Examination of the Criticality of Customer Segmentation Using Unsupervised Learning Methods

Circular Economy and Sustainability, Год журнала: 2024, Номер 4(2), С. 1447 - 1460

Опубликована: Янв. 9, 2024

Язык: Английский

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A Study on Machine Learning-Enhanced Roadside Unit-Based Detection of Abnormal Driving in Autonomous Vehicles

Electronics, Год журнала: 2024, Номер 13(2), С. 288 - 288

Опубликована: Янв. 8, 2024

Ensuring the safety of autonomous vehicles is becoming increasingly important with ongoing technological advancements. In this paper, we suggest a machine learning-based approach for detecting and responding to various abnormal behaviors within V2X system, system that mirrors real-world road conditions. Our including RSU, designed identify exhibiting driving. Abnormal driving can arise from causes, such as communication delays, sensor errors, navigation malfunctions, environmental challenges, cybersecurity threats. We simulated exploring three primary scenarios driving: overlapping vehicles, counterflow The applicability learning algorithms these anomalies was evaluated. Minisom algorithm, in particular, demonstrated high accuracy, recall, precision identifying vehicle overlaps, situations. Notably, changes vehicle’s direction its characteristics proved be significant indicators Basic Safety Messages (BSM). propose adding new element called linePosition BSM Part 2, enhancing our ability promptly detect address abnormalities. This addition underpins technical capabilities RSU systems equipped edge computing, enabling real-time analysis data appropriate responsive measures. emphasize effectiveness behavior offering ways enhance facilitate smoother traffic flow.

Язык: Английский

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The Future of Marketing: The Transformative Power of Artificial Intelligence

International Journal of Management and Administration, Год журнала: 2024, Номер 8(15), С. 1 - 19

Опубликована: Фев. 17, 2024

This research offers a rich narrative explaining this multifaceted relationship by exploring the transformative impact of Artificial Intelligence (AI) on marketing adopting qualitative descriptive approach for in-depth exploration. The findings reveal profound implications customer engagement, market strategy, and ethical considerations. integration AI into enables personalization increases brand loyalty. Predictive analytics enable businesses to develop proactive strategies aligned with future dynamics. Despite its advantages, considerations surrounding data privacy consumer consent require be used responsibly transparently. Integrated augmented reality, virtual predictive journeys, Internet Things that transform dynamics must harnessed balance concerns. A comprehensive resource academic researchers industry professionals, work provides clear roadmap organizations effectively leverage in their operations an environment increasing reliance digital platforms expanding availability.

Язык: Английский

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Assessing the intention to adopt computational intelligence in interactive marketing

Journal of Retailing and Consumer Services, Год журнала: 2024, Номер 78, С. 103765 - 103765

Опубликована: Фев. 24, 2024

Язык: Английский

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The Impact of AI-enabled CRM Systems on Organizational Competitive Advantage: A mixed-method approach using BERTopic and PLS-SEM

Heliyon, Год журнала: 2024, Номер 10(16), С. e36392 - e36392

Опубликована: Авг. 1, 2024

The recent advances in machine learning and deep algorithms, along with the advent of generative AI, have led AI to become "new normal" organizations. This trend has extended CRM, resulting development AI-enabled CRM systems, or AI-CRM. Despite growing adoption as part competitive strategies, many firms report minimal no positive effect on performance. study addresses research questions: "What are critical features AI-CRM systems?" "How do these impact organizational advantage?" To explore this, we aim identify key characteristics assess their In Study 1, utilize BERTopic topic modeling extract from user reviews. 2 employs PLS-SEM examine how influence advantage. 1 reveals four main (general, marketing, sales, service/support), each comprising distinct features. shows that differentially capability, significantly affecting performance findings offer valuable insights for both theory practice regarding effective use

Язык: Английский

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Deep Learning in Finance: A Survey of Applications and Techniques

AI, Год журнала: 2024, Номер 5(4), С. 2066 - 2091

Опубликована: Окт. 28, 2024

Machine learning (ML) has transformed the financial industry by enabling advanced applications such as credit scoring, fraud detection, and market forecasting. At core of this transformation is deep (DL), a subset ML that robust in processing analyzing complex large datasets. This paper provides comprehensive overview key models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Deep Belief (DBNs), Transformers, Generative Adversarial (GANs), Reinforcement Learning (Deep RL). Beyond summarizing their mathematical foundations processes, study offers new insights into how these models are applied real-world contexts, highlighting specific advantages limitations tasks algorithmic trading, risk management, portfolio optimization. It also examines recent advances emerging trends alongside critical challenges data quality, model interpretability, computational complexity. These can guide future research directions toward developing more efficient, robust, explainable address evolving needs sector.

