Customer Database Research Articles

A framework for preventing money laundering in banks

PurposeThe purpose of this paper is to form a new framework for preventing money laundering by mapping COBIT (Control for Information and Related Technology) processes to COSO (Committee of Sponsoring Organisation) components.Design/methodology/approachFirst, a new framework for preventing money laundering in banks is formed by mapping COBIT to COSO. Further, the potential of the mapped framework to comply with the Bank Secrecy Act requirements is analysed.FindingsThe mapped framework effectively supports all the activities of financial sectors through defining efficient information technology‐based processes and control methods. Information systems play a key role for financial sectors in producing financial statements, managing customer databases, detecting frauds, etc.Research limitations/implicationsCase studies of banks of different sizes, and in different countries are needed. It is necessary to improve the mapped framework by considering Basel III regulations.Practical implicationsCOBIT‐mapped‐COSO framework is useful for banks to fight money laundering. While adopting the new framework, an organisation should apply the best practices that suit its operations rather than all the control objectives.Social implicationsThe new framework can help banks fight money laundering.Originality/valueFor preventing money laundering through banks, a number of policies and intelligence systems are in place. However, there is no efficient framework that could guide banks to follow these policies and use information technologies. This paper proposes a new framework to target these gaps.

Organizational Learning and CRM Success: A Model for Linking Organizational Practices, Customer Data Quality, and Performance

A high quality customer database is a cornerstone of successful interactive marketing strategies and tactics. Based on the notion that customer data quality is not only a technical but also an organizational problem, this study develops and tests an organizational learning framework of the relationship between organizational processes, customer data quality and firm performance. The findings show that high quality customer data impact both customer and business performance and that the most important driver of customer data quality comes from the executive suite. A large portion of the impact of organizational culture on performance is mediated by customer data quality and data sharing. The results support the presence of a hierarchy of effects for enhancing data quality that runs from organizational learning (committed to a shared vision for CRM data), to cross-functional learning (marketing/IT cooperation, marketing/IT integration) to functional learning (data sharing).

Optimal selection of media vehicles using customer databases

This paper investigates the problem where an organization must select among multiple media vehicles for a marketing campaign, and determine how many names from each vehicle to impress (the contact depth). The organization can estimate the return from contacting each prospective customer, and this return decreases as depth increases. Different vehicles have different marginal costs per impression, and may have minimum-spend requirements or activation costs to use the vehicle. Decisions are to be made to maximize profit subject to a constraint on the total amount spent. We proposed an optimization model and two computationally efficient methods that often lead to global optimal solutions under practical assumptions. The model is illustrated with two data sets.

Challenges in PII data protection

Personal data has become the prime target of hackers and cyber-criminals. Dubbed ‘PII’ (most commonly taken to mean Personally Identifiable Information, although definitions vary, as we'll see) it can be exploited in many ways, from identity theft, spamming and phishing right through to cyber-espionage. Yet something as simple as a customer database has proven surprisingly difficult to protect. Why is this? And what can be done about it? Personally Identifiable Information (PII) data has become the prime target of hackers and cyber-criminals. It can be exploited in many ways, from identity theft, spamming and phishing right through to cyber-espionage. Yet something as simple as a customer database has proven surprisingly difficult to protect. Why is this? And what can be done about it? Yigal Rozenberg of Protegrity looks at the roles that technologies such as hashing, masking, encryption and tokenisation can play – and their weaknesses.

Unprofitable Cross-Buying: Evidence from Consumer and Business Markets

Conventional wisdom, marketing literature, and cross-selling practices to date are based on the notion that customer cross-buying is positively associated with customer profitability. However, this study finds that when certain customers with persistent adverse behavioral traits (e.g., limited spending, excessive revenue reversals, excessive service requests, promotion purchase behavior) engage in cross-buying, they exhibit a downward spiral of unprofitable relationship, with the losses increasing with higher levels of cross-buy. The authors analyze the customer databases of five firms and find that 10%–35% of the firms' customers who cross-buy are unprofitable and account for a significant proportion (39%–88%) of the firms' total loss from its customers. Consequently, the authors present a two-stage framework to enable managers to discern customers who are likely to engage in profitable versus unprofitable cross-buying. Overall, the findings refine the basic understanding of the cross-buy phenomenon and motivate managers to rethink their marketing practices and policies, which are typically designed to maximize cross-buy opportunities for every customer.

