CRM系統(tǒng)：基于OLAM技術(shù)的分析型CRM設(shè)計(jì)與開(kāi)發(fā)_英文_

Design and development of ana lytica l CRM ba sed on OLAM technology Zu Q iaohong　　L iW enfeng (School of L ogis tics Engineering,W uhan U niversity of Technology,W uhan 430063, China) Abstract: Some key technologies of actualizing customer relationship management (CRM ) systems are researched. The customer analysis m ining p rototype systems on the basis of on2line analyticalm ining (OLAM ) is designed. After transaction analysis, the data warehouse of CRM is constructed. The CLV /CL /CC customer division model based on customer lifetime value, customer loyalty and customer credit is emphatically researched. Three parameters of customer division—customer lifetime value, customer loyalty and customer credit—are calculated by corresponding algorithms, which can realize customer divisions effectively and imp rove the accuracy of distinguishing among customers. The data of p roduct sales are analyzed by the sequence association rules algorithm, the potential rules of the p roducts relevance are discovered, which can p rovide evidence for supporting decisions such as p romotion strategies. The transaction data such as p roduct sales volumes and order lists are analyzed on2line through multi2dimensional and multi2level up2drills, down2drills, and horizontal / longitudinal sections. The customer p roperty factors are analyzed as well. The theory and p ractice of OLAM and its visualization are further exp lored. Key words: on2line analytical m ining (OLAM ); analytical customer relation management (CRM ); data ware2 house Rece ived 2007209210. Foundation item: The National Key Technology R&D Program of China during the 11th Five2Year Plan Period (No. 2006BAH02A06). Biography: Zu Q iaohong (1970—) , female, associate p rofessor, zuqiaohong@whut. edu. cn. O n2line analyticalm ining (OLAM ) , an important kind of know ledge discovery method w ith the on2line analyt2 ical p rocessing (OLA P) fram ew ork, combines w ith the data m ining algorithm. It can obtain useful information and know ledge from massive, incomp lete, noisy, fuzzy and random data, w hich can be app lied to macroscop ic auxiliary decision2making and enterp rise development[1 - 2 ]. D ata m ining usually depends on w ell2organized and w ell2p rep rocessed data sources, and the quality of data sources can directly influence the m ining results. Prep rocessing data sources at the early stages are very important for data m ining. The OLA P tools of a data w arehouse can conduct various kinds of multi2dimensional data analyses and integrate w ith data m ining so as to realize flexible and interactive m ining in a number of abstract layers[3 - 4 ]. The data m ining system combined w ith OLA P tools is so flexible and interactive that it can imp rove quality and the efficiency w hile m ining. It is uniform ly designed in data m ining, data w arehousing and on2line analysis, w hich not only leads to a great developm ent of the data w arehouse system, but also can be w idely used in p ractice. 1　OLAM M echanism The key p roblems of realizing the OLAM m echanism are quick response and effective realization in large data2 bases or w arehouses. A n follow ing factors should be taken into account: 1)Q uick response and high performance m ining G radually refined quality of the OLAM data m ining method: A n interesting model is identified using the fast m ining algorithm in large am ounts of data, and a higher cost but m ore accurate algorithm is analyzed in detail. 2)M ining method based on data cube The m ining method based on the data cube w ith high performance is the core of the OLAM mechanism. Effec2 tive computation of a multi2character data cube as w ell as the non2traditionalmetric and comp licated dimension data cube is important in data m ining[4 ]. 