Credit Card Customers Research Articles

Changing Preferences of Indian Customers’ towards combinations of services offered through Credit Cards: A Conjoint Analysis


 Goals: Increasing competition and growing risks are major challenges. In a fiercely competitive industry, credit card issuers need to develop a loyal customer base and motivate their card holders to use their cards at a sufficient level to assure profitability.
 Objectives: The objectives of this article is to know the weightage given by the customer to the different attributes of the credit cards and to design a consumer model of credit card to retain customer loyalty.
 Results: It is a convenience sample of several cities and metros which shares almost major characteristics of Indian consumers. This study has identified four schemes like Medi-claim facility (M, Assigned Value-1), Insurance facility (I, Assigned Value-2), Discounts facility for purchases (D, Assigned Value-3) and Wide Acceptance in different sectors (W, Assigned Value-4) as independent variables that provides stability and sustainability to the firm-customer relationship. The loyalty model of customer has developed through the conjoint analysis by taking the utilities of different service factors associated with the credit cards. The highest service factor score was 25.891 and 20.274 at the different timings of (2002-05) and (2006-09) respectively.
 
 Conclusions: In order to develop sustainable relationships, marketers of credit cards should leverage involvement in their customers by employing strategies such as branding, positioning, and attractive and flexible service benefits to retain the customer loyalty. Further, credit card customers have an affinity towards high service quality with an affordable cost, therefore making value a prime consideration for achieving loyalty.

Changing Preferences of Indian Customers’ towards combinations of services offered through Credit Cards: A Conjoint Analysis

Goals: Increasing competition and growing risks are major challenges. In a fiercely competitive industry, credit card issuers need to develop a loyal customer base and motivate their card holders to use their cards at a sufficient level to assure profitability. Objectives: The objectives of this article is to know the weightage given by the customer to the different attributes of the credit cards and to design a consumer model of credit card to retain customer loyalty. Results: It is a convenience sample of several cities and metros which shares almost major characteristics of Indian consumers. This study has identified four schemes like Medi-claim facility (M, Assigned Value-1), Insurance facility (I, Assigned Value-2), Discounts facility for purchases (D, Assigned Value-3) and Wide Acceptance in different sectors (W, Assigned Value-4) as independent variables that provides stability and sustainability to the firm-customer relationship. The loyalty model of customer has developed through the conjoint analysis by taking the utilities of different service factors associated with the credit cards. The highest service factor score was 25.891 and 20.274 at the different timings of (2002-05) and (2006-09) respectively. Conclusions : In order to develop sustainable relationships, marketers of credit cards should leverage involvement in their customers by employing strategies such as branding, positioning, and attractive and flexible service benefits to retain the customer loyalty. Further, credit card customers have an affinity towards high service quality with an affordable cost, therefore making value a prime consideration for achieving loyalty.

Trust and privacy on the internet: The solution is ahead

Who has stopped and thought about their data out there on the internet? Data about a person: Twitter followers, Facebook friends, eBay reputation. Or data about one’s company: purchase record, customer lists, customer credit card numbers. Data are spread around, most often only protected with passwords and too easily accessible. The Digital Asset Grid (DAG) is a research project run by Innotribe (the innovation arm of SWIFT) in 2012, which envisions a solution to this problem using the following components: a crowd-sourced, user-centric identity; privacy and trust on the internet, based on the Respect Trust Framework; an infrastructure for secure storage and exchange of digital assets. This paper explores the DAG and looks at the status of the project.

Sensitivity Analysis of b in MCLP Model for Credit Card-Holders Classification

Credit card customer classification is an important part of credit card customer management, which is also the key of risk control. As an effective approach, MCLP (multi-criteria linear programming) model has been applied to credit card customer classification successfully. Here, b is an important parameter in MCLP model which is decided by decision makers during the classification process. How the parameter affects the classification accuracy in the MCLP model is an important problem. Thus, sensitivity analysis of b is analyzed in the paper for credit card-holders classification. And credit card-holders’ datasets are selected for experiments. The experiment results show that b equals to zero is the best choice for this problem.

