A Customer Profiling’ Machine Learning Approach, for In-store Sales in Fast Fashion

Fares, Naila; Lebbar, Maria; Sbihi, Najiba

doi:10.1007/978-3-030-11928-7_53

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…, 2020). The situation is even more challenging for fast-fashion retail due to its varied customer profiles (Fares et al. , 2018b) and supply chain complexities (Fares and Lebbar, 2019).…”

Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

“…As a result, a huge demand-supply gap in goods distribution has been observed . The situation is even more challenging for fast-fashion retail due to its varied customer profiles (Fares et al, 2018b) and supply chain complexities (Fares and Lebbar, 2019). Western countries were around two months behind the outbreak in China before the worldwide spread due to globalisation and trading (Fernandes, 2020).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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Enablers of post-COVID-19 customer demand resilience: evidence for fast-fashion MSMEs

Fares

Lloret

Kumar

et al. 2022

BIJ

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PurposeThis study aims to analyse the resilience of customer demand management post-coronavirus disease 2019, using fast fashion as an example. The paper provides insights for potential applications to micro-, small and medium enterprises (MSMEs).Design/methodology/approachBased on the qualitative analysis and an integrated Plan-Do-Check-Act (PDCA)-decision making trial and evaluation laboratory (DEMATEL)-fuzzy technique for order of preference by similarity to the ideal solution (TOPSIS) methodology of fuzzy multi-criteria decision-making, we explored and prioritised the enablers of resilience management for fast-fashion MSMEs.FindingsThe results reveal that the highest priority enabler is maintaining customer loyalty. Other enablers are associated with e-commerce endorsement, a customer-focussed assortment of items and flexible store operations.Research limitations/implicationsThe study findings will enable fast-fashion MSMEs to develop effective actions and priorities in operations efforts to promote post-pandemic recovery.Originality/valueDespite the importance of the resilience project and the changing fast-fashion customer patterns, only a handful of studies have explored how resilience can be managed in this field. Thus, the findings can contribute to closing this gap in the context of operations resilience research as well as MSME operations.

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“…, 2020). The situation is even more challenging for fast-fashion retail due to its varied customer profiles (Fares et al. , 2018b) and supply chain complexities (Fares and Lebbar, 2019).…”

Section: Theoretical Background and Literature Reviewmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

Enablers of post-COVID-19 customer demand resilience: evidence for fast-fashion MSMEs

Fares

Lloret

Kumar

et al. 2022

BIJ

View full text Add to dashboard Cite

show abstract

“…Classification techniques such as Naive Bayes, Random Forest, and Decision Tree are used to identify suitable customers for offering banking products [6]. For fashion customers, profiling is very helpful in determining the most purchased products related to the current season [7]. Additionally, consumer profiling to determine their opinions regarding a product can help the industry build a more intimate relationship with the consumers for the products or services offered [8].…”

Section: A Common Cases Profilingmentioning

confidence: 99%

Method to Profiling the Characteristics of Indonesian Dangdut Songs, Using K-Means Clustering and Features Fusion

Mahardhika,

Warnars,

Nugroho

et al. 2023

IJCDS

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There have been numerous studies that discuss profiling for various subjects, including criminal profiling, consumer profiling, and employee profiling, among others. However, song profiling is a relatively rare and underexplored area. In fact, profiling songs can provide us with new insights. Dangdut, one of the most popular musical genres in Indonesia, is a unique blend of musical rhythms from Arabic, Malay, Indian, and local music, and has the ability to captivate listeners and get them dancing and swaying along. In this study, we utilized feature selection techniques and feature fusion in conjunction with the K-Means clustering method to profile 281 Dangdut songs into two groups of clusters, with the best Silhouette score of 0.646. Additionally, we compared our method with non-Dangdut song data and obtained a Silhouette score of 0.549.

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An inclusive survey on machine learning for CRM: a paradigm shift

Singh¹,

Singh

Gupta³

2020

Decision

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A Customer Profiling’ Machine Learning Approach, for In-store Sales in Fast Fashion

Cited by 6 publications

References 8 publications

Enablers of post-COVID-19 customer demand resilience: evidence for fast-fashion MSMEs

Enablers of post-COVID-19 customer demand resilience: evidence for fast-fashion MSMEs

Method to Profiling the Characteristics of Indonesian Dangdut Songs, Using K-Means Clustering and Features Fusion

An inclusive survey on machine learning for CRM: a paradigm shift

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