A Study of Discovering Customer Value for CRM:Integrating Customer Lifetime Value Analysis and Data Mining Techniques

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1 A Sudy of Dcoverng Cuomer Value for CRM:Inegrang Cuomer Lfeme Value Analy and Daa Mnng echnque Ch-Wen Chen Chyan Yang Chun-Sn Ln Deparmen of Managemen Scence Naonal Chao ung Unvery Inue of Informaon Managemen Naonal Chao ung Unvery 00 Unvery Road Hnchu awan 300 ROC Correpondng Auhor: Ch-Wen Chen E-Mal Addre: Abrac Cuomer relaonhp managemen (CRM) ha become an mporan raegy for bunee. Under he curren compeve commercal envronmen he dcoverng manenance and renghenng of cuomer value a key for bunee o earn uanable prof. Pa ude have found ha cuomer lfeme value (CLV) can be ued o calculae each cuomer' conrbuon o he company and daa mnng can be employed a an analycal ool o dcover cuomer' poenal behavoral paern and characerc. hough boh are complemenary rarely are here ude applyng he wo mehod a he ame me. h reearch develop a concepual framework whch combne CLV analy and daa mnng echnque o enhance CRM. Frly we ue CLV analy o calculae cuomer curren value (CCV) and cuomer poenal value (CPV). Ne he cluerng analy ued o group cuomer baed on each cuomer CCV and CPV. Fnally a daa mnng mehod employed o dcover he characerc and he poenal purchang behavoral paern of each group. By eablhng a cuomer value pyramd baed on our fndng and provdng he markeng mplcaon for each group h reearch erved a a reference for manager engagng n CRM raegy. Keyword: Cuomer Relaonhp Managemen Cuomer Poenal Value Cuomer Lfeme ValueDaa Mnng. Inroducon he concep of cuomer relaonhp managemen (CRM) ha pervaded everal ndure n he pa decade. he focu of compane ha hfed from reang cuomer a ju an eny nvolved n he bune proce o reang hem a a crucal componen of her ucce (Jan and Sngh 00) Compane have been nereed n acvely developng relaonhp wh argeed cuomer becaue hey are becomng ncreangly aware of he many poenal benef provded by CRM (Km Suh and Hwang 003). hu CRM ncreangly found a he op of corporae agenda (Guru Ranchhod and Hackney 003). Recenly ncreaed empha for CRM ha been placed on developng a meauremen o underand he value ha cuomer creaed and o gve manager a beer dea of how her CRM polce and program are workng (Wner 00). One of good canddae o develop h meauremen Cuomer Lfeme Value (CLV) analy whch he preen value of all fuure prof generaed from a cuomer (Gupa and Lehmann 003; Haenlen Kaplan and Beeer 007).CLV very ueful ool for meaurng cuomer value becaue a yemac way o underand and evaluae a frm relaonhp wh cuomer (Haenlen e al. 007; Jan and Sngh 00). However CLV analy may no be able o underand and predc cuomer behavoral paern. A a reul he nformaon ha CLV analy provde no good enough o make a correc decon for manager. hu urgenly need a ool o a CLV analy o carry ou he meauremen of CRM. 4