Credit where it is due: drivers of loyalty to credit cards

PurposeIn a fiercely competitive industry, credit card issuers need to develop a loyal customer base and motivate their card holders to use their cards at a sufficient level to assure profitability. The purpose of this article is to propose a consumer model of customer loyalty in the credit card industry.Design/methodology/approachA model of customer loyalty incorporating service quality, value and involvement is developed theoretically and validated empirically through SEM using data collected from a global sample of 114 credit card holders.FindingsThe loyalty model proposed was validated, showing that the independent variable, i.e. customer involvement, had path loadings of 0.32 and 0.26 on quality and value, respectively, while both service quality and value had direct effects on loyalty, with path loadings of 0.30 and 0.51, respectively.Research limitations/implicationsCustomer involvement has been shown to directly influence both service quality and value, and it should be further investigated for its effect on sustaining relationships, as well as a variable for segmenting customers.Practical implicationsIn order to develop sustainable relationships, marketers of credit cards should leverage involvement in their customers by employing strategies such as branding, positioning, and attractive and flexible frequent use benefits. Further, credit card customers desire high service quality, but at an affordable cost, therefore making value a prime consideration for achieving loyalty.Originality/valueThis study has identified “involvement” as an independent variable that provides stability and sustainability to the firm‐customer relationship. Despite its pertinence, this customer characteristic that may be also used to segment customers has not been investigated in prior quantitative studies.

An Exploratory Study of the Islamic Credit Card Users’ Satisfaction

The Islamic financial system industry is growing tremendously in Malaysia an the existence of the system in parallel with the existing conventional financial system provide an alternative to Muslim consumer in adhering to religious rules and regulation. The rapid development of the Islamic products' creation in the banking system is comparable but customer satisfaction should not be ignore. The researcher proposed to develop a customer satisfaction model for one of the Islamic banking product (i.e Islamic credit card). Research in customer satisfaction is predominant by the quantitative research, but the qualitative research is also getting attention. This paper is a preliminary investigation in identifying if religion has any relationship with Islamic credit card customers' satisfaction.

Discovering cardholders’ payment-patterns based on clustering analysis

This paper sampled approximately 9.3 million entries of data, concerning payments from 300,000 credit card customers over the past two years of Bank A in Taiwan. By applying data mining techniques to decipher customers’ behavior and perform risk analysis, the clustering algorithms divides card users into 9 groups of different levels of contributions and risk profiles, according to their consumption patterns. We generalize a set of clustering rules to identify high risk customer groups in advance. Therefore, the proposed suggestions could tell who was a bad risk and either deny their application or, for those who were already cardholders, start shrinking their available credit and increasing minimum payments to squeeze out as much cash as possible before they defaulted. On the other hand, banks are advised to adjust credit limits in a timely manner for the customer groups whose risks are low and contributions are high, in addition to the provision of value added services, in order to enhance earnings.

An Empirical Analysis of Credit Card Customers’ Overdue Risks for Medium- and Small-Sized Commercial Bank in Taiwan

This paper constructs a multiple regression model to evaluate the overdue risk of credit card holders. The results can identify the factors influencing the credit card holders’ overdue risk behavior in order to provide card issuing banks a decision-making reference in the investigation of credit card holders’ related characteristics and the relationship qual-ity between the credit card holders and the bank. In addition, the paper can also provide credit card issuing banks the practice reference of the application on risk management decision.

Behavioral assessment of recoverable credit of retailer’s customers

The increasing rate of late payments by credit card customers, which are caused by the recent economic downturn, is causing not only reduced profit margins but also significant sales losses for retail companies. Under pressure to increase revenues, credit prediction should be a part of customer delinquency management. In this study, a credit prediction model has been developed to manage delinquents holding retail credit cards. The hybrid model combines a Kohonen network and a Cox’s proportional hazard model. A Kohonen network is used to cluster credit delinquents into homogeneous groups. A Cox’s hazard model is used to analyze repayment patterns of delinquents in each group. The model estimates the expected time of credit recovery from delinquents. This model’s prediction accuracy scored above 93%.

Combined rough set theory and flow network graph to predict customer churn in credit card accounts

Customer churn has become a critical issue, especially in the competitive and mature credit card industry. From an economic and risk management perspective, it is important to understand customer characteristics in order to retain customers and differentiate high-quality credit customers from bad ones. However, studies have not yet adequately introduced rules based on customer characteristics and churn forms of original data. This study uses rough set theory, a rule-based decision-making technique, to extract rules related to customer churn; then uses a flow network graph, a path-dependent approach, to infer decision rules and variables; and finally presents the relationships between rules and different kinds of churn. An empirical case of credit card customer churn is also illustrated. In this study, we collect 21,000 customer samples, equally divided into three classes: survival, voluntary churn and involuntary churn. The data from these samples includes demographic, psychographic and transactional variables for analyzing and segmenting customer characteristics. The results show that this combined model can fully predict customer churn and provide useful information for decision-makers in devising marketing strategy.

