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

Chiang, Wen-Yu

doi:10.1108/k-12-2016-0348

Cited by 12 publications

(4 citation statements)

References 27 publications

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“…The study outcomes identified two papers referring to aviation, both written by Chiang (2018Chiang ( , 2019. The author investigated Big Data use in CRM strategies in airports.…”

Section: Mapping Of Papers According To Research Areasmentioning

confidence: 99%

A structured literature review on Big Data for customer relationship management (CRM): toward a future agenda in international marketing

Vecchio

Mele

Siachou

et al. 2021

IMR

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PurposeThis paper aims to advance the international marketing debate by presenting the results of a structured literature review (SLR) focusing on Big Data implementation in customer relationship management (CRM) strategizing. It outlines past and present literature and frames a future research agenda.Design/methodology/approachThe research analyzes papers published in journals from 2013 to 2020, deriving significant insights about Big Data applications in CRM. A sample of 48 articles indexed at Scopus was preliminarily submitted for bibliometric analysis. Finally, 46 papers were analyzed with content and a bibliometric analysis to identify areas of thematic specializations.FindingsThe paper presents a conceptual multilevel framework demonstrating areas of specialization emerging from the literature. The framework is built around four coordinated sequences of actions relevant to “why,” “what,” “who” and “how” Big Data is implemented in CRM strategies, thus supporting the conception and implementation of an internationalization marketing strategy.Research limitations/implicationsImplications for the development of the future research agenda on international marketing arise from the comprehension of Big Data in CRM strategy.Originality/valueThe paper provides a comprehensive SLR of the articles dealing with models and processes of Big Data for CRM from an international marketing perspective. Despite these issues' relevance and the increasing literature focused on them, research in this area is still fragmented and underexplored, requiring more systematic and holistic studies.

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“…The study outcomes identified two papers referring to aviation, both written by Chiang (2018Chiang ( , 2019. The author investigated Big Data use in CRM strategies in airports.…”

Section: Mapping Of Papers According To Research Areasmentioning

confidence: 99%

A structured literature review on Big Data for customer relationship management (CRM): toward a future agenda in international marketing

Vecchio

Mele

Siachou

et al. 2021

IMR

View full text Add to dashboard Cite

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“…Although RFM does not explicitly provide a net profit for customer value, Fader et al [11] indicated it can be used to build customer value. In addition, different industries can measure customer value from different view by using RFM's variants such as such as RFMDR [12], RFTM [13] and FSLC [14] in a more reasonable way. Meanwhile, RFM models show customer value in three-dimensional space or above, which makes it difficult for market decision makers to conduct marketing analysis.…”

Section: Customer Valuementioning

confidence: 99%

A Time-aware Multi-task Learning Model for Customer Value Prediction in Civil Aviation

2019

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering

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The precise prediction of customer value is essential for any successful dynamic customer relationship management (CRM) system. It is also the key for the company to maximizing customer returns. In this research, we concentrate on two main aspects of the work in civil aviation field. Firstly, a reasonable value model is the premise of this prediction issue. Therefore, we propose a parametric customer value model RFUM to estimate customer value in civil aviation. It evaluates customer value from four different attributes and then presents customer value by the weight of the attributes. Secondly, Time-aware Multi-task Value Prediction (TMVP) model is proposed to predict the future value of customer. It employs two supervisory signals of purchase propensity and customer value to better train a specific neural network to automatically learn features. Experiments demonstrate that the RFUM model can more accurately measure the value of customer in civil aviation market and the TMVP model can achieve a more precise regression prediction result. In addition, we also find that increasing the time of a single calculation window can improve the performance markedly.

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“…Marketing mechanism as a network of persons, users, providers and groups offers properties, services, skills, goods and ideas to meet customer demand based on collaboration (Choi and Joo, 2018; Kim and Kumar, 2018; Tsiakali, 2018). A marketing mechanism is a complicated group of multifaceted associations where market participants’ activities affect their companies, followers and customers in a long-term manner (Chiang, 2018; Ebrahimian et al , 2018). Also, it plays an increasingly noticeable part of economic action and theoretical study (Cluley and Greenhalf, 2018; Redmond, 2018).…”

Section: Introductionmentioning

confidence: 99%

Quality factors and performance outcome of cloud-based marketing system

Ying

Wang

2021

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Purpose This study aims to propose an integrated model based on the DeLone and McLean Information System Success Model (DMISS) to examine whether quality factors (system, service and information) can affect user satisfaction and performance of cloud-based marketing systems. Design/methodology/approach Recently, technologies change quickly, innovation becomes a vital base of productivity and sustainable growth of businesses is widely accepted. Cyber-physical system technologies help industries change production and marketing cycles according to customers’ needs in real-time. In addition, processing information through cloud service helps companies meet customer needs. The advantages of cloud technology also make it easier for companies to quickly collect the latest data from various sources, making it more effective in decision-making. This research recommends cloud-based marketing to help companies maximize their revenue by providing useful information and better quality for business development. The data were gathered from China automotive companies’ customers. A total of 220 questionnaires were distributed, and 165 (82.5%) usable questionnaires were analyzed using structural equation modeling. Findings This study verified that costumers’ perceived information quality, system quality and service quality positively caused the user satisfaction in the cloud-based marketing system. Practical implications This paper presents beneficial advice for improving cloud-based marketing systems. Besides, the topic is relevant to cloud-based marketing systems’ success. A better understanding of the impact of intention to use and user satisfaction on cloud-based marketing systems could significantly enhance companies’ success. This paper’s theoretical and practical contributions are expressed to guide organizations and policymakers in increasing cloud-based marketing systems acceptance. Originality/value This study empirically tests the relationship of quality factors and performance outcome of cloud-based marketing system through a model based on DeLone and McLean theory. This study bridges the research gap by identifying the factors that drive the adoption of cloud-based services in marketing and the impact of user satisfaction and intention to use on the cloud-based marketing system performance in the case of china companies.

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

Cited by 12 publications

References 27 publications

A structured literature review on Big Data for customer relationship management (CRM): toward a future agenda in international marketing

A structured literature review on Big Data for customer relationship management (CRM): toward a future agenda in international marketing

A Time-aware Multi-task Learning Model for Customer Value Prediction in Civil Aviation

Quality factors and performance outcome of cloud-based marketing system

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