A case study of predicting banking customers behaviour by using data mining

Data Mining (DM) is a technique that examines information stored in large database or data warehouse and find the patterns or trends in the data that are not yet known or suspected. DM techniques have been applied to a variety of different domains including Customer Relationship Management CRM). In this research, a new Customer Knowledge Management (CKM) framework based on data mining is proposed. The proposed data mining framework in this study manages relationships between banking organizations and their customers. Two typical data mining techniques - Neural Network and Association Rules - are applied to predict the behavior of customers and to increase the decision-making processes for recalling valued customers in banking industries. The experiments on the real world dataset are conducted and the different metrics are used to evaluate the performances of the two data mining models. The results indicate that the Neural Network model achieves better accuracy but takes longer time to train the model

THE BORDER BETWEEN BUSINESS INTELLIGENCE AND PSYCHOLOGY- SEGMENTATION BASED ON CUSTOMER BEHAVIOR

In today’s economy, marketers have been facing two challenging trends: fierce competition between companies offering essentially similar products, and dealing with customers that are increasingly informed and demanding, but less and less loyal. Under these conditions, it has become imperative for managers and for marketing professionals to invest in business intelligence in order to find patterns in the consumers’ behavior that could predict their future buying decisions. In this report we have presented how Decision Support Systems, data analysis and customer segmentation can help companies to know their customers better in order to predict (and influence) their future actions. At the same time, we have argued that Business Intelligence should meet psychology and neurology halfway, and accept that there is a very high emotional subconscious component that produces a high degree of unpredictability in consumers’ behavior.DSS, business intelligence, consumer behavior, segmentation, buying decision process

Does segmentation always improve model performance in credit scoring?

Credit scoring allows for the credit risk assessment of bank customers. A single scoring model (scorecard) can be developed for the entire customer population, e.g. using logistic regression. However, it is often expected that segmentation, i.e. dividing the population into several groups and building separate scorecards for them, will improve the model performance. The most common statistical methods for segmentation are the two-step approaches, where logistic regression follows Classification and Regression Trees (CART) or Chi-squared Automatic Interaction Detection (CHAID) trees etc. In this research, the two-step approaches are applied as well as a new, simultaneous method, in which both segmentation and scorecards are optimised at the same time: Logistic Trees with Unbiased Selection (LOTUS). For reference purposes, a single-scorecard model is used. The above-mentioned methods are applied to the data provided by two of the major UK banks and one of the European credit bureaus. The model performance measures are then compared to examine whether there is improvement due to the segmentation methods used. It is found that segmentation does not always improve model performance in credit scoring: for none of the analysed real-world datasets, the multi-scorecard models perform considerably better than the single-scorecard ones. Moreover, in this application, there is no difference in performance between the two-step and simultaneous approache

Telecom customer segmentation and precise package design by using data mining

Changes in the form of communication have prompted the telecommunications industry to flourish. In the "big data era" of information explosion, as one of the leading industries in the information age, the development of the telecommunications industry depends not only on communication technology, but also on the ability of enterprises to optimize resource allocation. At present, the information resources owned by telecom companies mainly come from customers. During the development process, they have accumulated a large amount of customer data, which truly and objectively reflects the behavior of consumers. This paper is dedicated to combining data mining technology with the rich data resources of the telecom industry and the latest marketing theories, not only effectively helping subdivide the telecommunications customer market, but also supporting telecommunications companies in developing more accurate and efficient marketing strategies. In addition, data analysis method such as factor analysis, regression analysis and discriminant analysis are used to analyze the demographic, business, SMS messages and expense characteristics of telecom customers, providing a new vision and reference for the telecom industry to achieve accurate packaging design. Based on the above research results, a discriminant model for the loss of telecom customers is constructed, which will help telecommunications companies to obtain a control method for telecom customer management risk. At last, data mining technology is used to optimize the combination design of telecommunication services, which offer effective advice on precise telecom package design to telecommunications companies

The research on customer structure characteristics and marketing measures of regional bank agency: a case from the Agricultural Bank of China

