Customer attrition analysis in the securities industry: a large-scale field study in Korea

2019 ◽  
Vol 38 (3) ◽  
pp. 561-577
Author(s):  
Yongkil Ahn ◽  
Dongyeon Kim ◽  
Dong-Joo Lee

Purpose The purpose of this paper is to identify the attributes that predict customer attrition behavior in the brokerage and investment banking sectors. Design/methodology/approach The authors analyze the complete stock trading records and customer profiles of 458,098 retail customers from a Korean brokerage house. The authors develop customer attrition prediction models and further explore the practicality of these models using statistical classification techniques. Findings The results from three different binary selection models indicate that customer transaction patterns effectively explain the attrition of active retail customers in subsequent periods. The study results demonstrate that monetary value variables are the most critical for predicting customer attrition in the securities industry. Research limitations/implications This study contributes to the customer attrition literature by documenting the first large-scale field-based evidence that confirms the practicality of the canonical recency, frequency and monetary (RFM) framework in the investment banking and brokerage industry. The findings advance previous survey-based studies in the financial services industry by identifying the attributes that predict customer attrition behaviors in the securities industry. Practical implications The outcomes can be easily operationalized for attrition prediction by practitioners in financial service firms. Moreover, the ex post density of inactive customers in the top 10 percent most-likely-to-churn group is estimated to be five to six times the ex ante unconditional attrition ratio, which ascertains that the attributes recognized in this study work well for the purpose of target marketing. Originality/value While the securities industry is regarded as one of the most information-intensive industries, detailed empirical investigation into customer attrition in the field has lagged behind partly due to the lack of suitable securities transaction data and demographic information at the customer level. The current research fills this gap in the literature by taking advantage of a large-scale field data set and offers a starting point for more elaborate studies on the drivers of customer attrition in the financial services sector.

2010 ◽  
Author(s):  
Julia Levashina ◽  
Frederick P. Morgeson ◽  
Michael A. Campion

2020 ◽  
Vol 47 (3) ◽  
pp. 547-560 ◽  
Author(s):  
Darush Yazdanfar ◽  
Peter Öhman

PurposeThe purpose of this study is to empirically investigate determinants of financial distress among small and medium-sized enterprises (SMEs) during the global financial crisis and post-crisis periods.Design/methodology/approachSeveral statistical methods, including multiple binary logistic regression, were used to analyse a longitudinal cross-sectional panel data set of 3,865 Swedish SMEs operating in five industries over the 2008–2015 period.FindingsThe results suggest that financial distress is influenced by macroeconomic conditions (i.e. the global financial crisis) and, in particular, by various firm-specific characteristics (i.e. performance, financial leverage and financial distress in previous year). However, firm size and industry affiliation have no significant relationship with financial distress.Research limitationsDue to data availability, this study is limited to a sample of Swedish SMEs in five industries covering eight years. Further research could examine the generalizability of these findings by investigating other firms operating in other industries and other countries.Originality/valueThis study is the first to examine determinants of financial distress among SMEs operating in Sweden using data from a large-scale longitudinal cross-sectional database.


2010 ◽  
Vol 108-111 ◽  
pp. 1158-1163 ◽  
Author(s):  
Peng Cheng Nie ◽  
Di Wu ◽  
Weiong Zhang ◽  
Yan Yang ◽  
Yong He

In order to improve the information management of the modern digital agriculture, combined several modern digital agriculture technologies, namely wireless sensor network (WSN), global positioning system (GPS), geographic information system (GIS) and general packet radio service (GPRS), and applied them to the information collection and intelligent control process of the modern digital agriculture. Combining the advantage of the local multi-channel information collection and the low-power wireless transmission of WSN, the stable and low cost long-distance communication and data transmission ability of GPRS, the high-precision positioning technology of the DGPS positioning and the large-scale field information layer-management technology of GIS, such a hybrid technology combination is applied to the large-scale field information and intelligent management. In this study, wireless sensor network routing nodes are disposed in the sub-area of field. These nodes have GPS receiver modules and the electric control mechanism, and are relative positioned by GPS. They can real-time monitor the field information and control the equipment for the field application. When the GPS position information and other collected field information are measured, the information can be remotely transmitted to PC by GPRS. Then PC can upload the information to the GIS management software. All the field information can be classified into different layers in GIS and shown on the GIS map based on their GPS position. Moreover, we have developed remote control software based on GIS. It can send the control commands through GPRS to the nodes which have control modules; and then we can real-time manage and control the field application. In conclusion, the unattended automatic wireless intelligent technology for the field information collection and control can effectively utilize hardware resources, improve the field information intelligent management and reduce the information and intelligent cost.


2013 ◽  
Vol 38 ◽  
pp. 1-15 ◽  
Author(s):  
Ahmet Demir ◽  
Mustafa Laman ◽  
Abdulazim Yildiz ◽  
Murat Ornek

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