2 Daa mnng anoher good canddae o meaure cuomer value for CRM. I he proce of earchng and analyzng daa n order o fnd mplc bu poenally ueful nformaon. I nvolve elecng eplorng and modelng large amoun of daa o uncover prevouly unknown paern and ulmaely comprehenble nformaon from large daabae (Berry 997; Frawley Paeky-Shapro and Maheu 99). herefore Daa mnng can denfy valuable cuomer predc fuure purchae behavor and enable compane o make proacve knowledge-drven decon (Je Sun 008; Rygelk Wang and Yen 00). hee benef are wha CLV analy need. Neverhele daa mnng could no calculae he preen value of all fuure prof generaed from a cuomer. In order word can no know he ranaconal value ha cuomer have. Gven he above nroducon on CLV and daa mnng we could realze ha CLV and daa mnng are all very ueful ool for meaurng cuomer value and behavoral paern repecvely bu hey all have her own problem. On he one hand CLV can calculae each cuomer poenal value ye could no analyze cuomer behavoral paern. On he oher hand daa mnng can erac cuomer poenal buyng paern bu no capable of underandng cuomer ranaconal value. Obvouly hey eem o be complemenary and need o be worked ogeher bu few ude combne hee wo mehod ogeher for CRM. herefore he major purpoe of our paper o develop a proce whch negrae CLV analy and daa mnng echnque o mprove he meauremen of cuomer value and enhance he effcency and effecvene of CRM.. Leraure Revew.. he Imporance of Meauremen of CRM CRM can be defned a manageral effor o manage bune neracon wh cuomer by combnng bune procee and echnologe ha eek o underand a company' cuomer (Km e al. 003). Gurau and h colleague (003) ndcaed ha he mplemenaon of cuomer-cenrc yem compre a number of eenal age: () collec nformaon abou cuomer; () calculae he CLV; (3) egmen he cuomer n erm of value (profably) and eablh he prory egmen. Hwang and h colleague (004) alo repored ha prece evaluaon of cuomer value and argeed cuomer egmenaon mu be crcal par for he ucce of CRM. Mulhern (999) preened ha cuomer are mporan nangble ae of a frm ha hould be valued and managed. Ryal (00) uggeed ha n order o manage relaonhp a ae compane need o know whch are her mo valuable and whch are her lea valuable relaonhp ae o ha approprae markeng raege can be pu n place. On a ba of leraure wh regard o CRM above we may nfer ha here are ome key ucceful facor for mplemenng CRM ncludng calculang he CLV fndng ou he mo and lea valuable cuomer egmenng he cuomer n erm of value eablhng he prory egmen and denfyng unque characerc of each cuomer wh arge egmen... Cuomer Lfeme Value analy Many CRM reearcher aemp o develop a comprehenve model of cuomer profably nce he queon who and where profable cuomer are alway a very mporan ue n CRM (Hwang Jung and Suh 004). For h reaon CLV become one of cenral dea of CRM. Hwang and h colleague (004) defne CLV a Sum of he revenue ganed from company cuomer over he lfeme of ranacon afer he deducon of he oal co of aracng ellng ervcng cuomer and akng no accoun he me value of money. Pfefer Hakn and Conroy (004) from CRM perpecve aer ha CLV he preen value of he fuure cah flow arbued o he cuomer relaonhp. Smlarly Ryal (00) who condered ndvdual cuomer and fuure poenal cuomer ugge ha CLV he preen 5

3 value of a cuomer fuure purchae. Uually he CLV calculaon baed on he epeced purchae of a ngle cuomer and adjued back o he preen day ung a dcoun rae. he leraure of CLV model have aken mulple drecon. However he bac rucure model of CLV hown a Equaon (Jan and Sngh 00). CLV n R C ( d) 0.5 Where he perod of cah flow from cuomer ranacon; R revenue from he cuomer n perod ; C oal co of generang he revenue R n perod ; n he oal number of perod of projeced lfe of he cuomer under conderaon. h mple CLV model ha been eended o more ophcaed model conderng more crcal facor. For eample Hwang and h colleague (004) who hough ha he eng CLV model dd no conder Cuomer Poenal Value(CPV) develop a novel CLV model whch conan oco-demographc nformaon pa prof conrbuon epeced fuure cah flow (poenal value) and cuomer loyaly. Indeed Bauer Hammerchmd and Braehler (003) ndcae ha many CLV model do no provde markeng-relevan nformaon regardng cuomer pecfc deal uch a epeced cro-ellng or reference. Smlarly Km and Km (999) alo ugge ha mporan o conder cro-ellng and up-ellng a well o calculae cuomer value. Due o h conderaon mporan o ake poenal behavor facor no CLV analy. Schmlen and Peeron (987) propoed a model called he Pareo/NBD model (or SMC model) conderng he dea of fuure acve of each cuomer. Fuure acve mean how many purchae can be epeced from each cuomer durng any fuure me perod of nere. Schmlen and Peeron (994) eended he model o eplcly ncorporae dollar volume of pa purchae and furher conruced he purchae volume model wh Pareo/NBD cuomer ranacon/reenon model. h facor que eenal nce can predc cuomer fuure purchae and could be condered no CLV analy. Indeed Jan and Sngh (00) aer ha h model provde a ophcaed way o ge hee probable of a cuomer beng acve n each me perod. he probable hu can hen be ued o calculae CLV..3. Daa Mnng echnque Shaw and h colleague (00) ndcae ha he knowledge abou cuomer from hee daabae crcal for he markeng funcon bu much of h ueful knowledge hdden and unapped. Snce daabae conan o much daa become almo mpoble o manually analyze hem for valuable decon-makng nformaon (Goebel and Gruenwald 999). Under uch a crcumance daa mnng ool can help uncover he hdden knowledge and underand cuomer beer (Shaw e al. 00; Yen and Lee 006). Gerry and Chye (00) hough ha daa mnng provde he echnology o analyze ma volume of daa and/or deec hdden paern n daa o conver raw daa no valuable nformaon. o dcover hdden characerc we need o know he proce of daa mnng (Hu and Jha 000). Hu and Jha (000) propoe he ep n daa mnng proce: () Eablh mnng goal. () Selec daa. (3)Preproce daa. (4) ranform daa. (5) Combne daa able and projec he daa ono workng pace. (6) Sore daa. (7) Mne daa. (8) Evaluae mnng reul. (9)Perform varou operaon uch a knowledge flerng from he oupu analyzng he uefulne of eraced knowledge and preenng he reul o he uer for feedback Our reearch wll eend h proce o buld dcoverng cuomer value proce whch combne CLV analy and daa mnng echnque. () 6

