Download Research of E-business Application based on Data Mining

Research of E-business Application based on Data Mining
SHEN Zi-hao, PENG Wei-ping
College of Computer of Henan Polytechnic Univer, Jiaozuo 454003, China
[email protected]
Abstract: Along with the development of electronic business and database technology, data mining as
an important application of e-business systems, will provide a powerful safeguard for the electronic
business company to make business decisions. This paper introduces the method of data mining
technology and mining process, and explores the application of data mining techniques in customer
relationship management and network marketing.
Key words: Data mining E- business Customer relationship management Network marketing
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1 Introduction
With the economic globalization, enterprises in all countries compete increasingly fierce, The rapid
development of Internet brings the world economy into a new phase when the world encomy has never
had a high-speed growth, With the maturity of network technology, traditional business is undergoing
a major transformation into full swing to the e-business, e-business concept has been gradually known
by more and more people.The e-business flood changes the traditional business model rapidly. Online
shopping, B2B, B2C has become the focus of discussion. In the next 20 years, the expansion of
e-business will form a curve of exponential increase. However, it is undeniable that the development of
e-business makes the managers receive massive data information, and it has became a matter of concern
for e-business operators and managers to find out the real valuable information for the guidance of
business decisions.The rapid development of data mining technology, has provided an effective solution
to this issue. This paper has carried on a more comprehensive discussion in view of the data mining
technology in the application of e-business.
2 Data Mining Technology
2.1 Data mining concept
Data Mining is from massive, incomplete, noise and ambiguous, random data of the practical
application, including the implicit in the extraction, people do not know in advance but is potentially
useful and ultimately understandable information and knowledge of the non-trivial process[1] .
Data mining is a use of a variety of analysis tools in mass data to discover that the model and the
data relations' process, uses these models and the relations may carry on the predict, It helps decesion
makers to find a potential association between the data and find the factors which were ignored. It offers
an effective method in the information age where we face the explosion of data and information but lack
the very information .It is a new business information processing technology.The main feature of data
mining is to extract the crucial data for business decisions,by extraction,conversion,analysis and
processing of other models from a large number of commercial database. It is a deep-level data
analytical methods.
2.2 Data mining method
Common methods of data mining include association analysis, sequential pattern analysis, class
analysis, cluster analysis, and so on [1] .They can be applied to customer-centric business decisions and
management of the different aspects and stages.
(1) Associations
Association analysis, namely carries on the data mining using the associations rules. The goal of
association analysis is the excavation hides in the data’s mutual relations, it can discover that such as
“95% percent of customers in a purchase of commodity purchased in the purchase X purchase
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commodity Y” and so on knowledge in the database.
(2) Sequential patterns
Sequential pattern analysis and associations analysis are similar, but the emphasis point lies in the
analytical data the around sequence relations. It can discover that such as “in some period of time, the
customer purchase commodity X, then purchase commodity Y, then purchase commodity Z, namely
sequence X >Y >Z presents the frequency which is high” and so on knowledge in the database.
Sequential pattern analysis description question is: in a given transaction sequence in the database, each
sequence is defers to the trading hours arrangement a group of transaction sets, the excavation sequence
function in this transaction sequence database, returns to a high frequency sequence which in this
database appears.
(3) Classes
With a database and a set of characteristics of different categories, the database records for each
category is given a tag, such a database known as the sample database. Classe analysis through the
analysis of sample data in the database, for each category to make a accurate description or create a
model or excavat classification rules, and then use this classification rules for other records in the
database.
(4) Clusters
What cluster analysis input is a group of unclassified records, and these records should be divided
into several categories that do not know in advance, through analysis of this data records in the database,
according to certain classifying rule, divides the record set reasonably, determines each record in the
category. It uses the classification rules by the clusters analysis tool decision. Using different cluster
method, the same record set may have different results of division.
Data mining can be directly tracking data and assist users to quickly make business decisions, and
users can update data in time to find a better behavior pattern, and apply them to the future
decision-making.
2.3 Data mining process
Data mining is a multi-step process, this process is iterative, many of which require the user to
engage in an interactive process. Figure 1 describes the basic data mining process and the major steps.
Fig.1 General Process of Data Mining
(1) Identify business objects
The clear definition business problem, understand the purpose of data mining is an important step
in data mining. Mining the final structure is unpredictable, but to explore problem should be predictable.
(2) Data Selection
According to the user's mining purposes, searchs all business object of internal and external data
from the data source in the extraction and mining-related data.
(3) Data Preprocessing
To the data selected carries on a processing, mainly includes checking data integrity and
consistency, to the noise data carries on a processing, calculation derived from the loss data,
eliminateing duplicate records, completion of the conversion, and other data types.
