Download Analyze eCRM among Securities Companies in China based on Data

Analyze eCRM among Securities Companies in China based on Data
Mining Techniques
Yuan, L. a,
He, X.F. a, b, *
The Key R&D Center for Finance of Chongqing, Chongqing Institute of Technology,
Chongqing, 400050, China
b
The State Key Laboratory of Arid Agroecology, School of Life Science, Lanzhou University,
Gansu Province 730000, China
a,
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Abstract e-Customer Relationship Management eCRM is the new strategy of customer relationship
management in security trade in the Internet era. Current CRM system overweigh the automation of
business activity more, and it fails to find information of database. Data mining technology has helped
fuel the growth of CRM strategies of securities company. The article introduces CRM of securities
company, data mining and the relationship between them, and points out the inevitability and feasibility
of implementing CRM for stockjobber in China. Data Mining can forecast the trend and behaviors,
thereby nicely support the securities brokers’s decisions. This research presents the application of eCRM
based on data mining in security trade for keeping good relationships with customers and promoting
core competitiveness. Data mining process the data and find information efficiently. Data mining make
CRM system become administrator’s real decision and analysis tool.
Keywords Security Trade; Customer Relationship Management (CRM); e-Business; Data Mining
1. Introduction
The growing waves of merger and acquisition in the international business arena, coupled with
broader use of e – technologies, have contributed to increase in the scale of business relationships of
securities company and also to exponential increase in customer data. From the year of 2001, with the
changes of market and operation environment in China, the profits of securities company’s main
management items have decreased obviously. Particularly, China Security Regulatory Commission’s
released the transaction commission percentage in 2002 year, the securities traders in China was
beginning to realize the necessity to understand their customers and to know themselves. Within it, the
economics of customer relationships are changing in fundamental ways, and companies are facing the
need to implement new solutions and strategies that address these changes ( Rygielski, 2002).
With the heating-up of competition in securities, it becomes easier to lose customers. To exploit
business opportunities, more and more securities companies are rapidly implementing integrated CRM
systems to make their business processes more customer – focused. Therefore, it is necessary to employ
the customer relationship management function and new technologies to discover valuable customers,
develop new customers, to rationally allocate the limited resources to achieve maximum profits, and
eventually to maintain advantageous position in the market. According to Rygielski (2002), technologies
such as data warehousing, data mining have made customer relationship management a new area where
securities companies can gain a competitive advantage.
Chinese security market accumulated abundant historic data for last decade, which contain a great
deal of valuable information. How to make effective analysis on these historic data and obtain useful
information become the priorities of the securities companies. Through data mining—the extraction of
hidden predictive information from large databases—organizations can identify valuable customers,
predict future behaviors, and enable securities companies to make proactive, knowledge-driven
decisions ( Rygielski,2002).
In recent years, data mining applications have been successfully applied in many areas , such as
astronomy ,molecular biology ,medicine , and geology etc( Yang ,2004). Some researchers are trying to
apply this new method to CRM applications. A large number of studies have been carried out data
mining techniques for customer relationship management, focusing more on e-business (Brown
2001),management information systems (Post, et al., 2004);fault diagnosis for large-scale equipments in
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thermal power plant(Yang et al., 2004),etc. Unfortunately, very little attention has been given to
studying the customer relationship management of securities company based on data mining
techniques especially in China. This paper addresses the basics of CRM of securities company in China,
explains the concepts of CRM and data mining in securities company.
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2. Customer Relationship Management (CRM) in Security Trade
CRM stands for Customer Relationship Management. CRM is a comprehensive approach which
provides seamless integration of every area of business that touches the customer – namely marketing,
sales, customer service and field support – through the integration of people, process and technology,
taking advantage of the revolutionary impact of the information technology. CRM creates a mutually
beneficial relationship with your customers. It is fundamentally cross-functional, customer – focused
business strategy (Fulk & Whang, 2000). According to Cash (1999), CRM is in fact about creating value
for customers.
The Internet has enabled a dramatic reduction in the cost of transactions and interactions among
people and businesses, which in return has created a new set of expectations among customers(Norris,
2000). The popularity of internet and e-commerce could bring enterprises huge opportunities and change
the structure of financial circles (Wu,2002). There is a global competition in all of the securities
companies when the major economies enter WTO. In China, CRM become even more important to
securities company in today’s Internet marketplace. CRM of the securities companies, therefore, must be
a well – integrated effort, using the Internet to shorten time and distance to customers, creating new
levels of speed and efficiency in business.
eCRM is customer management for e – business that must confront the complexity of managing
sophisticated customers and business partners in a variety of media including: online and offline media,
personal contact, and more automated and electronic forms of communication (Nykamp, 2001).