Язык: Английский

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Customer segmentation in the digital marketing using a Q-learning based differential evolution algorithm integrated with K-means clustering

PLoS ONE, Год журнала: 2025, Номер 20(2), С. e0318519 - e0318519

Опубликована: Фев. 7, 2025

Effective and well-structured customer segmentation enables organizations to accurately identify comprehend the distinct characteristics needs of various groups, thereby facilitating development more targeted marketing strategies. Contemporary artificial intelligence technologies have emerged as predominant tools for segmentation, owing their robust capabilities in analyzing complex datasets extracting profound insights. This paper proposes a framework within realm digital marketing, which integrates reinforcement learning-based differential evolution algorithm with K -means clustering using dimensionality reduction techniques address challenges process. Initially, correlation matrix is used redundant noise multicollinear features feature Principal Component Analysis applied denoising enhance ability model potential features. Subsequently, parameter adaptive adjustment method based on Q -learning proposed, significantly augments performance -means. Ultimately, effectiveness proposed validated Kaggle dataset, elbow employed ascertain optimal number clusters. Based cluster category centers, typical different types are analyzed. Furthermore, four widely recognized machine learning methods classify results, achieving over 95% classification accuracy test set. The experimental results demonstrate that exhibits high degree characteristic identification not only enhances efficiency satisfaction but also fosters corporate profit growth through strategic formulation initiatives.

Язык: Английский

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Customer Segmentation

International Journal for Research in Applied Science and Engineering Technology, Год журнала: 2024, Номер 12(1), С. 1586 - 1591

Опубликована: Янв. 31, 2024

Abstract: Effective marketing involves targeting specific customer groups with personalized products, services, and campaigns, making segmentation a crucial strategy in modern business. This paper introduces pioneering method that utilizes machine learning techniques to accurately efficiently segment customers based on their behaviors, demographics, transaction history. By combining transfer learning, Rfm (recency, frequency, monetary) modeling, clustering algorithms like K-means, our approach generates meaningful segments, offering valuable insights for improved experiences. We showcase the positive impact of real-world dataset, displaying noteworthy enhancements effectiveness satisfaction.

Язык: Английский

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Bibliometric insights into the evolution of digital marketing trends

Innovative Marketing, Год журнала: 2024, Номер 20(2), С. 1 - 14

Опубликована: Апрель 1, 2024

This bibliometric analysis aims to delineate the progression of research in domain digital marketing by examining 513 English-language articles published Scopus during period 2003–2024. An examination scholarly productivity indicates an upward trend, as evidenced increase publications from one 2003 115 2022 and citations 79 1131 2021, determined keyword, citation, authorship analyses. A review citation patterns reveals that with significant impact are primarily found prestigious academic journals, such Industrial Marketing Management International Journal Research Marketing. Prominent contributors hail Jordan, Finland, Spain, United Arab Emirates, Saudi Arabia; among other regions – States, Middle East, Europe, Asia. Keyword revealed emphasis on emerging technologies artificial intelligence traditional techniques (e.g., social media, content marketing, internet marketing). Co-occurrence theme highlighted strategy, audiences, transformation business acceleration adoption a result COVID-19. Further areas investigation encompass optimizing utilization emergent media platforms, implementing virtual augmented reality enhance customer experience, capitalizing potential machine learning augment efficacy marketing. By utilizing data-driven insights, this study offers guidance for curricular enhancements, agendas, practice. AcknowledgmentThe author thanks everyone who helped make possible, but especially those at Ho Chi Minh University Banking, Vietnam.

Язык: Английский

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Exploring the Depths of Digital Marketing: A Systematic Literature Review on Segmentation, Targeting, Differentiation, and Positioning Strategies

International Journal of Business Law and Education, Год журнала: 2024, Номер 5(1), С. 1270 - 1283

Опубликована: Апрель 17, 2024

This research aims to find out in depth about digital marketing strategies, namely segmentation, targeting, positioning and differentiation using methods favorite announcing thing (Preferred Reporting Items) andmeta examinations (meta analysis) or commonly called the PRISMA method. 4 (four) journal websites, Google Scholar, Sciencedirect, Emerald, Taylor & Francis. The results of this show that segmentation a brand will have sustainable competitive advantage. A product advantage if is considered important unique by customers. Targeting process evaluating each segment's attractiveness then selecting one more characteristics serve. discusses issue how select, select reach market.

Язык: Английский

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