Download Application and Research of Analytical CRM System in Securities

Application and Research of Analytical CRM System in Securities
GUO Jinxu, WANG Xuejin
School of Information Engineering Wuhan University of Technology 430070 Wuhan, P. R. China
，
（
Abstract: This paper introduced the concepts and denotative meanings of analytical CRM Customer
Relationship Management and emphasis discuss how the analytical CRM applied to securities industry
to improve and enhance its management of customer relationship. Then the article introduced the design
and creation of the analytical CRM system in securities industry in detail.
Key words: Securities industry; Analytical CRM; Data mining
）
1
Introduction
Along with the constant development of china’s securities industry, it is more intense between the
securities companies’ competition. The securities companies retain old customers as much as possible to
prevent the loss of customers, to gain new customers and enhance its own market share become the key
to success. Securities companies must change the traditional mode of operation, and actively establish a
customer-centered business model.
We should take the customers as the center to positively develop the customer resources,
strengthens the training of the effective customer and example customer, and through the individual
development to realize the customer’s greatest value, only in this way can it obtain the survival and the
development in the intense competition environment. So the purpose to analyze the Customer
Relationship Management system is to know the client's needs and to create personalized services to
meet different customer needs, which become to the most important things to enhance business
competitiveness.
In the securities industry may use analysis CRM, through the analysis (mining) on a large number
of business data we can fully analysis customer’s contribution to the enterprise core business, defines
valuable customer, witch truly reflects the various customer’s behavior characteristics and attributes. It
provides the basis for the Decision Support System and provides the powerful safeguard for enterprise's
management decision-making and the operation behavior.
2
The characteristic of Analytical CRM
CRM may divide into three kinds from the system constitution or function: Operation CRM (flow
management function), analytical CRM (relational management function), and cooperation CRM
(access function).
Analytical CRM has different from Operation CRM and cooperation CRM, mainly manifested in
the focus of attention, the real-time, and the ability of dealing with the amount of data. Analytical CRM
is a powerful analytical processing functions, may carry on processing to the mass data through this
system.
Analysis CRM first must collect two aspects information: On the one hand it is information that the
enterprise and the customer transact with each other, on the other hand it is the external relevant
information. Fusing these two kinds of important information to compose business data warehouse, it is
the basis for the operation of CRM. The analysis CRM stress in the analysis customer data makes the
enterprise clearly to understand the type of his customer and grasps the accurate demand of different
type of customers, thus excavate customer in its best and give better serves to the customer. The best
situation of building the well customer relation is the CRM system which is covering the entire
enterprise, that is using the various contact means which are provided by the operational CRM, and
using the data that Analytical CRM provides in order to acquaint customer deeply, finally achieve the
goals of making distinctions among different customer. After that the feed backing data will be collected
by the operational CRM, constantly the process circulating repeat, so the customer relation will be
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optimized continuous.
Analysis CRM has acted the role of collate and clear the description to customer behavior
characteristic and attribute characteristic in the entire customer relations management strategy, which
makes the enterprise’s managers and operators of specific things through subdivide, discovery of the
behavior and so on to carry on customer marketing and the service strategy, and makes the enterprise get
the best benefit.
3
System module and function
First, analytical CRM system should establish a data warehouse to store customer information and
transaction data. It can achieve subject-oriented information extraction through mine data and the
relational analysis of these data. The system categorize the patterns of customer’s demand and
profitability to find most valuable and potential clientele, help them to get better allocation of resources,
and give each client's the suitable suggestion. Thus it achieves personalized service for the ticket
business to retain their most valuable customers [1].
This module includes the following five function models: The analysis of customer value, the
analysis of customer’s account, analysis of customer transaction behavioral, analysis of holds warehouse,
the analysis of customer’s loyalty, the customer suit suggestion analysis. Fig.1 is this system function
module
chart.
Analytical CRM system
Customer
value
analysis
Customer
account
analysis
Customer
transaction
behavior
analysis
Customer
credit
analysis
Customer
suit and
suggestion
analysis
Fig.1 The design of system function module
Customer value analysis: It includes analysis of customer property, commission analysis,
organization commission analysis, commission structure analysis, potential customer analysis,
investment repayment analysis and so on. This module analyses the contribution of the customer
combining with the input-output. According to the calculation of the Client Value in Enterprises,
Enterprises can divide customers by the methods of categorization and clustering analysis, in order to
give different services to the different kinds of customers.
Customer account analysis: It includes the analysis of customer fund, the analysis of fund change,
break even analysis, the analysis of fund exceptionally fluctuates, and the analysis of mochikura
exceptionally fluctuates and so on. This module analyzes customer’s account, the fund, and the change
of negotiable securities situation in real-time. It also provides the reasonable suggestion for the different
customer and helps the customer have more profit.
Customer transaction behavior analysis: It includes request deal analysis, business volume analysis,
transaction way analysis, transaction live skip analysis, transaction time analysis and so on. The module
is targeted to the transactions of clients in accordance with customer transactions, the transactions and
active investment habits analysis of different customer groups, according to which, we can know your
customer preferences and get the trend of analysis, which can give our client targeted advisory and
marketing activities.
