Tài liệu Consumer analytics toward development of cross selling products at retail banking an approach on big data at eximbank

MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HO CHI MINH CITY TO PHUC NGUYEN KHUONG CONSUMER ANALYTICS TOWARD DEVELOPMENT OF CROSS SELLING PRODUCTS AT RETAIL BANKING: AN APPROACH ON BIG DATA AT EXIMBANK MASTER THESIS Ho Chi Minh City – 2020 MINISTRY OF EDUCATION AND TRAINING UNIVERSITY OF ECONOMICS HO CHI MINH CITY TO PHUC NGUYEN KHUONG CONSUMER ANALYTICS TOWARD DEVELOPMENT OF CROSS SELLING PRODUCTS AT RETAIL BANKING: AN APPROACH ON BIG DATA AT EXIMBANK Specialization: Business Administration Executive Business Administration Code : 8340101 TUTOR: ASSOC.PROF. DR. TU VAN BINH Ho Chi Minh City - 2020 COMMITMENT I assure you this is my own research. The figures and results stated in the thesis are honest and have not been published in any other works. I would like to assure you that all of the help for the implementation of this dissertation was thanked and the information cited in the thesis has been traced. Trainees implement the thesis (Sign and write full name) TÔ PHÚC NGUYÊN KHƯƠNG ABSTRACT Based on internal database as big data of the Eximbank, 3,527 active customers with the initial length of service more than 12 months, the method of data mining is concerned, in which the mathematic methods of K-Means of cluster, Tree Decision, and Association Analysis are applied. The findings show that consumers’ characteristics using services at EXIMBANK are various, in which staffs, directors as individual customers occupy a high proportion in total customers. Based on descriptive statistics, there are eight main products, such as VG (Visa Gold), VC (Visa Classis), MG (Master Gold), MS (Master Standard), VP (Visa Platinum), VV (Viva Violet Card), VA (Visa Auto Card), and others are concerned most by the customer, in which VG and VC are the two top cards used. Directors are more interested in VG card, while staffs concern VC card. In addition, using two cards, called the main card and the extra card, is popular. This is a potential chance for the bank to develop cross-selling products, which the product bundle strategies are packed into groups. Based on the method Association Analysis, propobality of using two cards or three cards at the same time of customers are derived. This finding basically supports the bank to address cross-selling products of cards to customers. To do this, recommendations of the strategies of bundle products are suggested in the thesis, together with implementation plans TABLE OF CONTENTS COMMITMENT ABSTRACT TABLE OF CONTENTS LIST OF FIGURES LIST OF TABLES CHAPTER 1: INTRODUCTION...........................................................................................1 I. II. III. IV. V. RATIONALE OF RESEARCH .............................................................. 1 OBJECTIVES OF STUDY .................................................................... 2 RESEARCH QUESTIONS .................................................................... 2 SCOPE AND LIMITATION.................................................................. 3 RESEARCH METHODOLOGY ............................................................ 3 Data collection............................................................................................. 3 Methods to analyze data ................................................................................ 5 CHAPTER 2: CONCEPTS CONCERNED AND ITS FRAMEWORK ..................... 7 I. CONCEPTUAL FRAMEWORK............................................................ 7 Service retention.......................................................................................... 7 Remaining good relationships between the customer and retail banking ........... 8 1.The concept of big data and its consideration in banking ....................... 8 2. Big data in Banking and its contribution ............................................... 9 Application of Big Data to investigate the customer’s spending habits ............. 10 Customer segmentation and review customer’s records .................................. 10 The sales includes other services (service cross-selling) ................................. 11 Building a system to record customer feedback ............................................. 11 Personalized marketing .............................................................................. 12 Flexible suggesting good services to customers ............................................. 12 II. CONCEPTS RELATED TO APPROACHED QUANTITATIVE METHODS......................................................................................... 13 1. RFM ............................................................................................. 13 2. Customer Value Matrix Model ........................................................ 14 3. Customer lifetime value based on RFM ............................................ 16 4. Customer segmentation................................................................... 18 5. Cross-selling products..................................................................... 19 CHAPTER 3: BIG DATA OF BANKING INDUSTRY AND CONCEPTS APPROACHED………………………………………………………………………21 I. SITUATION OF EXIMBANK AND ITS BUSINESS ............................ 