Intelligent customer complaint handling utilising principal component and data envelopment analysis (PDA)

Faed, A; Chang, E; Saberi, M; Hussain, OK; Azadeh, A

Intelligent customer complaint handling utilising principal component and data envelopment analysis (PDA)

Faed, A Chang, E Saberi, M

Hussain, OK Azadeh, A

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Publisher:: ELSEVIER SCIENCE BV
Publication Type:: Journal Article
Citation:: Applied Soft Computing Journal, 2016, 47, pp. 614-630
Issue Date:: 2016-10-01

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Field	Value	Language
dc.contributor.author	Faed, A
dc.contributor.author	Chang, E
dc.contributor.author	Saberi, M https://orcid.org/0000-0002-5168-2078
dc.contributor.author	Hussain, OK
dc.contributor.author	Azadeh, A
dc.date.accessioned	2022-08-09T07:08:33Z
dc.date.available	2022-08-09T07:08:33Z
dc.date.issued	2016-10-01
dc.identifier.citation	Applied Soft Computing Journal, 2016, 47, pp. 614-630
dc.identifier.issn	1568-4946
dc.identifier.issn	1872-9681
dc.identifier.uri	http://hdl.handle.net/10453/159811
dc.description.abstract	In this study, we consider customer to be a company's crucial asset. In order to have a fast, efficient decision-making process, it is vital that a customer relationship management (CRM) decision-maker condenses and abstracts the existing information. A questionnaire survey was conducted among respondents in order to obtain the required data. The questionnaire contains nine categories of satisfaction variables. To perform the analysis, we used principal component analysis (PCA) and data envelopment analysis (DEA). PDA has been utilised as an abbreviation for the integration of these two methods. To effectively analyse the procedure, PCA was utilised to assign a number to each category of questions related to each satisfaction variable. To achieve optimal precision, DEA was applied to the three categories of customers (‘most important’, ‘important’ and ‘ordinary’ customers) in order to determine the strengths and weaknesses of customer services from these customers’ perspectives. Customers were clustered and then DEA was used to determine their viewpoints. Using DEA, we have optimised our recognition of customers’ complaints and then provided recommendations and remedial actions to resolve the current issues in logistics and transport industry in general, and at Fremantle port in particular. Significance The current study integrates soft computing and optimisation technique in order to build the CRM recommender system. It demonstrates the hybrid soft computing strengthens in area of CRM as the relevance solution. The significance of the proposed algorithm is three fold. First, it integrates soft computing and optimisation technique in order to build the CRM recommender system. Second, it utilises the most standard CRM variables in its decision making process. Third, it is an optimising algorithm because it integrates DEA with PCA technique.
dc.language	English
dc.publisher	ELSEVIER SCIENCE BV
dc.relation.ispartof	Applied Soft Computing Journal
dc.relation.isbasedon	10.1016/j.asoc.2015.02.018
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Intelligent customer complaint handling utilising principal component and data envelopment analysis (PDA)
dc.type	Journal Article
utslib.citation.volume	47
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2022-08-09T07:08:32Z
pubs.publication-status	Published
pubs.volume	47

Abstract:

In this study, we consider customer to be a company's crucial asset. In order to have a fast, efficient decision-making process, it is vital that a customer relationship management (CRM) decision-maker condenses and abstracts the existing information. A questionnaire survey was conducted among respondents in order to obtain the required data. The questionnaire contains nine categories of satisfaction variables. To perform the analysis, we used principal component analysis (PCA) and data envelopment analysis (DEA). PDA has been utilised as an abbreviation for the integration of these two methods. To effectively analyse the procedure, PCA was utilised to assign a number to each category of questions related to each satisfaction variable. To achieve optimal precision, DEA was applied to the three categories of customers (‘most important’, ‘important’ and ‘ordinary’ customers) in order to determine the strengths and weaknesses of customer services from these customers’ perspectives. Customers were clustered and then DEA was used to determine their viewpoints. Using DEA, we have optimised our recognition of customers’ complaints and then provided recommendations and remedial actions to resolve the current issues in logistics and transport industry in general, and at Fremantle port in particular. Significance The current study integrates soft computing and optimisation technique in order to build the CRM recommender system. It demonstrates the hybrid soft computing strengthens in area of CRM as the relevance solution. The significance of the proposed algorithm is three fold. First, it integrates soft computing and optimisation technique in order to build the CRM recommender system. Second, it utilises the most standard CRM variables in its decision making process. Third, it is an optimising algorithm because it integrates DEA with PCA technique.

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http://hdl.handle.net/10453/159811