Neural Networks: A New Tool for Predicting Thrift Failures*

Salchenberger, Linda; Çinar, E. Mine; Lash, Nicholas A.

doi:10.1111/j.1540-5915.1992.tb00425.x

Cited by 450 publications

(194 citation statements)

References 17 publications

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“…Though it is really difficult to make a universally approved definition of CRM, it can be explained as a comprehensive strategy for acquiring, retaining and partnering with selective customers to create value for both a company and its customers. Many previous CRM-related researches have applied data mining techniques to analyse and understand customer behaviours and characteristics, and most of them have worked well (Bortiz et al ., 1995;Fletcher et al ., 1993;Langley et al ., 1995;Lau et al ., 2003;Salchenberger et al ., 1992;Su et al ., 2002;Tam et al ., 1992;Zhang et al ., 1999). In this section, we review previous researches mainly on classification and association rules for a variety of tasks in CRM domain.…”

Section: Researches On Crm Using Data Mining Techniquesmentioning

confidence: 99%

VIP-targeted CRM strategies in an open market

Lee¹,

Shim²,

Suh³

2015

Journal of the Korean Data and Information Science Society

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Nowadays, an open-market which provides sellers and consumers a cyber place for making a transaction over the Internet has emerged as a prevalent sales channel because of convenience and relatively low price it provides. However, there are few studies about CRM strategies based on VIP consumers for an open-market even though understanding VIP consumers' behaviors in open-markets is important to increase its revenue. Therefore, we propose CRM strategies targeted on VIP customers, obtained by analyzing the transaction data of VIP customers from an open-market using data mining techniques. To that end, we first defined the VIP customers in terms of recency, frequency and monetary (RFM) values. Then, we used data mining techniques to develop a model which best classifies and identifies influential factors customers into VIPs or non-VIPs. We also validate each of promotion types in the aspect of effectiveness and identify association rules among the types. Then, based on the findings from these experiments, we propose strategies from the perspectives of CRM dimensions for the open-market to thrive.

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Section: Researches On Crm Using Data Mining Techniquesmentioning

confidence: 99%

VIP-targeted CRM strategies in an open market

Lee¹,

Shim²,

Suh³

2015

Journal of the Korean Data and Information Science Society

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“…? 95.50% Brabazon and Keenan [12] 82.67% 78.67% 80.67% Brockett et al [47] 94 [70] 90.00% 96.00% 92.50% Sen et al [32] ? ?…”

Section: Mlp-bpmentioning

confidence: 99%

“…Salchenberger et al [70] Multiple regression applied to variables commonly used in financial analysis…”

Section: Mlp-bpmentioning

confidence: 99%

Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy

Jardin

2010

Neurocomputing

112

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We evaluate the prediction accuracy of models designed using different classification methods depending on the technique used to select variables, and we study the relationship between the structure of the models and their ability to correctly predict financial failure. We show that a neural network based model using a set of variables selected with a criterion that it is adapted to the network leads to better results than a set chosen with criteria used in the financial literature. We also show that the way in which a set of variables may represent the financial profiles of healthy companies plays a role in Type I error reduction.

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“…The OR community has primarily concentrated on applications to credit risk, for example the special issue of JORS (Crook et al, 2001) is particularly related to consumers. Other areas of application with similar characteristics include bankruptcy prediction (see the highly cited papers by Salchenberger et al, 1992;Tam and Kiang, 1992, and more recently, Zhang et al, 1999).…”

Section: Customer Relationship Management and Data Miningmentioning

confidence: 99%

“…Three of these highly cited articles (Salchenberger et al, 1992;Tam and Kiang, 1992;Wilson and Sharda, 1994) provided early introductions to the application of a computer-intensive method, new to the OR community (neural networks), to bankruptcy prediction. The only recent high citation articles concern the effects of uncertainty on the supply chain (Chen et al, 2000, with more than 100 citations, and Cachon and Lariviere, 2001, with 50).…”

Section: Or Journal Articles Included In the Citationmentioning

confidence: 99%

Forecasting and operational research: a review

Fildes

Νικολόπουλος

Crone

et al. 2008

Journal of the Operational Research Society

227

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From its foundation, operational research (OR) has made many substantial contributions to practical forecasting in organizations. Equally, researchers in other disciplines have influenced forecasting practice. Since the last survey articles in JORS, forecasting has developed as a discipline with its own journals. While the effect of this increased specialization has been a narrowing of the scope of OR's interest in forecasting, research from an OR perspective remains vigorous. OR has been more receptive than other disciplines to the specialist research published in the forecasting journals, capitalizing on some of their key findings. In this paper, we identify the particular topics of OR interest over the past 25 years. After a brief summary of the current research in forecasting methods, we examine those topic areas that have grabbed the attention of OR researchers: computationally intensive methods and applications in operations and marketing. Applications in operations have proved particularly important, including the management of inventories and the effects of sharing forecast information across the supply chain. The second area of application is marketing, including customer relationship management using data mining and computer-intensive methods. The paper concludes by arguing that the unique contribution that OR can continue to make to forecasting is through developing models that link the effectiveness of new forecasting methods to the organizational context in which the models will be applied. The benefits of examining the system rather than its separate components are likely to be substantial.

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Neural Networks: A New Tool for Predicting Thrift Failures*

Cited by 450 publications

References 17 publications

VIP-targeted CRM strategies in an open market

VIP-targeted CRM strategies in an open market

Predicting bankruptcy using neural networks and other classification methods: The influence of variable selection techniques on model accuracy

Forecasting and operational research: a review

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