Hala(2000) shows that a positive customer experience drives more e – loyalty than traditional
attributes like product selection or price, in traditional channels, you can lose customers if they walk into
a physical location and are disappointed in the store appearance, have an unsatisfactory experience with
a salesperson, or can’t find what they want. ECRM could be viewed as an oxymoron: it implies turning
over to the customer the management, responsibility, data and relationship (Nykamp, 2001) in securities
company. That approach itself is not a CRM strategy, because the contacts with the customer become
electronic and directed by the customer. Therefore, companies should limit the “e” to e – customer care
or e – customer service. It must be greared for customers who prefer to communicate electronically –
and it should be just one component of an entire CRM strategy.
To make the benefit of eCRM a reality, customers must be able to choose the communication channel
that best fits their immediate business needs, their individual working styles, and their current access to
the business (Timmers, 2001). eBusiness systems are integrated throughout the value chain, not just at
the level of the financial transaction. To understand eCRM, companies should understand eBusiness as a
whole (Brown, 2001).
A successful eCRM strategy is designed to engage the customer in a relationship with the company
– beyond a single transaction – so the customer will keep coming back. This requires that the business
engage the entire enterprise – from service and support to sales and marketing to product development –
in servicing the customer at every point of contact. By so doing, companies can engage their customers
in an ongoing knowledge exchange in which the company can learn more about their customers’ needs
and work to develop and deliver the products and services that can exceed their customers’ expectations
on a regular basis.
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Securities industry is of data-intensive. Technology is a critical component of eBusiness (Brown,
2001). It is required for building an infrastructure that can streamline the ordering, inventory, invoicing,
payment, and other systems that add so much complexity to modern businesses. Data mining technology
has helped fuel the growth of CRM strategies. Data capture, storage, and analysis capabilities have
improved dramatically, as have the applications, user interface and data retrieval capabilities for
Customer Service Representatives (CSRs). Essentially, eCRM involves giving customers Web access to
customer, services, and company information, greatly reducing the number calls handled by CSRs.
3. Data Mining in securities company
3.1 Data Mining (DM)
“Data mining” is defined as a sophisticated data search capability that uses statistical algorithms to
discover patterns and correlations in data (Rygielski,2002). Data mining is used to construct six types of
models aimed at solving business problems: classification, regression, time series, clustering, , and
sequence discovery. At present, CRM system is mainly implemented with the techniques of data mining,
such as classification, regression, clustering, summarization and association analysis. The first two,
classification and regression, are used to make predictions, while association and sequence discovery are
used to describe behavior. Clustering can be used for either forecasting or description (Rygielski, 2002).
Using customer service platform, the securities companies collect data, like the customer
requirements and behaviors, to establish customer data warehouse. Then, with the help of database
application programs and tools, such as on-line analytical processing, OLAP, and data mining, the
companies analyze and process these data to gain the relevant information and knowledge of their
customers. And then, taking the background of the listed companies and together with both macro- and
micro-economy data into consideration, the securities companies collect and analyze the correlation
relationship between the customer behavior and market elements, customer behaviors, profit and loss
results, profit distribution, purposely, to extract the crucial information about the customers. With the
valuable information, the securities companies are able to provide well-customized investment advices
to their clients.
Data warehouse and data mining are key tools for the analysis of the customer information. The basic
designation of customer relationship information relies on database tools. It depends on data mining to
summarize, analyze, and make judgments on the information from the existing database, and extract
relevant knowledge about the clients to provide customized services to them and as well as important
evidence for decision-making purposes.
A securities company and its subsidiaries have accumulated a great deal of data for their years’ of
operations, which contribute to the decision-makings on management policies and marketing strategies.
It gives the securities company advantageous positioning in the market to take good use of the year’s of
accumulations. The securities companies of China have always been strict with the accuracy, timeliness
and safety of the data. With competition escalating, securities companies are increasing their
dependency and sensibility on the data more and more. As an important tool of analysis and
decision-making support, data mining has gained attention significant attention from the securities
companies of China.
3.2 The process of Data Mining securities company
Companies in various industries can gain a competitive edge by mining their expanding databases for
valuable, detailed transaction information( Rygielski,2002).Each securities company is interested in
predicting the behavior of its customers through the knowledge gained in data mining.
Data mining is a process of discovering new,meaningful and interesting information directly from
large amounts of data (Han,2001). The process of Data Mining can be divided into three main steps
below:
3.2 1. Pretreatment step
In this step, the purposes of data mining and the scope of knowledge are made out.