Customer credit analysis: Including the analysis of the time of open an account, the analysis of
customer sells the hold characteristic, the long-term customer characteristic analysis, the analysis of
trading volume and activity. According to the historical data as well as the change of the habit it can
obtain the customer possible purchase direction and get the customers which have better faith.
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Customer suit and suggestion analysis: The analysis of the count of suit, the customer suit object
analysis, the analysis of the subject of suggestion, the analysis of customer’s reply to the suggestion, the
analysis of sues and drains. This module understands customer opinion and listens to the customer and
solves the problem in time.
4
Design of system
Unlike the common Operation CRM, Analytical CRM is based on the massive amount of customer
data to analyze the data according to the different kinds of subject matters and then find out the valuable
information hidden back of the data, so the data will be changed into knowledge, finally the user can
gain profits by applying knowledge. Basing on the data warehouse, analytical CRM leads into the OLAP
and the data mining then integrate and analyze the data, in order to achieve the purpose of successful
decision-making. Fig.2 is the block graph of this system.
Fig.2 System design block diagram
Fig.3 Multidimensional Data Warehouse Model
4.1
Build of data warehouse
Data warehouse, a subject-oriented, integrated, persistent data set for supporting decision and
analysis application which changes with time, and collects the historical data into the center warehouse
and achieve the transformation between data and information in order to support the structured query,
analysis report and management decision-making process.
Data warehouse is also a database, most of which is based on the design of the relational database.
Other than the bargaining-market database in the securities industry and financial database in the finance
industry, the data warehouse integrates the original data of the bargaining and fund of client. For the
sake of rapid query and statistics, the analysis data warehouse is created with the star type which lack of
connection between the tables. So the table is full of redundancies, large volume and long records.
After the data integration and storage in the data warehouse, the transaction data in the securities
industry can be converted into the analysis data. In the data warehouse, there are a lot of comprehensive
data, semi-comprehensive data, multidimensional views and multidimensional data cubes with the
different hierarchy and dimensionality for the various analysis demands. Fig.3 is the multidimensional
data model in the securities industry.
4.2
OLAP technology
OLAP is a key of decision-making and analysis on data warehouse, which is a series of inter active
data query process, which process requires multiple levels and multi-stage analysis on data to obtain
highly summarized information [2]. The objective of OLAP is to meet the needs of decision support
and special query on many dimensions. The core of the technology is the concept of “dimension”.
Therefore, OLAP also may be called the multi-dimensional data analysis technology. OLAP forms the
decision-making data carries on the synthesis, the statistics, and the analysis to the database and the data
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warehouse, by the specialized report forms which reflect the results of inquiries to the decision-making
process.
The remarkable characteristic of OLAP is can provide the multi-dimensional view. It enables the
user to observer the data on many points of view. Thereby the user can deeply understand the
information in the data. There are many analyzing methods of OLAP including slice, Dice, drill-down,
Roll-up, Rotate. They can satisfy the needs of analyze to meet different angles.
4.3
Data-mining technology
Data-mining or being called knowledge-discovering, is one kind of process that decision-making
support[3]. Data-mining highly automatically analyses the original data of enterprise, makes the
generalization reasoning, excavates out the latent pattern, forecasts the customer's behavior, helps the
decision-maker of enterprise adjust market tactics, detects risk and makes the correct decision.
Data-mining is the key technology in Analytical CRM system. The analytical methods in data-mining
could be divided into four forms as the function: classified analyses, the forecast analyses and clustering
analyses [4].
(1) Classification Analysis
We use the method of Classification Analysis to distribute customers in the Prior definition groups.
We can classify the customers according to sex, age, education level and the fund balance. According to
each classification, system will analyze the investment of different customers to get the information of
different gender, age, education level and the amount of funds of customer's investment ability.
According to the information, securities dealers can make market position and marketing strategy which
can make the securities business more Initiative.
(2) Forecast
To use time series to forecast the stock price . Connecting the way above and important decisions
of Enterprise of different period and macro social economic, we analyze the impact of different
information to the stock price and get the wave risk characteristic of stock. We can find relatively
accurate technical analysis method to forecast stock price trend.
(3) Clustering
Using clustering to analyze customer’s business data, we can get the business instance of every
custom and cluster the customers, and also review the contribution of customer .According to the
characteristic of customer's business behavior; we can know who is most valuable to the company. The
securities companies take care of customers who have big contribution to the company according to
customer's behavior characteristics, and develop the customer to make more contribution to the
company.
5
Conclusion
Analytical CRM being able to help a decision-maker to analyzing customer characteristic in Securities
system, adopt effective tactics improving marketing benefit. Studying the application of analytical CRM
system in securities business is better of discovering the potential consumer, reserving now available
consumer, improving company benefit and so on.
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The Author can be contacted from Email: [email protected]
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