21 II. BIG DATA AND ITS APPLICATION IN BANKING........................... 25 III. DATA ANALYTICS ............................................................................ 29 1.PROFILE OF CUSTOMER.................................................................... 29 2.RMF AND MARKET SEGMENT.......................................................... 33 Cross – Selling strategy ............................................................................... 38 Summary of chapter.................................................................................... 43 CHAPTER 4: CONCLUSION AND SOLUTIONS ............................................... 44 I. II. III. IV. V. GENERAL CONCLUSION ................................................................. 44 FINDING OF MARKET SEGMENTS.................................................. 44 DETAILED CHARACTERISTICS BY MARKET SEGMENT .............. 45 IDEAS OF MARKETING STRATEGY............................................... 46 SOLUTIONS..................................................................................... 46 1. PRODUCT STRATEGY ...................................................................... 46 2. PRICE STRATEGY............................................................................. 48 3. PROMOTION STRATEGY .................................................................. 49 4. PROCESS STRATEGY ........................................................................ 50 VI. EMPLICATION OF STATEGY........................................................... 48 REFERENCE ......................................................................................................... Web link:.............................................................................................................. APPENDIX ............................................................................................................ LIST OF FIGURES Figure 2.1: Customer value matrix and its description ............................................ 15 Figure 2.2: Customer value matrix and its description............................................ 16 Figure 2.3: The diagram of proposed model .......................................................... 18 Figure 3.1: Share of investment in big data analytics by sector................................ 26 Figure 3.2: Share of investment in big data analytics by sector................................ 26 Figure 3.3: Position of customer .......................................................................... 29 Figure 3.4: Type of products that consumers concerned ......................................... 30 Figure 3.5: Length of service of active customers .................................................. 31 Figure 3.6: Type of customers using services at the bank ....................................... 32 Figure 3.7: Type of products that consumers concerned ......................................... 33 Figure 3.8: Matrix of LOS and Recency ............................................................... 34 Figure 3.9: Market segments based on RFM ......................................................... 36 Figure 3.10: Result of Tree Decision of five market segments ................................ 38 Figure 3.11: Result of Association Analysis and its potential bundles...................... 39 Figure 3.12: Characteristics of potential bundle product between MS and VA.......... 40 Figure 3.13: Potential bundle strategy (MS-VA) by customer income...................... 41 Figure 3.14: Characteristics of potential bundle product among VP, MS, and VA..... 41 Figure 3.15: Potential bundle strategy (VP-MS-VA) by customer’s income ............. 42 Figure 3.16: Characteristics of potential bundle product among MG, VV, and VA ... 43 Figure 3.17: Potential bundle strategy (MG-VV-VA) by customer’s income ............ 43 Figure 4.1: Ranges of account balance of customers .............................................. 45 Figure 4.2: Marketing strategies suggested to the bank........................................... 46 Figure 4.3: Type of product................................................................................. 47 Figure 4.4: Top prestigious banks in Vietnam ....................................................... 48 LIST OF TABLES Table 1.1: Information of variables concerned 4 Table 2.1: Recency, Frequency and Monetary Score Description 14 Table 2.2: Presentation of customer value matrix 15 Table 4.1: Interest rate of top ten prestigious bank in Vietnam 49 Table 4.2: Plan of strategic implication 53 1 CHAPTER 1: INTRODUCTION I. RATIONALE OF RESEARCH Rapid development of information technology in the banking industry has created a big concern that the banks must think of how to explore internal data to serve competitive strategies. In fact, currently, most banking institutions, insurances and financial services are attempts to adopt a new approach toward data mining to the development and innovation of services that they provide to customers. Like most other industries, big data analytics are going to be a major change to support business units to generate campaigns for a short term and long term strategies to attract more new customers, retain existing ones and fight against the competitors. Investing a big data system cause