CRM系統：基于OLAM技術的分析型CRM設計與開發_英文_
3 Softw a re Realization Process of the A nalytica l CRM System B ased on DM
The softw are p rocess of the analytical CRM system based on OLAM is as follow s:
Fig. 3 Customer analysis and m ining functions based on
OLAM technology
1) Selection of softw are and hardw are. M ain soft2
w ares:W indow s 2003 Server enterp rise edition (sql, in2
cluding IIS); SQL Server 2005 simp le Chinese, corpora2
tion edition. M ain hardw ares: one server (Rackmount
server) , w hich is suitable for develop ing DB server / data
analysis server in LAN environments and used to build
the data w arehouse and the data m ining m odel; the other
app lication2server (desktop ) is to be a server of the oper2
ational system, w hich is used as a server in develop ing
environments.
2) System architecture. The B /S model is adop ted.
Operational systemps developm ent uses SQL Server 2005
DBMS to realize the control and management of the
source code.
3) D ata p rep rocessing. D ata p rep rocessing is one
important step in the data m ining (know ledge discovery)
p rocess. Especially for the data m ining of noise data, in2
comp lete data, and inconsistent data, it imp roves the
quality of the objects of data m ining and reaches the pur2
pose of imp roving models and know ledge obtained from
data m ining. A fter the logic design of every top ic data
w arehouse, data w arehouses w ill be built in the SQL
Server 2005.
4) The app lication analysis of OLAM technology
and the developm ent of analytical CRM. W hen users in2
put the parameter values of the chosen algorithms, they
can obtain the analytical results of custom er classifications, the calculation of customer loyalty, and the characteris2
tics of customersp hobbies, w hich can p rovide a scientific basis for develop ing marketing strategies. Introducing
fuzzy theory, analytical parameters of customer classifications are researched by com bining qualitative and quantita2
tive methods.
① The significance factors are researched w hich influence customer lifetime value based on allotting expenses.
A ccording to the non2back effect of customer2w allet2shareps changes, the model of a quantitative analysis is p ro2
posed w hich can calculate exactly customer lifetime value w ith a custom er transferring matrix based on theM arkov
chain.
② Parameter system is built to p redict customer loyalty degree. Combining the fuzzy neural netw ork algorithm
and attribute significance theory, customer loyalty degrees can be p redicted by the fuzzy neural netw ork algorithm
w ith initial param eter w eights. The effect is demonstrated better in experiments.
③ Fuzzy comp rehensive evaluation theory is studied to evaluate the credit of individual customers or enterp rise
customers. Tw o kinds of cap ital credit calculation parameter systems are built. A fter every parameter w eight w hich
influences client credit has been decided by means of op tim ization selection, the degree of client credit can be calcu2
lated.
The CLV /CL /CC customer classification m odel is put forw ard. The results of clustering customers by the
scalability K2m eans algorithm is the data p rep rocessing of customer classification by the imp roved B ayesian algo2
rithm, w hich realizes the organic combination betw een clustering and classifying. The customer purchase sequence
model is studied based on association rules. The association of p roducts can be visualized by the dependence rela2
tions netw ork, and the customer sale rules can be screened out, w hich p rovide a decision2m aking basis for bundling
sales, market p rom otions and so on. OLA P technology is used to drill dow n, roll up, and slice the customer and
p roduct information in multi2dimensions, and analyze the potential law and the influencing factors of sales volume,
the am ount of orders, etc.
The graphs of development and imp lementation of the CRM system on2line analysis are show n in Figs. 4 to 6.
4 Conclusion
OLAM technology is one of the most important DBMS technologies in the information industry society. It is
important to app ly OLAM technology to analytical CRM development. B oth algorithm study and analysis of the
p ractice need further research. There are still some concrete p roblems to analyze deep ly.
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