Artificial intelligence in e-commerce: automation, personalization, efficiency

The article provides a comprehensive analysis of the introduction of artificial intelligence in e-commerce with a focus on automation of operations, analytical tools and personalization of interaction with customers. The digital transformation of e-commerce determines the growing need for the introduction of innovative technologies that can not only optimize business processes, but also form an individual approach to each consumer. One of the key drivers of such changes is artificial intelligence (AI), which provides ample opportunities for automating operations, personalized communications and predictive analytics. The article analyzes the current directions of AI application in e-commerce, in particular: automatic order processing, intelligent inventory management, dynamic pricing, use of chatbots and recommendation systems. The concept of micropersonalization of interfaces and digital content based on cognitive thinking styles of users (visual, analytical, emotional-impulsive and logic-consistent) was first proposed. An adaptive model has been developed that integrates behavioral analytics tools, classification algorithms (K-means), deep learning (LSTM, CNN) and predictive optimization. A mechanism for ethical filtering of personalized content is proposed, which ensures compliance with the norms of responsible interaction with the user and the formation of trust in the brand. The proposed solution can be scalable for different e-commerce platforms. An interdisciplinary approach combining marketing, neural networks and behavioral economics was used. The methodological basis of the study was made up of interdisciplinary methods: system analysis, modeling of user behavior, content analysis, elements of cognitive psychology, machine learning, A/B testing of the effectiveness of models. The results of the research are of applied importance for digital marketing, UX/UI design and strategic planning, can be scaled into various e-commerce platforms, mobile applications and CRM systems. The proposed model combines innovation with practical suitability, promoting customer loyalty, conversion and competitiveness of e-commerce companies.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-30
DOI
https://doi.org/10.5281/zenodo.15148588
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
0.00
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article

Artificial intelligence in e-commerce: automation, personalization, efficiency

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

Artificial intelligence in e-commerce: automation, personalization, efficiency

article en

Abstract

The article provides a comprehensive analysis of the introduction of artificial intelligence in e-commerce with a focus on automation of operations, analytical tools and personalization of interaction with customers. The digital transformation of e-commerce determines the growing need for the introduction of innovative technologies that can not only optimize business processes, but also form an individual approach to each consumer. One of the key drivers of such changes is artificial intelligence (AI), which provides ample opportunities for automating operations, personalized communications and predictive analytics. The article analyzes the current directions of AI application in e-commerce, in particular: automatic order processing, intelligent inventory management, dynamic pricing, use of chatbots and recommendation systems. The concept of micropersonalization of interfaces and digital content based on cognitive thinking styles of users (visual, analytical, emotional-impulsive and logic-consistent) was first proposed. An adaptive model has been developed that integrates behavioral analytics tools, classification algorithms (K-means), deep learning (LSTM, CNN) and predictive optimization. A mechanism for ethical filtering of personalized content is proposed, which ensures compliance with the norms of responsible interaction with the user and the formation of trust in the brand. The proposed solution can be scalable for different e-commerce platforms. An interdisciplinary approach combining marketing, neural networks and behavioral economics was used. The methodological basis of the study was made up of interdisciplinary methods: system analysis, modeling of user behavior, content analysis, elements of cognitive psychology, machine learning, A/B testing of the effectiveness of models. The results of the research are of applied importance for digital marketing, UX/UI design and strategic planning, can be scaled into various e-commerce platforms, mobile applications and CRM systems. The proposed model combines innovation with practical suitability, promoting customer loyalty, conversion and competitiveness of e-commerce companies.

Zenodo (CERN European Organization for Nuclear Research)
Vasyl' Stus Donetsk National University (UA), State University of Trade and Economics (UA)
Industry, innovation and infrastructure
Openalex Percentile: Top 53%
Geochemistry and Geologic Mapping
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