simulation that data mining is much concerned in the banking industry, because it supports to extract valuable information form huge amounts of data. Particularly it contributes into finding out consumer behavior and present a market situation picture of a firm. However, it is not easy to explore big data of firms. Data scientists developed quantitative methods as mathematic approaches, such as descriptive and predictive analytics, etc. Nowadays, banks have realized the importance of customer relation, this is one of successful factors. However, challenges of how to retain most profitable customers and to reduce a churn rate are problematic. To solve this problem, consumer behavior should be investigated and analyzed, which big data are a worth resource and quite helpful to measure and predict consumer behavior correctly. Therefore, the power of data is to derive utility across various spheres of their functioning, product across selling, regulatory compliance management, risk management, and customer service management. As we know, particularities of banks’ activities generate a huge amount of data from unstructured data, such as transaction history, customer to the unstructured data such as the customer’s activities on the website, or the mobile banking application on social networks. However, how to explore big data available is the most problem. Although there are not few banks who recognize that and want to turn the big data available to the most effective weapon for the market competition, they are seemly facing problems of new system, skills, and so on. With changes in integration policies of Vietnam through information technology system, together with fire competition, more and more banks in Vietnam have paid more attention to investing a huge money for big data system and people capacity. For example, EXIMBAK is one of the banks, who is willing to pay money to structure data 2 system very early. It realizes benefits of data warehouse to support business strategies. In fact, the benefits of internal brought not the raise of the internal management efficiency, but also help increase the competitive advantages, maximize profits. Currently, the banks must be flexible for their plan toward activities of innovation, in term of capturing needs of customers and improving satisfaction and retention of customer. By the way, CRM is quite helpful and recognized for acquisition and retention of customers. As a result, the bank can get more opportunism to make long lasting and profitable relationships with customers. To investigating big data system of the bank, Eximbank has offered a program of building capacity for staffs, who are directly related to data analytics and business development. In addition, the department of database is established to exploit internal database, which the role of an employee is placed in the ecosystem talent development. However, everything is seemly concerned and exploited more and more toward data mining, predictive analytics on customer behavior. II. III. OBJECTIVES OF STUDY - Presenting characteristics of consumers who are using services of banking - Analyzing history of transaction behavior of consumers - Identifying and developing market segments through consumer behavior - Developing developments of cross-selling products to increase benefits of customers toward customer retain. RESEARCH QUESTIONS There are some questions concerned as follows (1) What are characteristics of consumer toward service usage at banking? (2) How do customers take transaction at the bank? (3) How is the market segment developed? (4) What are cross-selling product developments to increase benefits of customers to retain them? 3 IV. SCOPE AND LIMITATION The thesis only concerns on internal database as well as big data of Eximbank located in a district of Ho Chi Minh City. Due to the confidential information and secret information requirements, the name of district is asked to hidden. Even the study is not taken qualitative method into the study, because the author is the head/leader of the branch of Eximbank, what the thesis is done it is based on actual demand of the bank. Its finding is actual expectation for the Eximbank’s business plan in the time coming. V. RESEARCH METHODOLOGY Data collection Data used are extracted from data warehouse of the bank. Because of security requested, this thesis is seriously asked to be confidential. Using information as well as findings of this study is not convinced, it must be responsible for who going to use that. Data pre-processing is taken into account of the data mining process, because it improves the accuracy and efficiency of subsequent modeling (Han & Kamber, 2006). Activities of data pre-processing techniques are data cleaning, data transformation, data integration and data reduction, these are concerned, due to data quality. The database used in the study is extracted from data warehouse of the bank, which the period of study is 12 months, from January 1, 2019 to December 31, 2019. Accordingly, the database selected has 130,000 rows, equivalent to 3,527 customers. This means one individual customer has more transactions during study, so the rows are more than the customer amount. As presented in table 1.1, 25 fields or variables are taken into account of the current study. Each one has a defined measurement, such as nominal, continuous, date, categorical. 