Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Goti, Aitor; Querejeta-Lomas, Leire; Almeida, Aitor; de la Puerta, José Gaviria; López-de-Ipiña, Diego

doi:10.3390/math11132943

Cited by 5 publications

(3 citation statements)

References 258 publications

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“…The Lime-based Explainability module derives feature importance scores using a perturbation technique. The scores are mathematically represented in Equation (11).…”

Section: Interpretability Through Limementioning

confidence: 99%

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A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing

Talaat,

Aljadani,

Alharthi

et al. 2023

Mathematics

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In the evolving landscape of targeted marketing, integrating deep learning (DL) and explainable AI (XAI) offers a promising avenue for enhanced customer segmentation. This paper introduces a groundbreaking approach, DeepLimeSeg, which synergizes DL methodologies with Lime-based Explainability to segment customers effectively. The approach employs a comprehensive mathematical model to harness demographic data, behavioral patterns, and purchase histories, categorizing customers into distinct clusters aligned with their preferences and needs. A pivotal component of this research is the mathematical underpinning of the DeepLimeSeg approach. The Lime-based Explainability module ensures that the segmentation results are accurate and interpretable. The mathematical rigor facilitates businesses tailoring their marketing strategies with precision, optimizing sales outcomes. To validate the efficacy of DeepLimeSeg, we employed two real-world datasets: Mall-Customer Segmentation Data and an E-Commerce dataset. A comparative analysis between DeepLimeSeg and the traditional Recency, Frequency, and Monetary (RFM) analysis is presented. The RFM analysis, grounded in its mathematical modeling, segments customers based on purchase recency, frequency, and monetary value. Our preprocessing involved computing RFM scores for each customer, followed by K-means clustering to delineate customer segments. Empirical results underscored the superiority of DeepLimeSeg over other models in terms of MSE, MAE, and R2 metrics. Specifically, the model registered an MSE of 0.9412, indicative of its robust predictive accuracy concerning the spending score. The MAE value stood at 0.9874, signifying minimal deviation from actual values. This paper accentuates the importance of mathematical modeling in enhancing customer segmentation. The DeepLimeSeg approach, with its mathematical foundation and explainable AI integration, paves the way for businesses to make informed, data-driven marketing decisions.

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“…The Lime-based Explainability module derives feature importance scores using a perturbation technique. The scores are mathematically represented in Equation (11).…”

Section: Interpretability Through Limementioning

confidence: 99%

“…A preeminent XAI technique, Lime elucidates machine learning predictions with local and interpretable explanations. In contrast, deep learning continues to demonstrate exemplary performance across diverse tasks, including customer segmentation [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing

Talaat,

Aljadani,

Alharthi

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…In manufacturing, AIpowered systems are optimizing production processes by increased productivity, decreased expenses, enhanced quality, and decreased downtime [12]. In retail, AI and ML technologies are utilized for personalized marketing, content preferences, or adjust pricing based on inthemoment comparisons of rival products, reach out to potential customers with attractive commercials and valuable personalized content at more convenient times [13,14]. The entertainment industry is leveraging AI and ML for content recommendation systems, enabling personalized content suggestions to users on streaming platforms [15].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence and Machine Learning: A Review of State-of- the-Art Trends, Global Developments, and Practical Implications

2024

J Curr Trends Comp Sci Res

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This paper presents a comprehensive review of the latest trends, global developments, and practical implications in the field of Artificial Intelligence (AI) and Machine Learning (ML). The introduction highlights the transformative impact of AI and ML across various sectors, including healthcare, finance, transportation, manufacturing, retail, and entertainment. The purpose of the scientific research is to analyze the scientific, knowledge, and practical significance of AI and ML developments, emphasizing their potential impact on diverse domains. The study encompasses three main parts: state-ofthe- art trends, global developments, and practical implications. The scientific and practical importance of this paper lies in its examination of the growth and transformative potential of AI and ML technologies. The research methodology involves a comprehensive literature review, covering academic journals, conference proceedings, and reputable research papers and reports, to provide valuable insights and advancements in the field. The key findings of the paper revolve around significant trends in AI and ML, such as the increasing adoption of deep learning techniques and the integration of AI with big data analytics. Additionally, the study highlights global developments in AI research and investments from countries like the United States, China, Canada, Japan, South Korea, and the European Union. Practical implications include improved disease diagnosis and personalized treatment plans in healthcare, enhanced fraud detection and risk assessment in finance, and the use of AI-powered virtual assistants in customer service interactions. The research brings practical relevance to industries and policymakers, demonstrating the transformative potential of AI and ML in reshaping sectors and improving various aspects of human life. The paper concludes with recommendations for establishing robust frameworks to govern the responsible development and deployment of AI and ML systems, ensuring their long-term viability and beneficial impact on society.

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Role of artificial intelligence and augmented reality in fashion industry from consumer perspective: Sustainability through waste and return mitigation

Karadayi-Usta

2024

Engineering Applications of Artificial Intelligence

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Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Cited by 5 publications

References 258 publications

A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing

A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing

Artificial Intelligence and Machine Learning: A Review of State-of- the-Art Trends, Global Developments, and Practical Implications

Role of artificial intelligence and augmented reality in fashion industry from consumer perspective: Sustainability through waste and return mitigation

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