Journal articles on the topic 'Bank client segmentation'

Abstract Segmentation in banking for the business client market is traditionally based on size measured in terms of income and the number of employees, and on statistical clustering methods (e.g. hierarchical clustering, k-means). The goal of the paper is to demonstrate that self-organizing maps (SOM) effectively extend the pool of possible criteria for segmentation of the business client market with more relevant criteria, including behavioral, demographic, personal, operational, situational, and cross-selling products. In order to attain the goal of the paper, the dataset on business clients of several banks in Croatia, which, besides size, incorporates a number of different criteria, is analyzed using the SOM-Ward clustering algorithm of Viscovery SOMine software. The SOM-Ward algorithm extracted three segments that differ with respect to the attributes of foreign trade operations (import/export), annual income, origin of capital, important bank selection criteria, views on the loan selection and the industry. The analyzed segments can be used by banks for deciding on the direction of further marketing activities.

Under conditions, when there is very intense competition, individualization of services is the most reasonable way to implement the client-oriented approach in banking. The article emphasizes the following organization and management tools needed for the successful individualization of corporate customer service in a commercial bank: deep market segmentation along with a selection of key clients considering their actual and potential importance for the bank, the development of flexible banking packages for sub-segments as part of banking services, the adoption of the institution a personal client's manager, staff development at all levels of the bank organizational structure in pursuit of internal marketing. The systematic use of these tools will allow a commercial bank to build partnerships with corporate clients, to raise the level of customers' satisfaction and loyalty, to ensure sustainability of interaction with the key customers that determine the essential characteristics of the banking business in the long term.

The This paper studies bank customers segmentation problem. Improved Apriori mining algorithm is a kind of data mining technology which is an important method in bank customers segmentation. In practical application, the traditional algorithm has shortcomings of the initial values sensitive and easy to fall into local optimal value, which will lead to low accuracy rate of silver class customer classification. According to the shortcomings of traditional algorithm, this paper puts forward a bank customer segmentation method based on improved Apriori mining algorithm in order to improve the bank customer segmentation accuracy. Experimental results show that the algorithm can effectively overcome the traditional algorithms shortcomings of easy to fall into local optimal value, improve the customer classification accuracy, make mining results more reasonable, lay down different customer service strategies for different client base, improve effective reference opinions of bank decision makers, and bring more benefits for the bank.

This article covers the key management issues in the loan syndications banking business. A syndicated loan is provided to a borrower by a group of commercial or investment banks. The global syndicated loan market is from one perspective, the primary funding source for corporations and on the other — one of the leading businesses for the global banks. There exist some unique challenges that must be responded by banks from a managerial and strategic perspective to establish and maintain leadership in the important business due to the features, structures, and industrial organisation of the market. We first consider how the loan syndications business is structured in a global bank, its functions and competitive advantages. Then we discuss the ways banks can implement an effective strategy and maintain leadership and growth in the market. Finally, we propose solutions to dealing with commoditization in banking: (i) adding more value-added services to the client offering; (ii) bundling of services in order to realize cross-selling opportunities and maximize share-of-wallet; (iii) further segmentation and customization of the client base (by industry/relationship/services consumption). By adopting these strategies, banks can successfully fight the commoditization magnet and increase the profitability of their loans syndications businesses.

The longitudinal study of the most sophisticated decision support system (DSS) for the management of debit accounts provides new answers to the question of conformity in French banking. In 2003, the analysis of 45 observations and qualitative interviews showed that the advisor maintains his free appreciation of risk. However, even if conformity does not exist, the results on the modification range show that the DSS does exert an influence on user behaviour. In addition, the interpretation and acceptance of DSS recommendation are different according to the type of portfolio managed and how the work is organised. The less the financial advisor knows the client, the greater the influence of the DSS. Recent decisions regarding the division of labour for the management of lower segments heighten the risk that DSS used without knowing the client leads to more conformity, or at least to what we conceptualise as strategic conformity, and a taylorisation of services.