4 Table 1.1: Information of variables concerned Name of variable Definition 1. CIF ID of customer 2. Branch ID of branch 3. No.document ID of customer document 4. Type of customer Type of customer + Credit customer Measurement Nominal variable + Individual customer + family business customer 5. Access date Date to register service of customer date 6. Approved date Date of documents approved date 7. Document score Score on initial documents of customer Continuous that the staff evaluates 8. Gender Gender of customer Nominal 9. Age Age in years Continuous Education of customer Nominal Behavior community relations Nominal 10. Education 11. Community + Prestige + Good enough + Unknown 12. Marital status Marital status: Single and married Nominal 13. Job position Positions Nominal + Manager + Worker + Director + Office staff 14. House ownership Status of house property of customers + Owned house Nominal 5 + Rented house + Stayed with parents’ house 15. Usage Time using loan services from banks Nominal + only loan services from Eximbank + loan services from Eximbank > another bank + loan services from Eximbank = another bank + loan services from Eximbank < another bank + No loan services from Eximbank 16. Type of product Type of loans Nominal 17. Type of card Type of card: Main and non-main Nominal 18. Level of card Value of card level Continuous 19. Balance account Balance account measured in Vietnamese Continuous currency (VND) 20. Fee Annual fee (Yes/No) Nominal 21. Card_date Date to issue the bank card Date 22. No.Card Account number of card Series number 23. Ncard_Date Date to approve the new card Date 24. Workingyear Number of years of customers working Continuous 25. Income Income of VND/month customer measured Continuous Methods to analyze data Data mining is one of tools concerned toward descriptive statistics. In addition, clustering techniques is employed to cluster customers into groups, which it to satisfy two main criteria: (i) each group or cluster is homogeneous; (ii) each group or cluster should be different from other clusters. This method mainly gains customer 6 segmentation. In term of classification method, the cluster of K-means is employed to segment customers (Khajvand & Tarokh, 2011). Input variables recruited in the cluster method are generated by the RFM indicator, which R as Recency, F as Frequency, M as Monetary are generated. Since segmentation is on the basis of Recency – Frequency – Monetary (RFM), the selected features of data to meet RFM is included last transaction date (purchased), count transaction (purchase) and total monetary that customer took loan during one year and count item which refers to variety of customer taken transaction. To count transaction, it is the frequency of customer transactions. In data transformation, the data is transformed in a way that can be exploited by data mining tools. C&R Tree as “Classification and Regression Tree” is also employed to investigate customer behavior in more detailed. It is supported to develop classification systems toward prediction based on a set of decision rules. Application of this method known as rule induction brings several advantages. Based on this the marketer can think and develop campaigns to retain customers and stimulate their consumption. Association Rules as the method of Association Analysis, it presents an association based on antecedent and consequent. It means that once the antecedent is true, then the consequent is also true. Association rules present a probabilistic rule and modify probability of consequent happened exactly, given that the probability of “antecedent” is truly happened. Suppose there are two inputs of X and Y employed in the method. Results of Association Analysis give indicators, which the confidence and the lift are concerned in this study. The confidence is defined as the conditional probability to find in a group Y having found X, while the lift presents whether the association between X and Y is positive or not. With lift ≥ 1 it gives a message of positive association between X and Y, otherwise negative association is happened. 7 CHAPTER 2: CONCEPTS CONCERNED AND ITS FRAMEWORK I. CONCEPTUAL FRAMEWORK Service retention Retaining current customers and returning them to loyalty has played an important role to save and reduce business cost. To obtain this, currently banks have used a variety of advanced analytical tools to find out customer behavior and grow customer relationships for maximizing profitability. Because of the competitive market, most commercial banks pay more attention to analytics on database available that determine habits of top performers, rather than what drive customers, and looking forward to application of big data in spheres like front office risk management to back office trade operations. Some of them calls over in-person meetings to investigate necessary information. In addition, to retain the customers, banks are wise to investigate a broad spectrum of data and measure data points across multiple segments of clients. Once data are deeply explored, the company can get higher efficiency with a low cost. In fact, many types of costs can be accursed in the business unit, e.g. costs of advertising