Injury survey of a non-traditional ‘soft-edged’ trampoline designed to lower equipment hazards

In Australia trampolines contribute one quarter of all childhood play equipment injuries. The objective of this study was to gather and evaluate injury data from a non-traditional, ‘soft-edged’, consumer trampoline, where the design aimed to minimise injuries from the equipment and from falling off. The manufacturer of the non-traditional trampoline provided the University of Technology Sydney with their Australian customer database. The study involved surveys in Queensland and New South Wales, between May 2007 and March 2010. Initially injury data was gathered by a phone interview pilot study, then in the full study, through an email survey. The 3817 respondents were the carers of child users of the ‘soft-edge’ trampolines. Responses were compared with Australian and US emergency department data. In both countries the proportion of injuries caused by the equipment and falling off was compared with the proportion caused by the jumpers to themselves or each other. The comparisons showed a significantly lower proportion resulted from falling-off or hitting the equipment for this design when compared to traditional trampolines, both in Australia and the US. This research concludes that equipment-induced and falling-off injuries, the more severe injuries on traditional trampolines, can be significantly reduced with appropriate trampoline design.

Cryptic Distant Relatives Are Common in Both Isolated and Cosmopolitan Genetic Samples

Although a few hundred single nucleotide polymorphisms (SNPs) suffice to infer close familial relationships, high density genome-wide SNP data make possible the inference of more distant relationships such as 2nd to 9th cousinships. In order to characterize the relationship between genetic similarity and degree of kinship given a timeframe of 100–300 years, we analyzed the sharing of DNA inferred to be identical by descent (IBD) in a subset of individuals from the 23andMe customer database (n = 22,757) and from the Human Genome Diversity Panel (HGDP-CEPH, n = 952). With data from 121 populations, we show that the average amount of DNA shared IBD in most ethnolinguistically-defined populations, for example Native American groups, Finns and Ashkenazi Jews, differs from continentally-defined populations by several orders of magnitude. Via extensive pedigree-based simulations, we determined bounds for predicted degrees of relationship given the amount of genomic IBD sharing in both endogamous and ‘unrelated’ population samples. Using these bounds as a guide, we detected tens of thousands of 2nd to 9th degree cousin pairs within a heterogenous set of 5,000 Europeans. The ubiquity of distant relatives, detected via IBD segments, in both ethnolinguistic populations and in large ‘unrelated’ populations samples has important implications for genetic genealogy, forensics and genotype/phenotype mapping studies.

Comment: If you ask me

A FALSIFIED attack on entertainment retailer Game earlier this year highlighted the serious damage a fake hack can do to a business. Reports at the time discussed a hack on the entertainment retailer, suggesting its customer database had been compromised and its customer contact details leaked on PasteBin.

A parsimonious stochastic model for forecasting gamers' revenues in casinos

The gaming industry is the largest entertainment industry in the United States, with more than $80 billion in revenue annually. Because of the stochasticity of gambling outcomes and the complexity of the casino context, forecasting individual‐level revenues in a casino setting is extremely challenging, and yet crucial for customer relationship management. Current approaches for customer base analysis are usually too general to handle the unique context of the casino setting. To fill this gap between research and practice, this paper develops a stochastic model that incorporates visitation, wagering, and gambling outcomes to forecast gamers' revenues for a major casino operator. The proposed model is parsimonious and can be scaled to handle massive casino customer databases. Despite its parsimony, a holdout prediction test shows that the proposed model provides more accurate individual‐level revenue predictions than other forecasting methods that are based only on the observed data. Copyright © 2012 John Wiley & Sons, Ltd.

Rough sets and principal components analysis: A comparative study on customer database attributes selection

The databases of real world contain a huge volume of information; however, part of these data is not interesting for the knowledge extraction. So, data are preprocessed for reducing the amount of information and selecting more relevant attributes. This paper addresses a contrastive study between rough sets and principal components analysis on customer database attributes selection. The experiments were carried out using the insurance company database to evaluate a k-means clustering. The objective is to investigate the capacity of these techniques to improve the identification of customers segments in databases, what appears as an important tool to increase the effectiveness of business communication. Key words: Rough sets, principal components analysis, attributes selection, customer database.

Survey of injury sources for a trampoline with equipment hazards designed out.