To prepare the data for data mining process. First, the database is checked to find out whether the
existing data are enough. And then both inside and outside information related to business are
investigated. Then the useful data are picked out.
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To preprocess the data. The collected data are cleaned and those flawed or with missing entries are
processed for data mining.
3.2.2. Building model step
The selecting of mining techniques, such as classification, clustering and regression lies on the
purposes of data mining defined in the first step. The proper model is selected in accordance with the
attributes of the business to extract valuable knowledge, for instance, the general knowledge about the
common characteristics of all the customers, and the specific knowledge about the features of certain
customer segment.
3.2.3. Processing Step
The mining model processes the knowledge and transfers it to visual format, such as charts, figures,
ect., which are communicated to the decision makers for reference.
To utilize the knowledge. To apply the extracted knowledge on the practice of the business and to test
whether the model is capable of resolving the problems as expected and to what degree can it satisfy the
decision requirements.
4. Data mining techniques for CRM in securities company
The business objectives of securities brokers can be summed as expanding customer bases with
lowest cost, increasing investment amounts and stimulating transactions. To increase the brokerage
amounts and improve market percentage are two of the main targets of marketing and CRM of securities
company in China.
The main tasks of data mining of CRM application system shall settle down the problems below:
To apply the commercial value of data mining on the CRM system:
(1) Customer profitability analysis
After carefully analyzed the relative information, the securities companies categorize their customers
by different profitability classifications with varied preferential policies to pursue maximum profit.
(2) Customer acquisition analysis
Using data mining to subdivide the customers, the securities companies pick up potential customers,
which will enhance the marketing affects and convert the potential customers into new clients. The
customer acquisition capability is the main index of securities industry development.
(3) Customer retention analysis
The cost to acquire new customers is increasing significantly with the heating-up of competition in
the securities industry, which makes it more important to retain the customers than ever before. It
becomes essential to set up a forecast model of customer churn and to analyze the causes why lose the
customers, which provide support for decision-making on customer retention measures.
4. 1. Customer analysis
A data warehouse is built up to store the information of all the customers, specific customer segments
and individual customers and all the transaction data. The minding techniques extracted the valuable
knowledge and analyze the correlation relationship among them. Those of specific subject are picked
out. As core of the mining system, customer analysis provides not only the value of the customers, the
loyalty degree of the customers, and the attributes of the customers, but the relation among different
customer segments.
Through comprehensive analysis on the customer condition, transaction behaviors, natural
characteristics, ect., the system subdivides the customers and then targets the most valuable ones, whom
the company shall improve services to retain.
Through extracting customer information from different aspects, the company can obtain variety of data,
such as contribution margin, loyalty degree, profitability, and position ratio, and information, like
customer complaints and customer churn. Thus, the securities company can get this information and
take actions as early as possible to retain the customers before they leave.
4. 2. Consultation service
The securities company can use the market information and transaction data, together with the
background analysis, to predict the market trends, analyze them and provide customized consultation
services to the clients.
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4. 3. Preventing risk
By analyzing the capital data, the securities company can get the capital information promptly and
therefore control the operation risk.
4. 4. Operating results analysis
Through data mining, the securities company can get information, like the operating results, profit
and loss condition, customer segmentation, ect., combined with market trends, to provide investment
advices for pursuing maximum profit on different markets.
5. Conclusions
In the e-CRM , the customer resource database should be established , the customer demand analysis ,
data mining of customer resource database and characteristic service can be carried out via the network.
In many securities companies in China are regarding customer relationship as a strategic resource and
build up develop solidify customer relationship. The eCRM storm has begun and executives in every
major company will have to formulate and execute CRM strategy rapidly in order to keep up with
competition. The ariticle introduces some basic concepts about CRM and data mining ,and some
benefits brought by data mining in CRM. At the end it points out how to apply data mining applications
in CRM.
Thanks to the efforts of the government and the securities companies, there has been an obvious
improvement on electronic transactions. CRM of securities company in China allows companies to build
one to one relationships with millions of customers. With the progress of information technology, the
management of customer relationships and profiling data has become the new focus of securities
companies in China. This research presents the application of eCRM based on data mining in security
trade for keeping good relationships with customers and promoting core competitiveness. Data mining
process the data and find information efficiently. Data Mining can forecast the trend and behaviors,
thereby nicely support the securities brokers’s decisions, which make CRM system become
administrator’s real decision and analysis tool. Based on data mining methodologies, the information
can be efficiently integrated among related departments so that the higher satisfaction of customers and
achievements of securities companies can be achieved.
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