4 .4. CLV Analy and Daa Mnng echnque for CRM CLV analy and daa mnng echnque have rong relaonhp wh CRM becaue CLV analy are a yemac way o underand and evaluae a frm relaonhp wh cuomer(jan and Sngh 00) and daa mnng echnque can help uncover he hdden knowledge and underand cuomer beer (Shaw e al. 00). Rygelk and h colleague (00) alo ndcae ha daa mnng have made CRM a new area where frm can gan a compeve advanage and denfy valuable cuomer predc fuure behavor and enable frm o make proacve knowledge-drven decon. 3. Concepual Framework and Proce 3. A Concepual Framework of Dcoverng CCV And CPV Baed on leraure revew our reearch negrae CLV analy and daa mnng echnque ogeher. he concepual framework dplayed n Fg. Daa Mnng echnque CLV Analy Dcoverng CLV CPV and Behavoral Paern Enhancng he Effcency of CRM Fg.. A concepual Framework of dcoverng cuomer value We evaluae CLV by conderng wo facor cuomer curren value (CCV) and cuomer poenal value (CPV). SAS Inue Inc. (006) ndcae ha o manage cuomer value mporan o underand boh her curren value and long-erm poenal value. herefore h udy eend h concep and baed on leraure develop a CLV model whch combne boh CCV and CPV o beer underand cuomer value. In he curren value econ Hwang and h colleague (004) develop a novel CLV model whch condered CCV CPV and cuomer loyaly a ame me. Our reearch adop her CCV par. he model hown n equaon.. CLV 0 N ( )( d ) N π () p : ervce perod nde of cuomer N : oal ervce perod of cuomer d : Inere rae : Pa prof conrbuon of cuomer a perod π p ( ) N he um of ( )( ) π p d repreen ne preen value (NPV) of he pa prof conrbuon N where π ( ) he prof conrbuon of cuomer a perod and ( ) d he nere rae facor whch ranform he pa prof no he preen value. In he poenal value econ a predcon for he poenal value obaned by wo elemen epeced cuomer acve probably and epeced purchae volume. Hence we oban he followng equaon o compue he poenal value of cuomer. Poenal Value P rob ( Acve ) E ( Volume ) (3) 7

5 Where Prob (Acve) he probably ha cuomer would be ll acve n he ne ranacon. E (Volume) mean he eceped ha cuomer purchae volume n he ne ranacon. ha he equaon above mean epeced purchae volume from a parcular cuomer who ll have acve for he company n he ne ranacon perod. We replcaed he emaon of he Pareo/NBD model ued by (Schmlen Morron and Colombo 987) o oban he neceary parameer emae for h udy and o calculae Prob (acve) for each cuomer (ee Append ). On he oher hand we ue eend Pareo/NBD model whch developed by (Schmlen and Peeron 994) o predc E(volume). In he daa mnng echnque he daa mnng echnque ued n h udy are aocaon rule OLAP decon ree and cluer analy whch are broadly ued for dcoverng poenal buyng paern confrmng characerc of cuomer and egmenng cuomer. 3. he Proce of Dcoverng Cuomer Value Fg how he proce of dcoverng cuomer value. h proce dfferen from ha of SAS Inue Inc. (006) n ha he whole proce conan more complee and deal nformaon of how o emprcally calculae CPV and how o combne CLV and Daa Mnng echnque. I con of major procee: elecng he raw daa daa preproceng CLV analy dcoverng prelmnary CCV and CPV applyng daa mnng echnque dcoverng CCV and CPV and mplemenaon. Our reearch would follow h proce o emae he emprcal daa. 8

