Due to the rapid development of artificial intelligence (AI) technologies, marketing professionals are faced with a changing landscape that requires a comprehensive understanding of the potential and challenges of AI applications. Quantum computing processes are the basis for the future development of predictive analytics, as their ability to simultaneously provide many variants of events with different variations of their occurrence allows achieving reliability of up to 99.75%, even in conditions of irrationality. The relevance of the research is related to the state priority for the development of quantum technologies, as well as technological advances, in particular, ease of use and accuracy of the data obtained. The research is aimed at determining the combination of AI technologies with the use of quantum computing processes that is most suitable for the tasks of marketing analytics in order to increase the accuracy of forecasting consumer behavior and market mechanisms in the long term. The methodological basis of the research was scientific work in the field of marketing strategies, artificial intelligence, as well as descriptions of the basics of quantum computers, practical work of leading centers for the implementation of quantum computing in business processes. The theoretical basis is bibliometric analysis, theory of quantitative methods, theory of innovation. For the purposes of the research, literature was analyzed and various AI software products were systematized, a list of cases was provided and practical results in the field of quantum computing application in predictive analytics in the field of marketing were compared. Their impact on such marketing components as market analysis and forecasting, audience sentiment analysis, and offer personalization is assessed. The information base consisted of a study of 160 articles written in the field of predictive analytics based on AI for marketing purposes, as well as an assessment of 26 cases of the use of quantum computing in business processes both in Russia and abroad. The key focus of the research is a critical assessment of the benefits of implementing artificial intelligence technologies in conjunction with quantum computing processes in shaping marketing strategies, including long—term forecasts of consumer behavior and changes in market mechanisms. The result of the research is a framework for the introduction of artificial intelligence technologies in conjunction with quantum computing processes in the formation of marketing strategies.
Due to the rapid development of artificial intelligence (AI) technologies, marketing professionals are faced with a changing landscape that requires a comprehensive understanding of the potential and challenges of AI applications. Quantum computing processes are the basis for the future development of predictive analytics, as their ability to simultaneously provide many variants of events with different variations of their occurrence allows achieving reliability of up to 99.75%, even in conditions of irrationality. The relevance of the research is related to the state priority for the development of quantum technologies, as well as technological advances, in particular, ease of use and accuracy of the data obtained. The research is aimed at determining the combination of AI technologies with the use of quantum computing processes that is most suitable for the tasks of marketing analytics in order to increase the accuracy of forecasting consumer behavior and market mechanisms in the long term. The methodological basis of the research was scientific work in the field of marketing strategies, artificial intelligence, as well as descriptions of the basics of quantum computers, practical work of leading centers for the implementation of quantum computing in business processes. The theoretical basis is bibliometric analysis, theory of quantitative methods, theory of innovation. For the purposes of the research, literature was analyzed and various AI software products were systematized, a list of cases was provided and practical results in the field of quantum computing application in predictive analytics in the field of marketing were compared. Their impact on such marketing components as market analysis and forecasting, audience sentiment analysis, and offer personalization is assessed. The information base consisted of a study of 160 articles written in the field of predictive analytics based on AI for marketing purposes, as well as an assessment of 26 cases of the use of quantum computing in business processes both in Russia and abroad. The key focus of the research is a critical assessment of the benefits of implementing artificial intelligence technologies in conjunction with quantum computing processes in shaping marketing strategies, including long—term forecasts of consumer behavior and changes in market mechanisms. The result of the research is a framework for the introduction of artificial intelligence technologies in conjunction with quantum computing processes in the formation of marketing strategies.
This work investigates the transformative role of quantum computing in personalized digital marketing, emphasizing its capacity to enhance hyper-personalized customer engagement through advanced predictive analytics and optimization techniques. This work employs a qualitative research methodology through a comprehensive literature review to identify critical research gaps regarding how quantum computing can optimize marketing strategies, improve data-driven decision-making, and refine recommender systems. Relevant publications were systematically searched in academic databases such as ScienceDirect, IEEE Xplore, Springer, Wiley, and Emerald. The analysis focused on topics related to quantum computing, predictive analytics, marketing optimization, and consumer behaviour. This study reveals that quantum computing holds significant promise for personalized digital marketing, notably through the application of quantum-inspired techniques like digital annealers. These technologies can enhance online advertisement conversion rates, improving accuracy from 0.176 to 0.326, and refine recommender systems, fostering hyper-personalized customer engagement. Despite these advantages, the incorporation of quantum technology into present marketing frameworks faces challenges such as high implementation costs and a shortage of skilled personnel. Additionally, the findings underscore the need for organizations to navigate complexities inherent in managing vast datasets, which quantum computing can address, thereby revolutionizing market research and strategic decision-making processes. This study contributes to the key benefits of quantum computing in personalized digital marketing, emphasizing the need for further empirical research to validate theoretical insights. It opens avenues for future studies to investigate practical applications of quantum technologies across diverse marketing contexts, addressing the gaps in existing literature. The transformative impact of quantum computing on marketing strategies, this chapter encourages scholars to explore its implications for consumer behaviour, data privacy, and algorithmic fairness. This work contributes to the understanding of quantum computing in reshaping personalized digital marketing. It highlights the convergence of quantum computing, artificial intelligence, and big data analytics, offering a roadmap for upcoming empirical research and real-world practical applications.