3) Interaction betw een m ultip le functions of data m ining OLA Pps advantages lie not only in the choice of a series of data m ining functions, but also in the interaction betw een data m ining and OLA P[5 ]. 4)V isualization tools In order to effectively disp lay the OLA P results and interactw ith m ining, it is very important to app ly multip le know ledge and data visualization tools, such as charts, curves, decision trees and rules maps, cube view s, etc[6 ]. 5) Expansibility The OLAM system, w ith the user and know ledge visualization packages at the top, comm unicates the data cube at the bottom. The OLAM of the comp licated data including structured data, sem i2structured data and unstructured data is also an important research direction. 2　A na lytical CRM B ased on OLAM Technology In the analytical CRM system, the core technology is on2line analysis and data m ining. Through data analy2 zing, some useful rules can be found in m uch of the data through a variety of m odels and algorithms, w hich can support decision2making. B y clustering and classification technology, custom ers can be grouped. A ssociation analy2 sis theory is mainly for analyzing customer associations, for examp le, w hether a high2value customer base is the high expense rate customer base or not. Pattern recognition technology is used to identify the particular behaviors of customers. Forecasting technology is used to find customer future behavior rules. For instance, valuable customer loyalty degrees can be p redicted by the method of fuzzy neural netw ork. In analytical CRM, the data m ining techniques can be used in the follow ing app lications: 1) Customer value analysis. Through analyzing the customerspcontribution to the enterp rise business, the cus2 tomer value can be calculated, so different services can be p rovided to customers of different groups[7 ]. 2) Customer development analysis. Through analyzing the customersp contribution to the p roduct business, a relatively accurate reference can be p rovided to a target customer base for p roduct marketing. The customers can be grouped into the easily loss of valuable custom er base, valuable and stable customer base, low 2value and unstable customer base, low 2value customer base and stable customer base. A company can adop t different services, market and p rice strategies to stabilize valuable custom ers, convert low 2value customers, and elim inate no value customers[8 - 9 ]. The analytical CRM system structure is designed based on OLAM technology (see Fig. 2); then, system func2 tion modules are designed. 強(qiáng)力推薦： 天柏客戶關(guān)系管理系統(tǒng) 天柏客戶關(guān)系管理系統(tǒng)（CRM）是一款集專業(yè)性、實(shí)用性、易用性為一體的純B/S架構(gòu)的CRM系統(tǒng)，它基于以客戶為中心的協(xié)同管理思想和營(yíng)銷(xiāo)理念，圍繞客戶生命周期的整個(gè)過(guò)程，針對(duì)不同價(jià)值的客戶實(shí)施以客戶滿意為目標(biāo)的營(yíng)銷(xiāo)策略，通過(guò)企業(yè)級(jí)協(xié)同，有效的“發(fā)現(xiàn)、保持和留住客戶”，從而達(dá)到留住客戶、提高銷(xiāo)售，實(shí)現(xiàn)企業(yè)利潤(rùn)最大化的目的。通過(guò)對(duì)客戶進(jìn)行7P的深入分析，即客戶概況分析(Profiling)、客戶忠誠(chéng)度分析(Persistency)、客戶利潤(rùn)分析(Profitability)、客戶性能分析(Performance)、客戶未來(lái)分析(Prospecting)、客戶產(chǎn)品分析(Product)、客戶促銷(xiāo)分析(Promotion)以及改善與管理企業(yè)銷(xiāo)售、營(yíng)銷(xiāo)、客戶服務(wù)和支持等與客戶關(guān)系有關(guān)的業(yè)務(wù)流程并提高各個(gè)環(huán)節(jié)的自動(dòng)化程度，從而幫助企業(yè)達(dá)到縮短銷(xiāo)售周期、降低銷(xiāo)售成本、擴(kuò)大銷(xiāo)售量、增加收入與盈利、搶占更多市場(chǎng)份額、尋求新的市場(chǎng)機(jī)會(huì)和銷(xiāo)售渠道，最終從根本上提升企業(yè)的核心競(jìng)爭(zhēng)力，使得企業(yè)在當(dāng)前激烈的競(jìng)爭(zhēng)環(huán)境中立于不敗之地。 關(guān)鍵詞：CRM,CRM系統(tǒng),CRM軟件,客戶關(guān)系管理,客戶管理軟件,客戶管理系統(tǒng),客戶關(guān)系管理軟件,客戶關(guān)系管理系統(tǒng)