6 Raw Soco-Demographc ranacon daa Selecon of Daa Daa Preproceng Daa Cleanng Daa CLV Analy Cuomer curren Probably acve & Epeced buyng Dcoverng Prelmnary CCV and CPV (Cuomer CCV and CPV Cluerng Soco-Demographc Daa Mnng echnque Aocaon Rule (Cro-Sellng and Decon ree OLAP (Dcoverng characerc) Dcoverng CCV and Dcoverng CCV and CPV and Implemenaon Vual Dplay CRM Sraegy Fg. he Proce of Dcoverng CCV and CPV 3.3 Daa Decrpon Raw daa of h udy wo year ranacon and oco-demographc daa of hypermarke. h daae compoed of appromaely record of ranacon accoun nformaon of 895 cuomer and over 500 em of produc. 3.4 Daa Preproceng here are a number of able n he daabae. Neverhele no all he daa are relaed o he choen purpoe. Afer an nal elecng our reearch conder four able o conue he daa rucure (ar-chema). he ep of daa cleanng o remove he noy erroneou and ncomplee daa. All he record wh mng value are deleed o avod problem n calculaon. Addonally ome cuomer had never bough anyhng before o hee cuomer are no uable for CLV analy hu we would delee. Our reearch ue SQL o deal wh hoe daa. Some daa are neceary o ranformaon no a pecfed forma (defned durng he conrucon of he daabae) n order o mne ak. For eample a new column Epeced_acve_pro (Epeced acve probably) creaed by calculang he Pareo/NBD model. h new arbue uable for furher analyzng CPV. 9

7 4. Emprcal udy Afer daa preproceng n h econ we emae and evaluae he model from he emprcal daa hrough CLV analy and daa mnng echnque. 4. CLV Analy for CCV In repec of CLV analy accordng o Equaon each ndvdual cuomer' prof value n all erm afer beng dcouned added o gve an ndvdual cuomer' curren value. able provde he reul of he op 0 cuomer' dcouned curren value oal number of ranacon of each cuomer and fr-me ranacon. In he able cuomer No.6 ha a hgh oal number of ranacon and early fr-me ranacon me o h CCV hgh (37.8). In conra Cuomer No.' oal number of ranacon low o h CCV low (.43) even hough h ranacon ook place early. 4. CLV Analy for CPV able CCV analy reul Cuomer ID oal number of ranacon Fr-me ranacon CCV reul CPV' calculaon are dvded no wo par cuomer acvy probably (Prob(Acve)) and epeced fuure purchae volume (E(Volume)). h udy calculae he Prob (Acve) baed on he Pareo/NBD model developed by Schmlen Morron and Colomobo (987). Afer obaned he neceary parameer emae for h udy we found ha parameer < ; herefore all parameer are pu no Cae A (ee Append ). Durng he calculaon proce each cuomer' lae ranacon me and oal number of ranacon mu be found ou and he Gau hypergeomerc funcon calculaed. he reul led n able (only op 0 cuomer). able how ha he cloer he lae ranacon me (he bgger he ) he bgger he Prob (Acve). For nance Cuomer No.' lae ranacon me o h Prob (Acve) a low a 0.0. On he oher hand Cuomer No.4' lae purchae me 9 o h Prob (Acve) a hgh a

8 able Prob(Acve) analy reul Cuomer ID lae ranacon me () F(a b ;c ;z ()) F(a b ;c ;z ()) Prob(Acve) A o epeced fuure purchae volume reference gven o he calculaon mehod propoed by Schmlen and Peeron (994). Fr n he enre ranacon daabae he oal average ale and varance are calculaed 6.54 and.99 repecvely. Ne each cuomer' average ale varance and relably coeffcen are calculaed. Bede becaue he varance for cuomer who ranaced only once zero all cuomer' varance mu be calculaed for he mean o erve a an nde for he varance. he reul a hown n able 3 (only op 0 cuomer). he reul fnd ha he maller he varance he more able he purchae volume epeced for he ne perod. For nance Cuomer No.5' average ale 5.74 varance.466 o h E (Volume) By conra Cuomer No.3' average ale 9.4 varance 5.7 o h E (Volume) able 3 E(volume) analy reul Cuomer Average Varance Relably ID ale coeffcen E(volume) *