(4) Data Transformation
It will be converted into a data analysis model. This analysis model is for the establishment of
mining algorithm. The establishment of a genuine mining algorithm for the analysis of data mining
model is the key to success. Through the projector, or other database operations to reduce the
dimensions of the data, thereby reduce the number of data mining to enhance the efficiency of mining
algorithm.
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(5) Data Mining
To get through the conversion of data mining. In addition to improve the suitable mining
algorithms choosed, all the other work can be completed automatically. The entire excavation process
should be interactive, iterative, that is, the user can control some mining parameters, such as support,
confidence, and granularity, and so on. The data mining algorithm is the core of the data mining system.
(6) Presentation and Assess
Results presentation is referring to the pattern which was obtained by means of data mining ,it is
the model of the user can understand, we can use visualization tools to help users to understand the
result of excavation.
Results assess is referring to the pattern which was obtained by means of data mining ,it is received
by the user and machine mode after the evaluation, and deletes redundant or unrelated pattern. If the
user is not satisfied with mining on the model, you can re-select data mining algorithms to recursive
implementation of the entire mining process, until user is with satisfaction.
(7) Knowledge assimilation
Analysis will be integrated into the organizational structure of the knowledge business information
systems.
3 Data Mining Technology in E-business Application
Change has taken place in the current economic model, from the traditional entity store to
electronic transactions on Internet, also in the relationship of sales and customer. Great mobility of
online customers, they are more concerned about the value of goods, unlike previous attention to brand
and geographical factors. Therefore, enterprises, a major challenge is to understand customer’s needs as
much hobbies, value orientation, in order to ensure the competitiveness in e-business era. Use of data
mining technology, e-business website can be a massive data collection every day for analysis to help
businesses to understand customer behavior and improve the efficiency of websites for assessing the
success of e-business.
3.1 Data Mining Technology in E-business Customer Relations Management Application
In the form of e-business, competition among enterprises becomes extremely fierce because of the
advanced technology brought out by network. But no matter how the form of business develop, in order
to maintain and develope their competitive advantage, enterprises must be as much as possible to
improve customer’s satisfaction, establish the customer's trust, loyalty to the enterprise. Only then they
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can win the customer, and achieve the profit-making .
Under the e-business environment, the enterprise implements the CRM to understand and satisfy
the target customer’s demand and desire well, set up customer-focused, customer-oriented services,
make the enterprise to survive in the fierce market competition and to be in an incincible position.
The customer information is the foundation of CRM, if there is no detailed customer data,also there
is no way to know your customer, then there will be no advantage at all. The main channel of e-business
is network transaction, so every e-mail, the CPC of Web site, each transaction or inquiry on self-service
equipment, are all important potential information resources to enterprises, which can be used to serve
the customer or discover customer. E-business will generate massive data, the greatest resource of
enterprise, that is, customer information, data, knowledge of the existence of electronic form. Well,
regarding these massive data, needs to use a computer analysis through data mining, analyses the
characteristics of customers, and explores business and the corresponding market operator rules, so
continuously improves the economic efficiency of enterprises.
The various stages of the life cycle will use data mining technology in business customer
management. Data mining can help enterprises to determine customers’s characteristics, thus can
provide targeted services. At present, the applications of data mining technologies in CRM lies in the
following four aspects:
1 Definite customer concerns
Use of data mining technology to mining on-line transaction information and the customer
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information, to understand the demands of customers, and to obtain customers’ concerns , thus may
carry on the marketing activity target-oriented.
2 Definite customer presence
How to make customers resident in a longer time in own website, we should know our customer's
browsing behavior, customer’s interest and demand,and dynamic adjust Web pages to meet customer’s
demands. Through data mining the customer's information visited, can know the customers browsing
behavior, thus understand customer’s interest and demands.
3 Customers maintain and customers obtain
The customer maintains is to keep the consumer accessing not only to repeat the same site, but also
repeat purchase of the site's commodity. Maintaining consumer's loyalty and providing the high grade
high-quality services, is the key whether sets up the Internet electronic commerce to obtain successful.
Uses of data mining may module for the customer who shifts for oneself, and recognize the pattern
which led to their shift. Then may use these patterns to discover the similar traitor in the current
customer, so can take the corresponding preventive measure.
The development of enterprises need to obtain the new customer unceasingly. Data mining can
distinguish the latent customer group, and enhance the response rate of market activity. For a new visitor,
through the classification found on the Web, know him and some public description of old customers
that has been classified ,and have the correct classification to him. And then from his classification to
judge whether or not this new visitor is profitable, and decide whether to treat him as a potential
customers, thus may adjust the marketing strategy with a clear goal.