to entice new customers, costs of a personal selling pitch to new prospects, costs of communication to explain business procedure for new clients and dealing. These costs can be reduced if the business units have a good approach on data mining. For example, banks in Vietnam have a high investment on big data system to record customer behavior from different channels. Loyalty in retail banking: Customer loyalty is one of criteria that any company is concerned very much. However, still some bankers have a little agreement among them to what behaviors constitute customer loyalty and how best to stimulate these behaviors. However it is not easy to earn loyalty, because of fierce competition. This is reason the most bankers want to be typically of product-oriented programs. Still many retail bankers are not clear of thinking of the customer loyalty with two distinct, e.g. customer stratification and customer retention. As the following, classification of these two definitions are presented. Customer satisfaction: what the customer is expecting, she or he is fulfilled, which his or his needs, wishes or desires on products or services are met. However, it is not convinced to be sure guarantee of retention or loyalty (Szus & Tóth, 2008). Srivastava & Gopalkrishnan (2015) used big data in Indian banks to analyze the customer satisfaction measurement. Accordingly findings showed poor services and other issues 8 that customers complain. Based on that marketing strategies are suggested to Indian banks. Customer retention: Unlike customer satisfaction and customer loyalty, customer retention is an ability to remain customer over time and measures a relationship between the customer and the firm. Once customer is retained longer, expectation of loyalty is possible (Jaiswal, et al., 2018). However, according to Kumar, et al. (2013), indicating customer satisfaction offers only small portion of variance in loyalty, but not convince much to enhance customer retentions. Retail banks are often guilty of mistaking customer inertia for loyalty. Correct cognition on customers can be on the four quadrants of behavior-attitude scale (Truly Loyal, Accessible, Trapped, Higher Risk), also must be awareness of the differences between the behaviors that customers display their attitudes toward the bank. Once the customer is trapped, but not loyalty, they can be remained for a long term. But this customer can leave any time once it realizes the bank in low esteem. As a result, to understand the degree of loyalty gaps, the bankers need to know very well the characteristics of customers as consumption behavior and how to connect between satisfaction and retention. As argued by Szus & Tóth (2008), it is not easy to know that customer loyalty can be possible to be displayed as customer retention. Remaining good relationships between the customer and retail banking Because of fire competition, retail banks have paid more attention to revamping loyalty programs toward customer. This is strongly considered toward incorporating a reward system into the bank’s plans. Currently, many retail banks offer a significant number of potential rewards to promote and solidify customer loyalty. The banks develop a relationship based on two-way street, which the customer will remain relationship only once there is value in doing so. However, once the customers’ purchase volume is increased and beneficial relationship gradually takes shape, the customers’ relationship with banks is remained (Jaiswal, et al., 2018) 1. The concept of big data and its consideration in banking There are many studies using big data to analyze customer consumption (Khajvand & Tarokh, 2011), these studies mention that big data as the tool allow a business unit to manipulate, create and manage huge data sets, also it is stored and required to support the volume of data, characterized by variety, volume and velocity1. Big data can be both structure and unstructured with the large volume of data. It records activities designed in the system. If doing business, this data is recorded consumption 1. 1 Meta Group. Application Delivery Strategies; February 2001 9 history of customers during period, it is enriched on a business a second-to second or day-to-day basis by time. It will change our world completely and is not a passing fad will a way. It is what organizations can use to investigate on business issues, management issues or/and other sectors. Banks internationally are beginning to harness the power of data in order to derive utility across various spheres of their functioning, ranging from sentiment analysis, product cross selling, regulatory compliances management, reputational risk management, financial crime management and much more (Srivastava & Gopalkrishnan, 2015). Big Data play an important role to extract the data value and support to obtain better decisions. Then, the high cost of running time, which will make the problem difficult to solve, can be avoided. Through these techniques, financial companies will have less risk when predicting that customers will be successful in their payments. So more people can get access to credit loans. 