Domain-Driven Classification Based on Multiple Criteria and Multiple Constraint-Level Programming for Intelligent Credit Scoring

Extracting knowledge from the transaction records and the personal data of credit card holders has great profit potential for the banking industry. The challenge is to detect/predict bankrupts and to keep and recruit the profitable customers. However, grouping and targeting credit card customers by traditional data-driven mining often does not directly meet the needs of the banking industry, because data-driven mining automatically generates classification outputs that are imprecise, meaningless, and beyond users' control. In this paper, we provide a novel domain-driven classification method that takes advantage of multiple criteria and multiple constraint-level programming for intelligent credit scoring. The method involves credit scoring to produce a set of customers' scores that allows the classification results actionable and controllable by human interaction during the scoring process. Domain knowledge and experts' experience parameters are built into the criteria and constraint functions of mathematical programming and the human and machine conversation is employed to generate an efficient and precise solution. Experiments based on various data sets validated the effectiveness and efficiency of the proposed methods.

Credit card customer analysis based on panel data clustering

Abstract In this paper, we propose a new distance measurement which can be used in panel data clustering. The distance as we designed can be calculated with weight and without weight. If users put more attention on recent data, a heavier weight can be assigned to the recent data. We use real panel data of a commercial bank’s credit card to examine the performance of our new distance measurement. The results show that our distance measurement can reflect the information of different periods and panel data can be used to cluster to find new knowledge. This study discovers different knowledge structure from the traditional econometrics analysis with the help of data mining algorithms.

Evaluating the Likelihood of Using Linear Discriminant Analysis as A Commercial Bank Card Owners Credit Scoring Model

The paper attempts to determine whether there exists a relationship between the on-time payments of credit card owners of a Commercial Bank and their demographic characteristics (particular personal and family status). It evaluates the statistical technique of discriminant analysis on credit card customers’ data of a Greek Commercial Bank and examine whether it is possible to create a model evaluating the credibility of prospective credit-card customer. The sample includes personal data, as well as, payment consistency for 829 customers of the Greek Commercial Bank (X-BANK) of average size.The statistical analysis of the sample data included the identification of the relationship between the theoretical and empirical prices of the distributions of the bank customers’ specific variables and discriminant analysis. The results showed that establishing a model to evaluate the credibility of prospective bank card customers, using the technique of the linear discriminant analysis, is not possible. The findings prove interesting and useful for all bank managers. The paper contributes to the financial services literature by adding a further critical analysis into credit scoring systems established by several banking institutions.

Antecedents of Redemption of Reward Points: Credit Card Market in China and International Comparison

ABSTRACT This article studies and identifies factors that affect the redemption of reward points under customer loyalty programs commonly adapted by Chinese credit card issuers. This article also studies the customer awareness of reward-point programs for Chinese credit card users. Regression analysis based on over 400 telephone interviews in China was conducted. Furthermore, many examples from different countries are introduced to compare with Chinese consumers. Results from the study show low customer awareness about point-based reward programs and low redemption rates. Further study shows the strong correlation between lack of awareness about the reward-point program and the low level of interest among customers toward the program. Key factors that affect the redemption of reward points include the awareness of the reward-point program, the attractiveness of the incentives, duration of the credit card, and frequency of credit card usage. This article suggests that effective communication by credit card marketers and customers' experience with the benefits are essential to promote awareness of the point-based reward programs. This study on credit card reward programs in the banking industry focuses on the lower-middle-income segment in the developing market.

What's Draining Your Wallet? The Real Cost of Credit Card Cash Advances

Americans have come to rely on their credit cards as both a form of payment for purchases and a flexible way to borrow cash. The total amount of credit card debt is approaching a trillion dollars. Credit cards are a key source of revenue for financial institutions and usually among the most profitable loan products available today.Credit card pricing has become highly complex and increasingly difficult for borrowers to follow. Credit card issuers at one time charged a single fixed interest rate to all customers and now charge individual customers several different varying interest rates at once, some of which expire after a short time period, and some rates suddenly changing to 'penalty rates' under certain conditions. The number and importance of fees charged to consumers has also grown dramatically.While there has been significant public discussion of certain hidden fees that are common on credit cards, manipulating how consumers’ payments are allocated towards a borrower’s balance is another hidden charge that can impose significant costs on the borrower without their knowledge. Borrowers can have balances on the same card at several different rates at once such as a purchase balance, a temporary promotional - or 'teaser' - balance and a high-rate cash advance balance. By putting all of the payment toward the lowest rate balance (typically the purchase balance or teaser balance), issuers can in effect substantially raise the interest rates paid by borrowers.This report demonstrates that this ordering of payments or 'payment allocation' policy can be both expensive and confusing for credit card customers who, as a group, have little knowledge of the mechanics or cost. The data analyzed in this report shows that allocating payments to the lowest rate first is harmful to borrowers, highly deceptive, and inconsistent with risk-based pricing. More specifically the study finds the system is stacked against borrowers in the following ways:Payment allocation forces borrowers to pay the highest prices, as much as 7 percentage points higher. By paying the lower-cost purchase balance first, payment allocation increases the borrower’s other balances and effectively raises the total interest paid by the borrower. As a result of payment allocation abuses, some borrowers with credit card debt pay as much as $700 extra each year. Customers are unaware of the significance and impact of these hidden charges. Only 3% of borrowers surveyed have the knowledge and capacity to evaluate credit card companies’ payment allocation policies.Payment allocation distorts risk-based pricing. When all measures of risk are taken into account, our analysis reveals the opposite of risk-based pricing. Lower risk customers pay significantly higher interest rates compared to high-risk borrowers.