The purpose of the research is the definition of the dominant ideas of financial technologies in digital banking. The methods of theoretical generalization, qualitative, quantitative and correlation analysis, causality tests, description and explanation are used, which made it possible to establish the relationship between the volume of investments in financial technologies and the performance of the banking system, identify the areas of application of financial technologies in the activities of the bank, determine the dominant ideas of financial technologies in digital banking and to uncover the factors and prospects of intensifying the use of financial technologies in digital banking in Ukraine. Results of the research are to substantiate the impact of artificial intelligence, biometrics, cloud services, big data, blockchain and open banking services on digital banking. Due to financial technologies in digital banking, it is possible to generate and store large amounts of data, simultaneously analyze and apply the results of their analysis, provide personalized banking services, perform the functions of central storage of information about the client of financial and non-financial nature, which facilitates the effective investment and credit decision-making, as well as improving the level of information security of banking operations. Practical implications. Financial services markets are transformed by the impact of financial technologies. Development of financial technology instruments by non-banking institutions necessitates the identification of opportunities for their use in banks. The set of financial technologies used by banks forms the digital banking system, the development level of which is the main competitive advantage of the bank in the business environment. Digital banking is characterized by the continuity and security of banking services, which provide the consumer with the ability to receive them online anywhere around the clock, personalization of banking services, digital authentication of users and digitization of banking transactions with the replacement of paperwork. The use of financial technologies in digital banking enables to automate customer segmentation processes, reduce costs on payment transactions, optimize accounting, financial and tax accounting, improve customer service and expand your customer base while maximizing revenue in certain business segments. Value/originality. The basic spheres of the use of financial technologies in digital banking, as well as the factors and prospects of intensifying the use of their instruments in Ukraine are revealed. The main areas of use of financial technologies in digital banking are customer behavior analysis, transaction monitoring, customer identification and segmentation, fraud management, banking services personification, risk assessment and regulatory compliance, customer response analysis, process automation, financial advice, investment decision-making, trade facilitation, syndicated loan services, and P2P transfers. The prospects for developing financial technology tools in digital banking include strengthening the interaction between regulators, banks and financial technology companies, the increased use of biometrics, the development of neo-banking and open banking services.

Purpose The competition among banks in Taiwan is fierce. The financial services offered by banks are highly similar and banks attempt to devise a variety of marketing campaigns to gain brand preferences of bank clients. However, little research regarding bank marketing has applied the segmentation strategy to precisely target bank clients. The purpose of this paper is to explore the moderating roles of cognitive load and brand story style in the impact of bank clients’ affective orientation on brand preference of bank clients. Design/methodology/approach A total of 216 participants who have bank accounts in Taiwan were randomly assigned to a 2 (brand story style: underdog vs top dog) × 2 (cognitive load: low vs high) factorial design. An ANOVA was conducted to examine the interaction effects of affective orientation, cognitive load and brand story style on the brand preference of bank clients. Affective orientation of participants was measured by Affective Orientation Scale. Findings Results demonstrate that for bank clients with low and high affective orientation, advertisements characterized by cognitive load (low vs high) and brand story style (underdog vs top dog) will elicit differential brand preferences of bank clients. Originality/value This is the first research to examine the moderating effects of bank clients’ affective orientation, cognitive load and brand story style on brand preferences of bank clients. Specifically, this research takes up the call to apply bank clients’ personality traits to examine the impact of bank marketing on brand preferences of banks.

Nowadays, Big Data (BD) Analytics is receiving great attention in banking industry, considering the worthy data that have been stored for several decades, to reach the main targets of marketing by increasing the bank’s efficiency of studying their clients, knowing their feedback, in addition to promoting active and passive security systems. This study focuses on utilizing BD analytics to develop marketing intelligence systems. It aims to explore the big data as a valuable resource for Egyptian commercial banks, to improve the customer experience, customer segmentation and profiling, selling products based on profiling, and describing customer behavior. In order to develop the proposed system, data were collected from several banks of transaction performed in 2016, including a report on customer satisfaction, a procedure of analyzing customer satisfaction data, consisting of about 39,000 records of transactions for customers and a collection of about 4,000 records of transaction data for cardholders. These data were analyzed using Apache Hadoop to perform many tasks such as profiling the bank's clients to groups, customer segmentation based on client’s history, interest and habits, predicting customer behavior based on profiling, designing a new marketing strategy, and presenting the right offers to the bank's clients as individuals or as groups. It was concluded that BD analytics were very beneficial for achieving Marketing Intelligence in Banks.

Today, during the period of ongoing changes in the financial market, banks face the challenges of cost reduction, revision of the product line and more explicit customer segmentation. In the environment, corporate clients are also observed significant changes: there is a rotation of personnel change the development strategies of companies that entails new requirements for banking products. Can banks quickly adapt to new market conditions and optimize work with corporate clients using existing technologies and information systems? Besides, that will help improve growth. Corporate sales of banking products in the current conditions? These questions the author tries to answer in her paper.