2. Big data in Banking and its contribution Once the number of customer increases, this affects to a certain extent, the bank need to think of providing quality. Practice shows that the analysis of existing data has simplified the process of monitoring and assessing customers' credit banks and financial institutions, based on large volumes of data such as information, dossiers personal and other confidential information. But with the big data available, banks can exploit to continuously monitor the behavior of the customer in real time, identify sources of data required to collect service of offering solutions Justice. Evaluation process customer records in real time will gradually boost operational efficiency and profitability, thereby promoting further organizational development. According to Forbes, 87% of companies consider Big Data will create major changes to their industry until the end of the 2nd decade of the 21st century. Even the company also think that without consideration on big data analytics and specific strategies based on empirical analysis will effectively make them fall in its business. There are many sources of Big data in most sectors and in different areas, not just in the banking sector and financial services. Every interaction, every transaction of customers at banks create electronic records, the backups are saved according to legal regulations, and transactions in the office ATMs in different locations as well information stored in the bank. Thanks to analyze Big Data, companies financial services no longer store the data as required mandatory as in the past but now they are active, more active in the extraction to get the results that are based on that offer solutions to improve operations, increase the profitability of the organization. In short, Big Data is an important resource, its nature core creates competitive advantage in any one financial institution does especially when capturing the needs of consumers increasingly complex though was more convenient, easier thanks to the boom of 10 technology and engineering. Big data will not only bring the new look, the creation of the innovation process for each type of service to the customers but also ensure business efficiency, risk and cost are minimized. Identifying array of services, parts, functions within financial institutions, where Big Data can be reviewed for the purpose exploited most efficiently based on a combination of knowledge, business model and the ability to apply software technologies to create competitive opportunities for the organization. Depending on the purpose, structure, resources, capabilities vary from organization that will be more cases big data applications ranging nature special, separately. Here are the use cases are the most popular - are we research and selection - which banks and companies financial services are performed to identify the value hidden deep inside the analysis of Big data. Application of Big Data to investigate the customer’s spending habits The bank has the ability to directly access information resources, abundant historical data related to the habits and behaviors of customer spending. The bank also holds information on the amount a customer is paid how much for example specific salary per month, the amount to be transferred into a savings account, the money was paid to the company provide utilities (e.g. electricity companies, companies providing internet services, ..), while customers using banking services, etc. This provides a basis, opportunities for banks access and deeper data analytics. Applying the information screening function (filter function), for example as the filter time or holiday celebrations and macroeconomic conditions (e.g. inflation, unemployment, .. ), the reasons for bank employees can understand the cause of the impact as customer wage increases or decreases and spending capacity of customers change how. This is one of the fundamental elements for the process of risk assessment, screening, evaluation profile lenders, evaluate the possibility of mortgage and offer financial products other (crossselling) to customers as insurance. Banks benefit a lot if they know the information customers to withdraw cash - all the money has been on payday - or if they want to keep the money on a credit card (credit card) / debit (debit card). Take advantage of that, banks can reach customers, expand service with the proposal, to attract customers to invest in short-term loans with high payout ratio and the appropriate interest rate, etc. Customer segmentation and review customer’s records Once the initial analytics of the spending habits of customers with identifying the type of service, channel transactions are priority customers (i.e. customers who want savings or want to invest in loans), the will get a database serving the segment and classify customers as appropriate based on the information and documents supplied by clients. Customers who spend comfortable ease, investors cautious yet thorough, 11 customers pay the debts quickly, customers began to repay the maturing, Big Data will give banks the knowledge, expertise and deep habits spending patterns of customers, simplifying the task of identifying the needs and wants of them. By being able to track each transaction by customers, the bank’s will be able to classify customers based on various parameters, including services often customers use, time to use the service, spending