Quantum computing is transforming consumer behavior analysis and market research, introducing a paradigm shift in predictive analytics and decision intelligence. This paper presents a theoretical framework integrating quantum computing principles with consumer decision models, addressing the complexity and uncertainty of consumer choices. By leveraging quantum probability, superposition, and entanglement, this study enhances market research methodologies, allowing businesses to analyze multi-dimensional consumer data with superior accuracy. The article introduces Quantum-Like Bayesian Networks and Markov Quantum Decision Models, providing a context-aware, probabilistic alternative to traditional predictive models. These frameworks capture behavioral inconsistencies, real-time decision shifts, and contextual influences, redefining consumer insights. Additionally, this study explores quantum sentiment analysis, quantum-enhanced predictive modeling, and real-time data processing, establishing quantum computing as a disruptive force in market intelligence. Beyond analytical advancements, the paper examines quantum cryptography for secure consumer data processing, quantum blockchain for consumer trust, and quantum-driven personalization strategies, demonstrating their potential for revolutionizing engagement and decision automation. Finally, the article addresses the ethical and operational challenges of quantum-driven consumer research, outlining future research directions and real-world applications. As quantum technology advances, it is poised to reshape predictive analytics, decision intelligence, and strategic marketing, offering businesses and researchers an unprecedented tool for next-generation market research..
This study investigates the prospective influence of quantum computing on marketing analytics. As the landscape of digital marketing continues to evolve, the need for more effective and precise analytical tools has surged to unprecedented levels. Marketing analytics, which is essential for assessing the success of campaigns, is undergoing significant changes driven by the increasing volume of data, the complexity of consumer interactions, and the urgent demand for immediate insights. Quantum computing, founded on the principles of quantum mechanics, is leading this transformation. Unlike classical computing, which depends on conventional bits, quantum computing employs quantum bits (qubits) that can exist in multiple states simultaneously, thus allowing for rapid data processing and the resolution of complex problems. This research delves into the theoretical applications of quantum computing within marketing analytics, concentrating on areas such as data processing, predictive modeling, and the development of personalized marketing strategies. It emphasizes the potential for better management of extensive datasets, improved predictive accuracy, real-time analytical capabilities, and a reimagining of marketing strategies. The prospects seem favorable, yet the study also highlights the challenges that arise from the early stage of quantum technology and the complexities of its integration. Although it is primarily theoretical, this research shows the considerable potential for transformation that quantum computing offers in marketing analytics, encouraging further exploration and experimentation in this field.
This chapter examines how quantum computing has impacted predictive analytics in neuromarketing in a significant way. It talks about how marketers understanding, and prediction of consumer behaviour is changing as a result of quantum algorithms' capacity to process large and complex datasets. The chapter explores how quantum AI is being applied in the real world and highlights businesses that have effectively used this technology to get more precise predictive insights. Furthermore, it provides guidelines for responsible implementation and discusses ethical issues related to the use of quantum AI such as privacy and data transparency. In order to improve consumer engagement strategies, the chapter also looks at interdisciplinary approaches that combine quantum AI with marketing and neuroscience. With an eye toward the future the chapter projects how quantum-enhanced predictive analytics will impact neuromarketing over the next ten years by identifying emerging trends possible obstacles and opportunities.
The digital ecosystem continues to expand around the world and is revolutionising the way markets are researched. Indeed, consumer experiences are advertised and disseminated through so many channels and media that it has become a major challenge for researchers and marketing practitioners to collect, process and generate valuable information to support strategic and operational decisions. In this article, the authors explore how advances in quantum computing, which can be used to process huge amounts of data quickly and accurately, could offer an unprecedented opportunity for researchers to address the challenges of the digital ecosystem. Three studies are presented to define the state of the art and future expectations of quantum computing in market research and business. By means of a bibliometric analysis of 209 publications and a content analysis of the 30 highest-impact articles, we describe the present landscape, and also forecast the future with the help of in-depth interviews with eight experts. The findings reveal that the US and China are at the forefront of scientific development, but the contributions from four other countries (India, the UK, Canada and Spain) are also in double figures. However, graphical analysis identifies four poles of development: the US orbit, which includes Canada and Spain; the Chinese orbit, which includes India; the UK orbit; and the Australian orbit. In terms of expectations, the experts agree on the opportunities offered by quantum computing, but there is less consensus as to how long it will take to develop.
Purpose: The aim of the article is to present potential applications of Quantum Artificial Intelligence (QAI) in enhancing Business Process Management (BPM), with a particular focus on predictive analytics. Design/methodology/approach: The primary research methods include a critical analysis of the literature. Deep neural network testing was also conducted to identify efficient predictors and detectors for BPM systems. In addition, intensive computational experiments were carried out to analyze the quality of solutions defined by the proposed quantum-inspired algorithms. Findings: The results of theoretical research and numerical experiments confirmed that QAI technology, developed within cutting-edge machine learning models and natural language processing (NLP), has a significant impact on enhancing BPM. In the context of predictive analytics in BPM systems, quantum predictive models trained on neural networks play a key role in increasing forecast accuracy. Furthermore, leveraging the power of NLP for task automation enables to extract insights and optimize processes, ultimately achieving business objectives more efficiently. Practical implications: The obtained results form the basis for recommendations on how QAI models, developed within machine learning and NLP, can enhance not only current business processes but, most importantly, predictive analysis. Social implications: By implementing QAI effectively and consistently, organizations can improve decision-making based on precise data analysis rather than relying on intuition or the experience of decision-makers. Originality/value: The authenticity of the research results stems from clear ideas for the effective use of quantum technologies developed in the fields of machine learning and NLP to enhance BPM, particularly in the area of predictive analytics.