспособности.
Ключевые слова: автоматизация, маркетинг, ритейл, искусственный интеллект (ИИ), клиентский опыт, персонализация, конкурентоспособность.