9 Afer Prob (Acve) and E (Volume) are obaned CPV can hen be calculaed baed on Equaon.3. he reul hown n able 4 (only op 0 cuomer) below. I found ha Cuomer No.3' probably of re-ranacon Prob (Acve) a well a epeced fuure purchae volume E (Volume) are bg o h CPV he bgge (8.75). On he oher hand Cuomer No.' probably of re-ranacon Prob(Acve) a well a epeced fuure purchae volume E (Volume) are mall o h CPV he malle (0.078). able 4 CPV analy reul Cuomer ID Prob(Acve) E(Volume) CPV Cluerng Cuomer for Smlar CCV and CPV In he ne ep ung he K-mean mehod baed on he CCV and CPV reul were appled for cuomer groupng. h mu pecfy he number of cluer k n advance. Afer k eed found ha he number of cluer k 3 ha he be cluerng effec. he reul provded n able 5 below. Wh regard o CCV he cluer cener pon of cluer 3 he hghe wh a oal number of cuomer 303; he econd-hghe cluer cener pon cluer wh a oal number of cuomer 340. Cluer 3 ha he hghe number of cuomer oalng 665 ye cluer cener pon he lowe. Wh regard o CPV cluer 3' cluer cener pon he hghe wh 375 cuomer; ne cluer wh 06 cuomer n all. he lowe cluer cener pon cluer wh 3059 cuomer.

10 4.4 Cuomer Value Mar and 0:80 Rule Wh he horzonal a a CPV and he vercal a a CCV h udy aemp o conruc a cuomer value mar and agn name and number o each cluer a hown n Fg 3 below. In he fgure cluer 9 ha he hghe CCV and CPV o h group of cuomer named golden cuomer. Cluer 7 ha a hgh CCV bu a low CPV o h group of cuomer mu be wn back. hough cluer 3 ha a low CCV CPV hgh. For h reaon ha a cuomer group worhy of beng developed. Cluer ha a low CCV and a low CPV a well o a nomadc cuomer group. Cuomer Curren Value Hgh- CCV Mddle- CCV Low- CCV 7.cuomer needng o wn back 4.Low-CPV Mddle-CCV.Nomadc cuomer 8. Mddle-CPV Hgh-CCV 5. Mddle-CPV Mddle-CCV. Mddle-CPV Low CCV 9.Golden Cuomer 6.Hgh CPV Mddle- CCV 3.Worhy of beng developed cuomer Low-CPV Mddle-CPV Hgh-CPV Cuomer Poenal Value Fg 3. Cuomer Value Mar o underand he profably and cuomer number of each cluer cro-analy wa carred ou and he reul hown n able 6. he golden cuomer group accoun for a mere 3.% of he oal number of cuomer bu prof make up nearly 40%. he nomadc cuomer group accoun for 3.% of he oal cuomer number wherea prof merely make up 5.4%. Overall he oal number of low-ccv cuomer group (group 3) accoun for 80% bu prof only make up 6.86%. By conra he oal number of mddle-ccv and hgh-ccv cuomer group accoun for abou 0% bu prof reache up o 83%.h endency correpond o he 0/80 rule.e. cuomer wh prof among he op 0% have conrbuon o he enerpre a hgh a 80%; by conra cuomer wh prof among he boom 80% have a mere 0% conrbuon o he enerpre. In addon o fndng he 0/80 rule phenomenon however h udy alo venure hrough he fndng of poenal value deep no he mporan raegc gnfcance behnd he 0/80 rule namely par of he cuomer amd he op 0% belong o fuure cuomer (cluer 7 and 4) wh low poenal and mu be gven aenon and oued back. In he cuomer group wh prof amd he boom 80% ome (cluer 3) may have grea fuure purchae poenal and are herefore worhy of beng developed. Such a concep dfferen from he pa hnkng n whch mporance mply placed on cuomer wh prof amd he op 0% whle neglecng cuomer wh prof amd he boom 80%. 3

