CC BY
13UDC 004.8
Kuznetsova Anna
master's degree, Novosibirsk State University Russian Federation, Novosibirsk
ARTIFICIAL INTELLIGENCE IN CONSUMER BEHAVIOR ANALYSIS:
TRENDS AND PROSPECTS
Abstract: Artificial intelligence (AI) significantly influences consumer behavior analysis by providing tools for service personalization, audience segmentation, and demand forecasting. This article highlights the main AI technologies, including machine learning, natural language processing, and computer vision, discussing their advantages and limitations. Practical applications such as recommendation systems, text data analysis, behavioral pattern identification, demand forecasting, and data generation are examined. Particular attention is paid to the prospects of using generative models and clustering for optimizing marketing strategies. Ethical issues related to AI application are also discussed. The conclusions emphasize the importance of integrating AI technologies to enhance consumer interaction efficiency.
Keywords: Artificial intelligence, consumer behavior, machine learning, natural language processing, recommendation systems.
INTRODUCTION
The modern development of artificial intelligence (AI) technologies significantly influences the analysis of consumer behavior [1]. In the era of digital transformation, organizations aim to leverage data to develop strategies that enhance customer interaction efficiency. The primary goal of such analysis is to identify key factors determining user preferences and behaviors, optimizing marketing and business decisions.
This article aims to analyze the current trends and prospects for using AI to study consumer behavior, including key technologies and their impact on decision-making. Special attention is given to practical examples and data visualization to understand the current opportunities and limitations in this field.
AI in this area encompasses various approaches such as machine learning (ML), natural language processing (NLP), and computer vision methods. These technologies allow for processing large amounts of data, real-time analysis, and predicting changes in consumer preferences.
MAIN PART
AI serves as a key tool for analyzing complex data generated by users across various digital channels [2]. The application of ML, in particular, enables the classification of consumers, the identification of their habits, and the formation of personalized offers. Figure 1 presents the distribution of different AI technologies used in consumer behavior analysis, highlighting ML, NLP, computer vision, and other methods.
Other
Machine Learning
40.0%
Natural Language Processing
Figure 1. Major AI technologies in consumer behavior analysis As shown in the figure, ML holds the leading position, which is explained by its versatility and ability to work with diverse data types. NLP, with a 30% share, emphasizes the importance of text information processing, including the analysis of reviews and user queries. Computer vision methods are used less frequently but are increasingly relevant in the context of visual content analysis [3].
AI applications are not limited to data analysis but extend to forecasting. For instance, modern ML models can predict the likelihood of purchase based on user behavior on websites and applications. Such approaches allow companies to reduce marketing expenses by focusing on target groups [4].
Table 1 illustrates the key advantages and limitations of using various AI methods in consumer behavior analysis.
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Table 1. Key AI technologies, their advantages, and limitations
Technology Advantages Limitations
Machine learning (ML) Versatility, accuracy, forecasting capability High data requirements, need for training
Natural language processing (NLP) Text data processing, sentiment analysis Complexity of unstructured data processing
Computer vision Image and video analysis, UX/UI enhancement High computational demand, resource-intensive
Generative models Data creation, idea generation Need for large data sets, complexity of interpretation
Clustering methods Consumer segmentation, pattern identification Interpretation issues, algorithm dependence
Recommendation systems Personalized suggestions, sales growth Contextual limitation, data dependency
Graph analysis Identifying object connections, network analysis High analysis complexity, need for high-quality graphs
The data in the table demonstrates a wide range of technologies used to analyze consumer behavior, each with unique advantages and limitations. For example, ML remains the most versatile tool due to its ability to accurately predict and analyze complex data. However, successful ML application requires significant amounts of training data and computational resources [5].
NLP and computer vision are specialized fields that play crucial roles in analyzing text data and visual content, respectively. Despite their high relevance, the complexity of unstructured data processing and high computational demand limit their use in some areas. Generative models and clustering methods offer innovative approaches such as data creation and consumer segmentation, making them promising for further development [6].
The development of recommendation systems and graph analysis also represents important AI directions. Recommendation systems enable personalized customer interactions, boosting sales and customer satisfaction. Graph analysis, despite its complexity, allows for identifying hidden connections between objects, particularly valuable for building complex network models [7].
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The application of each technology requires consideration of specific conditions and objectives. For maximum efficiency, it is essential not only to select the appropriate methodology but also to integrate it effectively into existing processes.