Benefits of Relationship Banking: Evidence from Consumer Credit Markets

This paper empirically examines the benefits of relationship banking to banks, in the context of consumer credit markets. Using a unique panel dataset that contains comprehensive information about the relationships between a large bank and its credit card customers, we estimate the effects of relationship banking on the customers’ default, attrition, and utilization behavior. We find that relationship accounts exhibit lower probabilities of default and attrition, and have higher utilization rates, compared to non-relationship accounts, ceteris paribus. Such effects become more pronounced with increases in various measures of the strength of the relationships, such as relationship breadth, depth, length, and proximity. Moreover, dynamic information about changes in the behavior of a customer’s other accounts at the bank, such as changes in checking and savings balances, helps predict and thus monitor the behavior of the credit card account over time. These results imply significant potential benefits of relationship banking to banks in the retail credit market.

Benefits of Relationship Banking: Evidence from Consumer Credit Markets

This paper empirically examines the benefits of relationship banking to banks, in the context of consumer credit markets. Using a unique panel dataset that contains comprehensive information about the relationships between a large bank and its credit card customers, we estimate the effects of relationship banking on the customers' default, attrition, and utilization behavior. We find that relationship accounts exhibit lower probabilities of default and attrition, and have higher utilization rates, compared to non-relationship accounts, ceteris paribus. Such effects become more pronounced with increases in various measures of the strength of the relationships, such as relationship breadth, depth, length, and proximity. Moreover, dynamic information about changes in the behavior of a customer’s other accounts at the bank, such as changes in checking and savings balances, helps predict and thus monitor the behavior of the credit card account over time. These results imply significant potential benefits of relationship banking to banks in the retail credit market.

Using Six Sigma DMAIC to improve credit initiation process in a financial services operation

Purpose – The credit initiation process for mid‐level corporate credit card customers involves dependencies on multiple people across divisions considered as a critical function for a US financial services company. Increasing efficiency and effectiveness of the process could save time and money for the company. The purpose of this study is to analyze the process using Six Sigma DMAIC tools in order to determine inefficiencies; specifically, to decrease the number of days it takes from the time a company submits a request, to the time it is approved from 20 days to 15 days, resulting in a 25 percent improvement in throughput.Design/methodology/approach – The process improvement tool used is the Six Sigma DMAIC methodology, in addition to cause‐and‐effect diagrams and the development of poka‐yokes.Findings – This study found several areas for improvement in the process studied. Using statistical testing, bottlenecks in the process were identified. Process changes are suggested, as well as, new measures that...

Does Compulsive Buying Affect Credit Card Defaults?

Non-financial as well as financial factors force credit card customers into the unmanageable debt levels, leading to default. The research question is to ascertain the relative importance of the psychological and socio-cultural factors in categorising the credit card users as non-defaulters and defaulters. This study and its findings throw light into the non-financial factors to be looked into so as to increase the customer suitability in retail sale of a financial product like credit card. This can considerably reduce the risk of wrong selling of credit card and its offered services.

A metric for customer lifetime value of credit card customers

Estimating customer lifetime value (CLV) is becoming increasingly important in order for firms to identify and invest on prospective profitable customers. A credit card issuer firm has to take several different decisions regarding a customer throughout her stay with the firm. CLV estimation can help a firm in making some of these crucial decisions. In this paper, we have presented a conceptual model for revenue from a credit card customer and have further presented a metric for CLV. This metric has been designed specifically for credit card customers. We have simulated different states of a customer to demonstrate how the proposed metric works.