Abstract This paper examines drivers of mobile banking adoption by analyzing large-scale transaction data of retail banking clients. We find that the overall demand for financial services is associated with faster mobile banking adoption. Moreover, customers who already use online banking services and show digital skills in their payment behavior, tend to adopt mobile banking faster. Also, adoption rates are higher among the young. Finally, the well-documented gender gap in mobile banking adoption appears to have vanished in recent years: towards the end of our period under review, men and women adopt mobile banking equally. Our results contribute to the literature by addressing novel research questions regarding the fastest growing banking channel. Moreover, our findings carry important managerial implications as they help bank managers in the customer segmentation process and the promotion of mobile banking services.

In South Africa, some of the bigger credit issuers operating in the direct marketing environment, specifically non-bank, personal finance companies, have complained that they are not seeing the return-on-investment they have come to expect from their direct mail campaigns, due to poor direct mail response rates. Low response rates have been encountered even though the market segment in which these companies are operating, has shown growing demand. With an increase in mailing costs and fierce competition in the direct marketing industry, lenders are constantly looking for ways to improve the effectiveness and profitability of their mailing campaigns. The approach followed was to analyse the response rate of a mailing campaign, and through regression analysis, determine the relationship between the number of active retail credit accounts held and response rates. This was done against the backdrop of segmentation opportunities and an increasingly credit-active South African population. The results indicate that, in the personal finance and loan marketing environment, there is a positive linear relationship between the number of active retail accounts held by prospective clients and the response rates to credit-related marketing offers. Finally, the implications for direct marketing companies are discussed, limitations presented and future research opportunities outlined.

General theoretical approaches to the content of the concept of multi-channel marketing have been generalized. It is proved that multi-channel marketing differs from multi-channel communications and is a modern and global technology of integration of all components of the marketing complex in the process of interaction with the consumer. It is substantiated that the level of possession of mobile devices in Ukraine and their penetration among the population creates a background for widespread using of Internet channels by service providers. The special relevance of the use of multi-channel marketing takes on the market of banking services because it allows personalizing the contact with the consumer and take into account his or her requirements in terms of access points and convenient time. The data about the increase of non-contact payments in the domestic market and stability of this trend has been displayed in this article. At the same time, the reduction of traditional branches of banks is not always justified, as the consumer must have their own choice as to the convenience of using one or another channel. The expert assessment proved that despite the high cost of maintaining the liaison office has relatively high efficiency among the clients of advanced age. Therefore, against the background of reduction of unprofitable branches, there should be processes of modernization of those that remain on the market from the point of view of conversion into financial service centers. The article proposes a method of constructing a system of multi-channel marketing of a banking institution, which consists of four stages: analysis of large amounts of data on consumer behavior, their preferences regarding the ways and means of connecting to banking services, products and services, the volume, timing and regularity of provision; segmentation of the market and the definition of target segments depending on the level of ownership of mobile devices and information technology, age, income and social activity; optimization of the set of channels from the point of view of maximization of profit and minimization of expenses for their maintenance in the context of each target segment; evaluate the effectiveness of multi-channel interaction and adjustment of the configuration of the channels. It is proved that the main feature of segmentation of consumers in the construction of multi-channel marketing should be the level of ownership and frequency of use of electronic devices. The results of the study may be useful for banking institutions that are trying to build a system of multi-channel marketing.

La fuga de clientes es un problema relevante al que enfrentan las empresas de servicios y que les puede generar pérdidas económicas significativas. Identificar los elementos que llevan a un cliente a dejar de consumir un servicio es una tarea compleja, sin embargo, mediante su comportamiento es posible estimar una probabilidad de fuga asociada a cada uno de ellos. Esta investigación aplica minería de datos para la predicción de la fuga de clientes en una empresa de distribución de gas natural, mediante dos técnicas de machine learning: redes neuronales y support vector machine. Los resultados muestran que mediante la aplicación de estas técnicas es posible identificar los clientes con mayor probabilidad de fuga para tomar sobre estas acciones de retenciónoportunas y focalizadas, minimizando los costos asociados al error en la identificación de estos clientes. Palabras Clave: fuga de clientes, minería de datos, machine learning, distribución de gas natural. Referencias [1]J. 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This article describes how commercial banks use digital marketing technology. The purpose of the research is to study effective methods of digital marketing technologies in commercial banks of Uzbekistan. However, the following problems in the provision of remote banking services by commercial banks in our country affect the quality of commercial banks' services: -non-delivery of banking services to consumers of bank services with effective use of marketing technologies; -Actual segmentation of the client base by static and dynamic attributes; - Increase in sales due to the preparation of personal offers and marketing campaigns in various communication channels; - Increasing control over sales performance due to relevant marketing analytics.

