Discovering customer value for marketing systems: an empirical case study

Chiang, Wen-Yu

doi:10.1080/00207543.2016.1231429

Cited by 20 publications

(14 citation statements)

References 27 publications

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“…In the transportation industry, the RFM and RFM-based models have also been used to determine the values of passengers [36][37][38][39][40][41][42][43][44] (Table 2). The common characteristic of these studies is that all of them consider the "frequency" parameter.…”

Section: Rfm and Its Versionsmentioning

confidence: 99%

“…Hence, different RFM-based models were generally constructed in order to better reflect the behavior of passengers. For example, in the airline industry, the measures of the LDcFR model [39] include length, distance, frequency, and recency, whereas the FSLC model [41] focuses on frequency, season, locations of traveling, and cancellation times; the FMCN model [43] contains frequency, monetary, cancellation times, and the number of family members measures. Our proposed model (RFLT) differs from these previous studies in two respects.…”

Section: Rfm and Its Versionsmentioning

confidence: 99%

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Passenger scoring for free-pass promotion in public transportation

Alan¹,

Birant²

2020

Turk J Elec Eng & Comp Sci

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The focus of promotions targeted to increase the use of public transportation concentrates on increasing the attractiveness of it, particularly by decreasing transportation fares. To serve that purpose, this paper proposes a novel passenger scoring model, namely RFLT (recency, frequency, loyalty, and time), for offering a free-pass promotion in public transportation. It presents the comparison results of RFLT and wRFLT (weighted version) using a real-world dataset obtained by a near field communication (NFC) mobile payment application. The experimental results show that the w-RFLT model provides a more balanced score distribution than the RFLT model, and the frequency parameter (F), among four metrics (R, F, L, and T), influences the scoring results the most. The results of this study can be used to establish an efficient policy for increasing public transportation ridership.

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Section: Rfm and Its Versionsmentioning

confidence: 99%

Section: Rfm and Its Versionsmentioning

confidence: 99%

Passenger scoring for free-pass promotion in public transportation

Alan¹,

Birant²

2020

Turk J Elec Eng & Comp Sci

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“…We believe that shifting to a configurational understanding of business performance will show that the individual components behave differently under different conditions. Chiang (2017) adds that the business impact of the data science is different when we examine company front-office (Customer) or back-office processes (Provider). On one side, by increasing the data capture at multiple points of the Customer management, analytics find customers' knowledge, improve the analysis of the customer journey, support human decision making with automated algorithms and finally allow to deliver a personalized and differentiated customer experience (Chiang, 2017;Fosso Wamba et al, 2015;Waller & Fawcett, 2013) .…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Chiang (2017) adds that the business impact of the data science is different when we examine company front-office (Customer) or back-office processes (Provider). On one side, by increasing the data capture at multiple points of the Customer management, analytics find customers' knowledge, improve the analysis of the customer journey, support human decision making with automated algorithms and finally allow to deliver a personalized and differentiated customer experience (Chiang, 2017;Fosso Wamba et al, 2015;Waller & Fawcett, 2013) . On the other side, data science leverages a direct communication with providers or distributors and permits real-time management of the supply chain orienting the business value in that case to the efficiency of the operations (Addo-Tenkorang & Helo, 2016).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Exploring data conditions to improve business performance

Grimaldi

Fernández

Carrasco

2019

Journal of the Operational Research Society

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Past researches drew from the industrial organization perspective have examined the role of the data to generate competitive advantage. Their analysis show data is a valuable resource that can leverage business partnerships, vertical integration or diversification. The emergence of data science has created new opportunities to understand better clients' needs and to manage more efficiently the organizations processes. Nevertheless, if data analytics represent an enormous potential, many organizations are still looking the conditions to obtain value from them. Our study contributes to this topical subject analysing the relationship between different combinations of data conditions and the company performance that we measure through the Customer management and Provider operations efficiency. Our methodology is novel compared to previous researches which are based in linear algebra. It is based on the use of a fuzzy-set qualitative comparative analysis (fsQCA) which allows to reveal multiple paths to achieve the possible outcomes. Our results show that the consistency, completeness and protection of the data along with a data-driven company profile are different possible solutions to a better Customer management and Provider operations efficiency. Our conclusions allow practitioners to uncover the strength of the data in the hopes of solving many of their business performance concerns.

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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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Discovering customer value for marketing systems: an empirical case study

Cited by 20 publications

References 27 publications

Passenger scoring for free-pass promotion in public transportation

Passenger scoring for free-pass promotion in public transportation

Exploring data conditions to improve business performance

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

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