AimIn Australia, trampolines contribute approximately one-quarter of all childhood play-equipment injuries. The purpose of this study was to gather and evaluate injury data from a nontraditional, ‘soft-edged’, consumer trampoline in which the equipment injury sources have been designed out.MethodsA survey was undertaken in Queensland and New South Wales. The manufacturer of the nontraditional trampoline provided the University of Technology, Sydney, with their Australian customer database. Injury data were gathered in a pilot study by phone interview, then in a full study through an email survey. Results from 3817 respondents were compared with earlier Australian and US data from traditional trampolines gathered from emergency departments.ResultsA significantly lower proportion of the injuries caused by falling off or striking the equipment was found for this new design when compared with traditional trampolines both in Australia and in the USA. The age of children being injured on trampolines in Australia was found to be markedly lower than in North America.ConclusionsThis research indicates that with appropriate design the more severe injuries on traditional trampolines can be significantly reduced.

Estimating the Impact of Free-Play Coupon Value on Players’ Slot Gaming Volumes

This study reports the results of a field experiment that assessed the relationship between free-play coupon value and a player’s slot gaming volume. Selected players in the customer database of a Las Vegas hotel casino received either a $50 or a $100 free-play incentive. Results indicated little effect of coupon value on a player’s slot volume. Indeed, the analysis found that complimentary rooms had a greater effect on coin-in, and complimentary food and beverage had the greatest effect of all. These findings imply that offering a relatively high incentive may not necessarily be more effective than providing a comparable lower incentive value in increasing a player’s slot gaming volume. Instead, casino managers should consider the overall profit picture from a given incentive and assess whether particular players are more responsive to complimentary amenities, such as room, food and beverage.

NON-DATABASE CUSTOMER AS SPATIAL ISSUES OF ESTIMATING HYPERMARKET’S LIFETIME VALUE: AN APPROACH OF SURVEY-GIS METHOD

Prospecting hypermarket lifetime value is vital important to predict how long the business will be survive in the marketplace as important as measured the profitability of the business. Mostly, the hypermarket has been used database customer as main resources of predicting their customer without consider any data from non-database customer, which also called “free customer”. In fact, non-customer is spatial-based information where never tied with hypermarket database of customer. This is actually the gaps that exist in prospecting customer lifetime value where most of study was aimed on database customer and also lacks in tied spatial-based information to the hypermarket’s customer database. The objective of this paper is to discuss non-database customer as spatial issues during estimating the profitability of hypermarket business. Secondly, this study is aimed to demonstrate the location of non-database customer as base platform for further works on prospecting customer lifetime value. The method use in this study is combination survey-GIS as solution for handle non-database customer in spatial environment. Specifically, Arcview software (GIS) is used as main method for modelling the physical location of marketplace. Meanwhile, a postal address of non-database customer which is collected from survey is used for testing under the physical model of marketplace location. The Seberang Perai Tengah of Penang in Malaysia is setting as location of the study. One of the result shows that non-database customer can visualized individually by specific location that based on street and road. This study will suggest on how to used spatial data as a part of predicting lifetime value of customer where it can produced results in more precise and concurrently.

과학기술정보콘텐츠 통합관리시스템 구축을 위한 데이터 마이그레이션 모델 수립 및 적용 사례

국내 데이터베이스 구축 시장은 작년 기준으로 3조 6,633억원 규모로 추정된다. 데이터베이스 산업의 지속적인 성장과 맞물려, 데이터 마이그레이션의 중요성도 날로 높아지고 있다. 고객 위치를 찾아주는 g-CRM이나 고객에게 상품을 추천하는 맞춤 설계 기능 등은 모두 고객 데이터베이스, 상품 데이터베이스, 지리정보 데이터베이스 등이 결합되어야 구현 가능한 서비스들이다. 스마트 비즈니스의 핵심 인프라도 통합되고 완전하며 신뢰할 수 있는 데이터베이스라고 할 수 있다. 이러한 데이터베이스의 중요성에도 불구하고, 데이터 마이그레이션 및 통합 과정의 효율적인 수행 방안이나 데이터 검증 방법에 대하여는 구체적인 연구가 부족한 실정이다. 본 연구에서는 다양한 타입으로 존재하는 과학기술분야 콘텐츠들을 대상으로 데이터 마이그레이션을 위한 모델을 설계하였고, 모델을 기반으로 실제 데이터 마이그레이션 작업을 수행한 결과를 제시하였다. 아울러 데이터 마이그레이션 결과에 대한 검증을 위해 데이터베이스의 완전성, 데이터값의 일관성, 관계의 일관성을 검증하였고, ANSI/ASQ Z1.4-2003에서 제시된 샘플링 검사 기법도 적용하였다. 결과적으로 모델 수립에 의한 체계적인 데이터 마이그레이션 수행은 데이터베이스 정합성 및 데이터값의 정확성에 영향을 미치고, 고품질의 데이터베이스를 유지하기 위한 필수 요소라 할 수 있다. The domestic market of database in Korea is estimated to be over 3.663 trillion won. The data migration is getting to be more important along with the continuous growth of the database industry. g-CRM and private recommending function are examples of the service that can be given through coupling among customer database, product database, geographic information database, and others. The core infrastructure is also the database which is integrated, perfect, and reliable. There are not enough researches on efficient way of data migration and integrating process and investigation of migrated data though trends of database in IT environment as above. In connection with this issue, we have made a model for data migration on scientific and technological contents and suggest the result of data migration process adapting that model. In addition, we verified migration's exhaustiveness, migration's consistency, and migration's coherence for investigation of migrated data and database. From the result, we conclude data migration based on proper model has a significant influence on the database consistency and the data values correctness and is essential to maintain high qualified database.