With intensified opening degree and increasingly fierce market competition of commercial banks, commercial banks innovate their products constantly and improve their service quality at the same time. The Agricultural Bank of China (ABC) is a state-owned commercial bank that has built branches in all county-level districts. Instead, branches of ABC in county-level have become the weakest links that reduce ABC’s competitive power. If the flaws in customer and market maintenance in the county-level branches are ever to be repaired, in my opinion, meeting customer perceived service quality and customer demands efficiently based on understanding of customer needs should be put in the first priority currently. Firstly, this part studies the customer segmentation of ** branch of Agricultural Bank of China. This thesis puts forward approach to segment bank customers based on the improved k-means clustering. The results show that the improvement algorithm effectively overcomes the defect that traditional k-means algorithm easily falls into local optimal value, increasing the accuracy of customer classification, and contributing to more reasonable clustering results. Secondly, this thesis uses the econometric panel data model to study the relationship between customer structure and bank performance. The results indicate that a good customer structure can bring benefits for banks and improve their competitiveness. Thirdly, this part analyzes different service quality requirements of different types customer in the ** branch of Agricultural Bank of China. We combine service quality evaluation theory and the background of Chinese commercial banks, establishing the SERVQUAL model for the ** branch. The Study has shown that the correlation coefficient between overall perception of service quality and customer satisfaction is positive; the overall perception of service quality and customer willingness to recommend are also positively correlated, but the degree of correlation is lower than the correlation between the overall perception of service quality and customer satisfaction; the correlation of overall perceived quality of service for all samples and willingness to accept the services of other banks correlation was not significant. At the same time, there is still a gap between the customer perceived service quality and customer expectation in the ** branch of Agricultural Bank of China. Finally, according to the results of customer structure classification and service quality survey of the ** branch of Agricultural Bank of China, the marketing strategies for different customer groups are proposed.Com o crescente grau de comercialização da indústria bancária chinesa e a entrada continua de bancos estrangeiros, a competição entre bancos está a tornar-se cada vez mais feroz, e as estratégias dos bancos comerciais com vista a ganhar vantagens competitivas muda gradualmente. Para além do lançamento de uma variedade de produtos financeiros, os bancos comerciais utilizam serviços diferenciados para poder dar resposta á procura do mercado diversificado de consumidores. Estes bancos estão igualmente a começar a entender que para os bancos gradualmente convergirem devem não só atingir uma vantagem competitiva através da oferta de produtos financeiros bem como serviços diferenciados de alta qualidade. Este meio tornou-se na única forma forma que o banco dispõe para poder vencer a sua competição. Portanto, para os bancos comerciais, estamos num período de inovação onde o aumento da qualidade de serviço é inevitável. O Agricultural Bank of China é um banco comercial do estado que possui uma filial em todas as regiões administrativas a nível de condado. Ligações e serviços, citadinos e urbanos tem sido a maior vantagem do Agricultural Bank of China, mas a situação actual não é favorável. A filial a nível de condado tem-se tornado na ligação pior e mais fraca da fundação deste banco. Ao mesmo tempo, bancos privados têm emergido em paridade com o rápido desenvolvimento dos instrumentos financeiros online e, o Agricultural Bank of China, como o representante dos bancos tradicionais está a enfrentar competição feroz. Em especial desvantagem no que toca a recursos ao consumidor e instrumentos online que os outros bancos oferecem. Os bancos comerciais tradicionais, estão desta forma confrontados com a perda de clientes bem como o elevado custo de adquirir novos clientes. O risco operacional do banco aumenta á medida que a estabilidade do mercado consumidor piora. Se querem mudar o status quo das filiais a nível de condado, necessitam entender a actual necessidade da qualidade de serviço ao cliente, analisar as características da procura do consumidor e estabelecer um mecanismo de ciclo virtuoso de mercado-consumidor-beneficio - são as maiores prioridades agora. Baseado nisto, este estudo usará marketing, processo de decisão da gerência, teoria e métodos, mineração de dados, técnicas estatísticas e métodos econométricos para analisar as características de procura do consumidor do Agricultural Bank of China. Primeiro, utilizar a análise de cluster de mineração de dados para efectuar uma estratificação analítica do grupo de consumidores do banco para manter a estrutura da procura dos consumidores e serviços; classificação da informação de procura dos consumidores, acesso ás tendências de procura dos consumidores do banco e tendência de produtos competitivos; na base de quantificar os requerimentos do consumidor, usamos o painel de dados econométricos para efectuar uma análise empírica sobre a estrutura de procura dos consumidores e a performance do Agricultural Bank of China