PRACTICAL APPLICATIONS OF AI IN CONSUMER BEHAVIOR
ANALYSIS
The use of artificial intelligence (AI) in consumer behavior analysis finds widespread application across various business sectors, where processing large volumes of data and personalizing services are critical success factors. One of the most prominent examples is the development of recommendation systems actively used in e-commerce and streaming platforms [8]. These systems, based on machine learning (ML), analyze user preferences and provide personalized recommendations. For instance, ML algorithms on platforms such as Amazon or Netflix help increase conversion rates and retain customers through accurate predictions of their interests.
Another significant example is the use of natural language processing (NLP) methods for analyzing textual data such as reviews, comments, and customer inquiries. In the hospitality and restaurant industries, review analysis helps not only assess customer satisfaction but also identify key service issues. NLP models can determine the emotional tone of text, identifying how positively or negatively customers perceive the provided services. Companies use this data to develop strategies for improving service quality [9].
In the retail sector, AI is employed for consumer segmentation. Clustering methods divide customers into groups based on their purchasing habits, preferences, and demographic data. This information is used to design targeted marketing campaigns. For example, clustering analysis can identify the most profitable customer segments, enabling companies to allocate resources with maximum efficiency.
Moreover, AI is actively used in demand forecasting. ML algorithms analyze historical sales data, seasonal trends, and external factors such as weather or events. These forecasts help optimize supply chains and reduce costs. For instance, in retail, demand forecasting prevents shortages of popular products or overstocking, minimizing financial losses.
Another important application is real-time consumer behavior analysis. Computer vision technologies are utilized to study customer routes in shopping malls and supermarkets. This data is used to optimize product placement and develop effective marketing strategies. For example, based on customer flow analysis, companies can redesign routes and sales zones to increase the time customers spend in stores.
Finally, an innovative direction involves the use of generative models for analyzing and predicting consumer behavior. Such models are used to create new data, for instance, simulating consumer reactions to new products or services. This is particularly relevant for testing concepts before market launch, minimizing risks and costs.
The examples of AI applications demonstrate its versatility and effectiveness in consumer behavior analysis. Each technology provides unique value, enabling companies to adapt to changing market conditions and enhance their interactions with customers.
CONCLUSION
Integrating AI technologies into consumer behavior analysis allows businesses to adapt to rapidly changing market conditions. ML, NLP, and computer vision methods provide companies with powerful tools to identify customer needs and personalize their interactions with products and services. These approaches not only improve marketing strategy efficiency but also enhance customer satisfaction.
One of AI's key advantages is its ability to process large data sets and provide accurate forecasts, critical in a competitive environment. Clustering, recommendation systems, and graph analysis help businesses better understand behavioral patterns and develop tailored offerings for various audience segments. However, implementing these technologies requires addressing challenges such as high data and resource demands.
The further development of AI in consumer behavior analysis is linked to advanced applications of generative models and other innovative approaches. These open new opportunities for businesses but also necessitate adherence to ethical
standards and data protection. AI adoption will become a vital driver of sustainable business growth, enabling new models of customer interaction.
REFERENCES
1. Zhang C., Lu Y. Study on artificial intelligence: The state of the art and future prospects // Journal of Industrial Information Integration. 2021. Vol. 23. P. 100224.
2. Lu Y. Artificial intelligence: a survey on evolution, models, applications and future trends // Journal of Management Analytics. 2019. Vol. 6. No.1. P. 1-29.
3. Gu F. The prospect exploration of artificial intelligence technology and its application // Transactions on Computer Science and Intelligent Systems Research. 2024. Vol. 3. P. 45-50.
4. Davenport T., Guha A., Grewal D., Bressgott T. How artificial intelligence will change the future of marketing // Journal of the Academy of Marketing Science. 2020. Vol. 48. P. 24-42.
5. Barinova N.V., Barinov V.R. Transformation of consumer economic behavior in the digital world // Bulletin of Plekhanov Russian University of Economics. 2020. Vol. 17. No. 5 (113). P. 169-181.
6. Dolzhenko I.B. Digital technologies, artificial intelligence and consumer behavior // Modern Science. 2021. No.10-2. P. 60-66.
7. Telichko D.V., Matisin I.N. Influence of neural networks and artificial intelligence on consumer interest and behavior // Vector of Economics. 2021. No.1. P. 5-5.
8. Chachis D.Y. The role of artificial intelligence in forecasting consumer behavior // Bulletin of Science. 2024. Vol. 3. No. 10 (79). P. 865-868.
9. Gamayunova O.A. Use of machine learning and artificial intelligence in marketing: Forecasting consumer behavior and personalizing communication // Technologies, Machines, and Equipment for Designing and Building Agricultural Complexes. 2023. P. 123-126.