Will the frog change into a prince? Predicting future customer profitability

More and more companies have customer databases that enable them to analyze customer profitability over time. These companies often seek to determine the most important customers as indicated by their current or historical profitability and focus attention on them. Focusing on profitable customers can result in more efficient use of marketing resources, but this approach neglects the fact that customers can evolve over time. Some customers begin as low-profit customers but eventually develop into high-profit customers. Others may start out as high-profit customers but become unprofitable over time. Previous efforts to predict future profitability have been relatively unsuccessful, with relatively simple, naïve models often performing just as well as or better than more sophisticated ones. Our paper presents a new approach to predicting customer profitability in future periods that performs significantly better than naïve models. We estimate the models on data from a high-tech company in a business-to-business context and validate the models' predictive ability on a holdout sample. We show that a model based on simulation of customer futures provides large improvements over naïve extrapolation of average profits. By using the simulation model to select customers, ROI from marketing efforts is projected to increase by 58%.

Instantaneous Marketing: An Analysis Of Processes To Make A Connection With Customers Through New Communications Technologies

Marketers try to find ways to respond to the needs of their target customers through most modern communications technologies. As the means to communicate with customers change, marketers find it most convenient and cost effective to make direct contact with customers. This study explores the advanced media for marketers to make a two-way communication to interact in real time with their customers. Instantaneous marketing has revolutionized marketing communication. The exploitation of customer databases and the necessity to acquire more customer information have led the marketers to explore the ways to connect with customers more rapidly to bring differentiation to their products or services. Moreover, customers provide flow of information about various aspects of companys operations back to the company. Some companies still find it hard to cope with this enormous amount information in the fastest way possible. This study explores various communication technologies which help instantaneous marketing to come up with a model to explain the phenomenon. Implications for marketers will be provided to make us understand how we can make use of this information in practice.

Customer data integration: Reaching more consumers with certainty

Data hygiene is nothing new, but in this multichannel landscape, the need to collect and manage data in a bid to recognise consumers at any touchpoint is greater than ever. This article looks at the cutting-edge techniques for gathering and maintaining data on- and offline. It takes in single customer databases and 360-degree views, and highlights how consumer channel preference can be used to inform marketing budget placement and campaign strategy. This article also includes comparison of in-house and external customer data integration management.

Clustering Value Drivers of Indian Telecom Customers - Pathway for Effective Strategy Formulation

In recent years, Indian telecom organizations are experiencing tremendous growth rate and are brimming up with immense potential. However, increased competition for more customers has forced these organizations to transform radically from what is ‘best produced’ to what the customer ‘perceives as the best’. The objective of this study thus, is to first explore various customer value drivers in the Indian telecommunications market. After identifying these through factor analysis using Principal Component Analysis Method and taking Eigen value greater than one, they are subjected to cluster analysis and the respondents are grouped based on their demographic profile. The objective of this analysis is to enable the telecom service providers to devise a suitable strategy based on the importance of a particular value driver from customer’s perspective. The results of cluster analysis reveal vital Information about the preference of a particular customer value driver for a particular demographic segment. This information may be vital for managers to cater more effectively to the needs of a particular cluster in a particular demographic segment; and thus enable them to improve their customer database resulting in larger profits.

Hackers may be ‘pimping’ data

At least one security specialist sees darker motives behind recent high-profile hacks that have resulted in the theft of user or customer databases.

Data Mining Techniques to Enhance Customer Lifetime Value

It is proved by many studies that it is more costly to acquire than to retain customers. Consequently, evaluating current customers to keep high value customers and enhance their lifetime value becomes a critical factor to decide the success or failure of a business. This study applies data from customer and transaction databases of a department store, based on RFM model to do clustering analysis to recognize high value customer groups for cross-selling promotions.