4 Identify key customers
Carries on the analysis with the data mining to customer behavior, obtains the profit ability from
customer. Those customer groups of the highest and most stable spending are identified as "key
customers." In view of the different customer scale, determines the corresponding marketing investment.
The "key customers " need to develop personalized marketing strategy in order to retain these large
customers shopping on the Internet.
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3.2 Data mining in E-business network marketing application
The marketing is the process that enterprise discoveries the demand and meets the demand, under
the conditions of e-business, enterprise's marketing activity is the marketing activity which carries on
through Internet, thus forms the network marketing. The network marketing takes the modern marketing
theory as a foundation, has a shift from taking sells products as the central traditional marketing " 4P "
(Product, Price, Place, Promotion) to taking meets customer need as the center " 4C " (Customer, Cost,
Convenience, Communication).
Network marketing is a new marketing combining information technology and marketing theory [5],
It through collects mass data of consumers and similar enterprises, and so on, and uses of data mining
and other analytical techniques for analysis and processing to these data, obtains the business
decision-making for using in carrieing on the directional marketing to the specific expense community
or the individual, thus brings more profits for the enterprise.
Association analysis uses in understanding the customer’s buying habits and preferences, is helpful
in deciding the market commodity placing and the product bundle sale strategy; Sequence pattern
analysis is with some time discovery product purchase or other behavior pattern forecast that in the
future will purchase the product or the service category probability; Cluster analysis uses subdividing in
the market, traces according to the similarity of its behavior or the characteristic divides the customer
for certain subdivideing the market, adopts the target-oriented marketing strategy; Classes analysis uses
in predictting that which people will have a response to mailling the advertisement and the product
catalog, the promotion methods such as ticket and so on , but may also use in the customer classification
and so on.
Through a large number of customer consumption data for analysis, marketing can be effective in
helping those on the surface through an unrelated large amounts of data and finds the inner association
between data, thereby not only able to make timely response to customer demands, but also can carry on
effective anticipation to it: Through the analysis, Sales can know the consumption and distribution of
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consumer groups, and the distribution of profits brought by customers and the characteristics of market
subdivision, and on this basis to develop effective marketing plans ,brings more profits finally for the
enterprise; And may monitor the change of commodity prices of competitors, and timely response, you
can access the data analysis of market conditions to determine marketing strategies.
The whole network marketing process includes determination network marketing goal, formulation
network marketing plan, one-to-one marketing, overlapping marketing. First through market subdivision,
target market selection and using of data mining technology from the customer data to analyze various
levels of market subdivision, locates for the company in its target markets and provides a reliable basis.
Then, through the collection customer expense data, uses the data mining technology to obtain the
consumer market and the consumer behavior, works out the effective network marketing plans. Through
the support of data mining technology, using classification and clustering technology to a large number
of customers divided into different categories, each category of customers with similar attributes, so that
enterprises can provide to each different type of customer service and realize a one-to-one marketing,
improve customer’s satisfaction. Using of data mining technology in association analysis, can mine
potential demand in other related demands from the customer's existing demand, consumers can be
issued to its previous consumer behavior related to the marketing materials, and realizes the overlapping
marketing.
4 Conclusion
The rapid development of data mining technology has provided an effective information safeguard
for the implementation of e-business. This paper introduces the concept, the method and the
implementation process of data mining technology, in light of the actual situation of e-business, in view
of the data mining technology in e-business website design, the customer relations management and the
application of network marketing has carried on a beneficial study. The data mining technology can
defer to business objectives of enterprise, through carries on an analysis to the massive concrete
business data, reveals the hidden, unknown or the confirmation known regularity, discoverers that
determined effective, innovative and potentially useful information, and further its modeling, made for
the product good marketing and decision-making departments to make important decision to provide the
help. Therefore, using of data mining technology effectively and rationally in e-business activities will
make the e-business more intellectualized and user-friendly, which will certainly become an important
means in the process of e-business.
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--------------------------------About the author:
Zi-hao Shen (1980~), male, born in Henan Nanyang , master, lecturer, presently works in College of Computer of
Henan Polytechnic Univerity,, his research direction is the computer network and the security, the data warehouse
and the data mining.. E-mail [email protected]
Wei-ping Peng (1979-),Male, received his Master of Engeering in Computer Application Technology from HeNan
Polytechnic University. After graduation, he joined the faculty of Computer science and Technology at his
Alma Mater in 2001.He has been engaged in the security of encryption.
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