Applying data mining on customer relationship management system for leisure coffee‐shop industry: a case study in Taiwan

Chiang, Wen-Yu

doi:10.1002/widm.1084

Cited by 8 publications

(10 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Han et al (2012) also pointed out that customer value is profit of customer to business. Thus, assessment of customer value consists of some attributes that can be used to calculate customers' past or expected future profits (Chiang, 2013).…”

Section: K 473mentioning

confidence: 99%

Identifying high-value airlines customers for strategies of online marketing systems

Chiang

2017

Self Cite

View full text Add to dashboard Cite

Purpose The purpose of this study is to discover valuable customers for enterprises. The international market of Taiwan airlines can be enhanced; thus, this study aims at Taiwan’s airline market as a research area. Design/methodology/approach This research uses data mining technologies with a proposed model to analyse airline customer values for big data online marketing systems, such as customer relationship management (CRM) system. The research applied supervised apriori algorithm, socio-economic variables and a proposed model to discover the rules/markets. Findings The results show that eight markets were discovered and three association rules were established for business systems of airlines. Originality/value The valuable travellers/markets can be discovered by this research. By collecting shoppers’ transactional data, global online CRM and point of sales (POS) systems can be big data marketing systems. The research framework can be easily applied in online CRM/POS or big data marketing systems for international airlines; however, it is for other global businesses as well.

show abstract

Section: K 473mentioning

confidence: 99%

Identifying high-value airlines customers for strategies of online marketing systems

Chiang

2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…Also pioneering works are Refs , where the interpretation linking the model and the data is based on user‐predefined labels. On the contrary, use fuzzy clustering and partitioning techniques to define the labels, with the objective of finding rules with better support/accuracy, giving more importance to quality than to understandability of the rules. Finding a tradeoff between both criteria is the objective in Ref , where an extension of the Equi‐depth (EDP) algorithm for mining fuzzy association rules involving quantitative attributes is presented.…”

Section: Finding Associations In Fuzzy Transactionsmentioning

confidence: 99%

Fuzzy frameworks for mining data associations: fuzzy association rules and beyond

Marín

Ruiz

Sánchez

2016

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Looking for associations in data is one of the data mining tasks that has aroused more interest in the literature. In this area, incorporating concepts of fuzzy set theory is useful in problems where imprecision and/or uncertainty appear. In most of the existing approaches, fuzzy association rules are widely seen as fuzzy rules, which are very different in nature from association rules, so problems like fuzzy inclusion and cardinality have been seldom taken into account explicitly, mostly providing ad hoc solutions for capturing semantics. In contrast, in this study we have taken the more general and natural approach of considering the elements of the association rule mining framework and studying possible and sensible fuzzy extensions, referred here as fuzzy frameworks for mining associations. As fuzzy frameworks are abstract mathematical models, another key contribution of the article is the notion of interpretations as mappings between fuzzy frameworks and specific datasets. This general analysis of the field is completed with a study of various important aspects that arise when proposing quality measures in a fuzzy environment, as well as those related to computational issues. The work also includes a review of the fuzzy framework that arises from fuzzy transactions regarded as fuzzy subsets of items, and shows that many of the approaches on fuzzy association rules that exist in the literature can be placed in the context of a proper interpretation of that framework. WIREs Data Mining Knowl Discov 2016, 6:50–69. doi: 10.1002/widm.1176 This article is categorized under: Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Technologies > Association Rules

show abstract

“…Generally, airlines or other businesses can employ a variety of marketing projects on traveler or customer segmentations to extend their travelers' or customers' life cycle (Berry and Linoff, 2004). Since airline businesses are service businesses, the model of airlines' customer value model can be modified according to the product's features of airlines for obtaining the higher customer values (Chiang, 2013).…”

Section: Background Of the Empirical Casementioning

confidence: 99%

Applying data mining with a new model on customer relationship management systems: a case of airline industry in Taiwan

Chiang

2014

Transportation Letters

Self Cite

View full text Add to dashboard Cite

The purpose of this research is to mine high-value family travelers for CRM systems of online airlines and travel agencies. This research uses the data mining technologies to analyze the online travel market, which consists of clustering, decision tree, and analytic hierarchy process (AHP) procedure with a proposed model. In the research, the market of online air travel (ticket or package) shoppers is divided into six markets. The markets can be ranked via AHP procedure. The study also applies the C5?0 decision tree algorithm on the discovered ranked markets, transactional variables, and socioeconomic variables to create four useful classification rules. The discovered rules can be employed in web-based customer relationship management (CRM) marketing systems for airlines and online air travel agencies for enhancing the travelers' growth rates and customer values.

show abstract

Applying data mining on customer relationship management system for leisure coffee‐shop industry: a case study in Taiwan

Cited by 8 publications

References 22 publications

Identifying high-value airlines customers for strategies of online marketing systems

Identifying high-value airlines customers for strategies of online marketing systems

Fuzzy frameworks for mining data associations: fuzzy association rules and beyond

Applying data mining with a new model on customer relationship management systems: a case of airline industry in Taiwan

Contact Info

Product

Resources

About