In this paper we have presented analytics as a crucial factor in marketing decision making. The banking environment is turbulent and complex today. The client is well educated and his needs are constantly changing. He has access to lot of information and has power of choice. His digital expectations are high: he needs to access banking products and services from any place and at any time. Also, the client is leaving data everywhere: in bank`s database and at different internet sites. As data are comparative advantages today, the bank is eager to collect them in order to analyze data and make marketing decisions. Analytics is helping bank in this new era of doing business. Analytics assumes analysis, interpretation and communication of understandable patterns in the data. It relies on mathematics and statistics techniques in order to find new knowledge and meaning of existing data. There are many analytics techniques which are based on algorithms and databases. Depending on which problem a bank needs to solve or what it aim wants to achieve, the bank uses one or more analytical techniques. Survival Analysis, Nearest Neighbor Classification, Neural Network, Logistic Regression and Decision Tree are the most common techniques used in banking sector.Marketing analytics models support marketing decisions. Marketing models enable bank to predict outcome (e.g. if a client is likely to leave) or to identify differences between group of clients. In order to achieve results, the bank has created different marketing models such as Response Models, Queues, Retention Models, Market Basket Analysis, and Win-Back Models. Marketing models are helping the bank to predict if the client will answer on offer which bank is offering through marketing campaigns. The aim of these models is to create target group of clients or segment with likelihood to increase their relationship with the bank. In order to create marketing model, the bank defines the aim which it wants to achieve. Usually, the bank wants to keep most profitable clients and to decrease costs. After defining the aim of marketing model, the bank collects, analyses and transforms data needed for creation of the model. Also, it is necessary to estimate data quality. If data are no longer of high quality, there can be issue with model results. Also, the bank has to take into consideration the volume, velocity and variety of data. Large data are collected from lots of data sources and stored in data warehouse or data marts using modern technologies. Model technologies help to convert data into valuable information which can be used for making decisions. After creation of a model, it is necessary to estimate its accuracy, comprehensibility and level of confidence in results given by the model. Also, every model has to be managed (quarterly or yearly) in order to test if the results are still valid or it has to be changed with a new model.Analytics gives competitive advantages to the bank. It can improve effectiveness of processes and organization and improve efficiency in making marketing decisions. The bank as a profit oriented organization tends to contact profitable customer in order to increase their value through customer lifetime value. In this way, the bank has a possibility to invest in relationship with clients which can be valuable in the long run. Analytics gives knowledge about the customer. It helps to discover pattern in large amount of data. The contribution of analytics can be seen in decreasing marketing costs by identifying clients who are likely to respond on marketing campaigns. Also, it contributes in pricing, channel management, selling, segmentation and product development. Today, text analytics is also important for banking business, as lots of data are unstructured and can be found in form of documents, blogs, video sharing and comments on internet sites. In order to use this kind of data, text analytics helps the bank to understand data and read them with certain limitation. However, there are also challenges which the bank faces when implementing analytics. Limited budget, employees without necessary skills for the development of models, poor quality of data, inadequate and unintegrated softer tools, problems with protection of client data as well as imprecisely defined aim of model can be resulted in unsatisfactory realization and poor position of analytics in the bank. In order to overcome these challenges, the bank needs to set up a strategy of analytics and to link it with all the internal processes in organization.

The banking sector has undergone a major revolution with the advent of digital transformation. The entry of Fintech and tech giants such as Google, Amazon, and Facebook have introduced convenient banking that is easy to understand and use. In this focused condition, banks are understanding the significance of client care and fulfillment and need to give close consideration to the Voice of Customer to improve client experience. By dissecting and getting bits of knowledge from client input, banks will have better data to settle on key choices. In their quest to better understand their customers, banks are seeking artificial intelligence (AI) solutions in the form the of sentiment analysis. What is sentiment analysis? In simple words, sentiment analysis is the process of detecting a customer’s reaction to a product, brand, situation or event through texts, posts, reviews, and other digital content. Using sentiment analysis, business leaders can gain deep insight into how their customers think and feel. The analysis can help in tracking customer opinions over a period of time, determine customer segmentation, plan product improvements, prioritize customer service issues, and many more business use cases