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

• Determine the strengths and weaknesses of customer services based on statistical and mathematical method.
• Analysis CRM system based on most important’, ‘important’ and ‘ordinary’ customers.
• Study CRM system in important Australian port.
• Using DEA to optimised recognition of customers’ complaints.
• Using PCA in order to modelling in a optimised space.

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.SignificanceThe 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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