habits when using a credit card or even a net asset value (net worth - income plus the value of customer assets minus liabilities). Benefits that bring customer segmentation is that it allows banks to target customers with better marketing campaigns relevant is designed to meet the exact needs of the customer. Data analysis capabilities for Big Data rising companies and organizations BFSI grasp the need to find hidden within each customer (customer insights) thereby creating customer segments. However, the collection and evaluation of information the requirements are investment in the infrastructure of the organization as well as investment in affiliate network between all employees of departments, functional parts of the organization technology, advanced engineering software for the process of exploiting Big Data. The sales include other services (service cross-selling) Based on a database which banks can attract or retain customers by introducing more other services. For example, banks can introduce investments with attractive interest rates to customers who have idle money or investors’ always careful consideration in making investment decisions. Or the bank has proposed short-term loans to customers who have a habit of spending "comfortable" for the needs of their daily consumption or customers who are having difficulty in paying old debts. Analyzing correctly on the profile of the customer, the bank can cross-sell other services more efficient and attract more customers with better deals to be "personalized" to focus precisely on demand customer demand more efficiently, thereby increasing revenue for the company. Building a system to record customer feedback Customers can leave feedback after every transaction or every time to get advice from the customer call center or via the feedback form, but often to share ideas through social media, e.g. Facebook, Zalo ... Big Data tools can search for sifting through the information and feedback publicly on the social media and collect all the data mentioned on the brand of the bank to be able to respond quickly and fully to customers. Also supports prevent rumors affecting business operations and customer confidence in banks, this was already happened in some Vietnamese banks currently. Once customers think that the bank is willing listen to them and appreciate their comments as ideas related to service improvements, the bank can create more chances to retain its customers. To do this better, the banks should build a data center - storage 12 centers all the interactions customers have with brands including private data base, transaction history, browsing history, services, etc. with the purposes are to support campaign developments to meet what the customers are expecting to increase their loyalty. Personalized marketing As previously mentioned, the application of Big Data analytics into business strategy of bank plays an important role to identify needs of each customer based on its comments, feedback. In addition, consumption behavior is significantly explored through transaction history. As a result, if the consumption information is clarified, it is quite well to support the bank in direction of personalized marketing to individual customers. Once customer segmentation is concerned by big data, banks can take advantages to personalized marketing to target customers based on understanding of personal spending habits of them. In addition, to obtain good database on the transaction history of customers, the banking needs to combine data unstructured - a form of data Big Data obtained from social networking or social media such as customer profile on Facebook. To get a more complete picture of the needs of customers based on the analysis of the psychological desire at all times point. On the other hand, the data of the customer in the background social media or social applications other smart will help banks analyze the risks that may occur, but consider whether to provide loans or outside the evaluation of dossiers as usual. After analyzing and understanding the specific needs and particular of each customer, the bank should continue to segment even further and provide solutions, marketing plans accordingly to thereby obtain response rate recovery, higher conversion rate from each customer. For example, banks use the e-mail marketing to be sent to customers the latest information about the services for short-term loans with interest rates moderate, or deposits with attractive interest rates, or chapters the other privileges, ... the creation of products and services provided to each customer segment, or even each specific client will help banks build brand image and build a good relationship in each client. Flexible suggesting good services to customers System Big Data can be a complex system linking various parts different functions, but its job is to simplify the tasks of an organization officials. Whenever a customer name or account number is entered into the system, the system of Big Data will support screening all data and transmissions or provide the data required to serve the process analysis. This will enable banks to optimize workflow and save both time and costs. Big Data will also allow the organization to identify and fix problems before they affect their customers.