Hybrid cluster analysis of customer segmentation of sea transportation users

Cahyana, Bambang Eka; Nimran, Umar; Utami, Hamidah Nayati; Iqbal, Mohammad

doi:10.1108/jefas-07-2019-0126

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…Moreover, the cluster analysis offers several insightful information about the consumers’ patterns, which is not available in other statistical techniques. Similarly, Cahyana et al (2020) added that there are several crucial applications offered by this analysis, including: marker segmentation; understanding buying behaviour; and identifying the opportunity. …”

Section: Resultsmentioning

confidence: 99%

The anatomy of non-Muslim consumers’ halal fashion buying behaviour: a quantitative approach

Tarofder

Sultana

Raisal

et al. 2021

JIMA

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Purpose The purpose of this study is two-fold: classifying non-Muslim halal fashion buyers by applying quantitative techniques and identifying the persuading determinants of the non-Muslim women’ halal fashion buying behaviour (HFBB). Design/methodology/approach By adapting items from prior studies, a structured questionnaire was developed and distributed face-to-face to various Muslim fashion stores in Malaysia. After a one-month effort, 221 responses were obtained from non-Muslim consumers by using convenience sampling. Next, a clustering analysis was used to classify them from a contrasting perspective. Finally, regression and Andrew F. Hayes’s process procedures were applied to examine the three independent variables’ effect and the moderating variables. Findings The results revealed the characteristic behaviour of the non-Muslim women explicitly, which is related to their halal fashion purchasing decision. Based on the ANOVA results, there were different motives for buying halal fashion by non-Muslim women. Additionally, it was found that the most crucial determinants for non-Muslim’s HFBB are “cultural adaptation”, albeit, there is no substantial proof of a significant moderating effect of age and income on the consumers. Research limitations/implications These discoveries are advantageous for halal fashion retailers and provide an appealing domain for further investigations in the context of the global halal study. Practical implications This study provided an idea for an untapped segment on the halal fashion sellers’ segmentation and positioning strategy. The study’s results suggested specific managerial and practical recommendation that the sellers can use to attract non-Muslim consumers. Originality/value This study was amongst the uncommon investigations within the halal fashion context that will enlighten the managers’ selling strategy on the most neglected market segment. The results of this study provided an empirical understanding of how to sell halal fashion to non-Muslim consumers.

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Section: Resultsmentioning

confidence: 99%

The anatomy of non-Muslim consumers’ halal fashion buying behaviour: a quantitative approach

Tarofder

Sultana

Raisal

et al. 2021

JIMA

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“…It is more efficient to apply cluster analysis as a preliminary phase with a subsequent portfolio optimization using the Markowitz model. (Cahyana et al 2020) used hybrid cluster analysis for the classification of customers of PT Pelindo I based on their satisfaction with the services offered by PT Pelindo I. Cluster analysis was performed for the purposes of grouping the research objects based on their characteristic similarities.…”

Section: Literature Researchmentioning

confidence: 99%

Artificial Intelligence for Cluster Analysis: Case Study of Transport Companies in Czech Republic

Kalinová

2021

JRFM

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What is the situation of the transport sector in the Czech Republic and what is its importance for the economy of the Czech Republic? How and to what extent do businesses operating in this sector influence the sector as such, and how many businesses in this sector have such influence? Additionally, what happens if the most important businesses in the transport sector go bankrupt, and which businesses are the most important ones? Searching for the answers to these questions is a subject of this contribution, focusing primarily on the cluster analysis using artificial neural networks (ANN), specifically with Kohonen networks, which represent the main method for processing a large volume of not only accounting data on transport companies. In this research, the dataset consists of the financial statements of transport companies for the years 2015–2018. The research part of the contribution deals mainly with the issue of the transport sector’s development in the years 2015–2018 with the companies operating in this sector and tries to identify the most important companies in terms of their importance for this sector. The results show that the whole transport sector is influenced mainly by the two largest companies, whose potential changes can affect companies themselves but to a great extent also the development of the whole transport sector. For the two companies, financial analysis is carried out using ratios, whose results show that despite the negative values of the important value generators of one of these companies, the company is still able to significantly influence the situation in the transport sector of the CR. This information is a clear guide for experts, development analysts, to determine the further development of the whole sector when focusing on the development of the two specific companies only. A question arises as to how the created model can be applied to other economic sectors, especially in other EU countries.

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“…An overall score can be computed by assigning weights to each of the RFM values. Equal weights are assigned to the Key Performance Indicators (KPIs) in [2] and [3]. Random weights could be assigned to these values in the case of neural networks, but no research has been able to differentiate between the attributes of recency, frequency and monetary value to determine which of those is more important than the other.…”

Section: Introductionmentioning

confidence: 99%

Customer Segmentation using Machine Learning

Jaiswal

Supraja

et al. 2023

2023 Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT)

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Customer segmentation has seen major growth in all sectors in the last decade. Several techniques have been devised to analyze customer behavior through loyalty, purchases, recency, frequency and monetary to develop efficient marketing strategies that cater to each client individually. As the availability of products and services increases, so does the competition. With the spiraling of automation accompanied by its cost-effectiveness and ease of availability, all businesses equip themselves with the required workforce and machinery to conduct experiments such as customer segmentation on an industrial scale. In the proposed work, the datasets are manipulated by extracting features from existing attributes. A widespread approach is RFM that calculates the Recency, Frequency and Monetary values for each customer tuple. This paper aims at laying out a new approach at every step of customer segmentation from pre-processing, clustering, validation and suggesting marketing strategies for customer retention. Two datasets-Online Retail II Set and Mall Customer Segmentation are modelled and the results from analysis of both the datasets are presented and compared to reach a generalized opinion.

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Hybrid cluster analysis of customer segmentation of sea transportation users

Cited by 6 publications

References 2 publications

The anatomy of non-Muslim consumers’ halal fashion buying behaviour: a quantitative approach

The anatomy of non-Muslim consumers’ halal fashion buying behaviour: a quantitative approach

Artificial Intelligence for Cluster Analysis: Case Study of Transport Companies in Czech Republic

Customer Segmentation using Machine Learning

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