11 4.5 OLAP Aocaon Rule and Decon ree wh CPV he am of h reearch o dcover CCV and CPV n oberved daabae o ha could beer underand dfferen value abou dfferen cuomer and develop new raege o provde beer ervce. In he prevou econ we ued CLV analy o calculae cuomer value no cluer. In h econ he OLAP aocaon rule and decon ree are ued o creae cuomer profle n golden cuomer (we eleced golden cuomer o analyze becaue mo mporan valuable and meanng).he purpoe of hoe mehod are o dcover characerc of cuomer poenal buyng paern cro-ellng opporune buyng preference and o generae rule for predcng who are poenal cuomer OLAP wh Golden Cuomer In repec of he feaure of he demographc ac over he golden cuomer hrough he daa ored n he demographc daabae feaure uch a INCOME_BY_YEARLY OAL_CHILDREN GENDER AGE MARIAL_SAUS OCCUPAION and EDUCAION_DEGREE are choen for muldmenonal analy (OLAP) along wh he golden cuomer. A number of he golden cuomer' arbue are obaned a follow: beween age and female are more han male bu age beween male are more han female; cuomer wh yearly ncome a have he mo conrbuon and he percenage of marred and ha of ngle are almo he ame. Mo cuomer have a educaonal degree n enor hgh chool whle cuomer wh a graduae degree are fewe. he cuomer group wh four chldren ha a hgher value followed by hoe wh 3 chldren. Cuomer whou chldren have he lowe conrbuon followed by hoe wh one chld. In erm of occupaon mo cuomer are profeonal. 4

12 4.5. Aocaon Rule wh Golden Cuomer hough he golden cuomer have purchae poenal he preference feaure a well a he paern of her purchae poenal reman o be known. A a reul of h aocaon rule were ued o eek he aocaon beween purchae n order o underand he pobly of cro-ellng. he oal number of golden cuomer' ranacon Aumng uppor.5% confdence 80% he reul hown n able 7 below. A oal of record of daa are obaned for purchae poenal paern. here are aocaon rule reachng 00% confdence n able7. Produc caegore of aocaon rule No. are dary produc and bread mplyng he pobly ha cuomer may ue bread wh dary produc for he meal. In addon produc caegore of aocaon rule No. 3 and 4 are beverage and prepared food ndcang he poenal for combnng beverage and prepared food for ellng n he ore. Produc caegore of aocaon rule of No.5 are prepared food and frozen food. Fnally produc caegory of rule No.6 he ame one beverage. able7 he reul of aocaon rule for golden cuomer Suppor.58% Head Imple Body Confdence. Pro. [08] AND Pro. [695] > Pro. [8] AND Pro. [958] 00%. Pro. [454] AND Pro. [5] > Pro. [] AND Pro. [436] 00% 3. Pro. [33] AND Pro. [77] > Pro. [558] AND Pro. [648] 00% 4. Pro. [08] AND Pro. [45] > Pro. [454] AND Pro. [67] 00% 5. Pro. [45] AND Pro. [454] > Pro. [08] AND Pro. [67] 00% 6. Pro. [53] AND Pro. [89] > Pro. [96] AND Pro. [504] 00% 7. Pro. [454] AND Pro. [] > Pro. [5] AND Pro. [436] 80% 8. Pro. [33] AND Pro. [558] > Pro. [77] AND Pro. [648] 80% 9. Pro. [03] AND Pro. [5] > Pro. [59] AND Pro. [746] 80% 0. Pro. [53] AND Pro. [96] > Pro. [89] AND Pro. [504] 80%. Pro. [9] AND Pro. [84] > Pro. [58] AND Pro. [6] 80% 5

13 Decon ree wh Golden Cuomer h udy adop a decon ree o predc golden cuomer rule whch mgh be of ome help o an underandng of new cuomer. able 8 l rule wh a hgher pobly of producng golden cuomer by elecng 85% or above rule ored ou by percenage n decendng order. he reul ha produced 9 decon rule. able 8 he reul of analy of Decon ree for Golden Cuomer Rule: IF( ) HEN Golden Cuomer Age and Annual Income $ $90000 and Educaon College and Occupaon Managemen Age and Gender F and Annual Income $ $50000 and Educaon Hgh School Degree and Occupaon Managemen Age and Gender M and Annual Income $ $90000 and Educaon Hgh School and Occupaon Managemen Age and Gender F and Annual Income $ $50000 and Educaon Graduae Degree and Occupaon Managemen Age and Gender F and Annual Income $ $0000 and Educaon Hgh School Degree and Occupaon no Managemen Age and Gender F and Annual Income $50000 and Educaon Hgh School Degree and Occupaon Profeonal Age and Gender F and Annual Income $ $0000 and Educaon Graduae Degree Age and Gender F and Annual Income $ $90000 and Educaon Paral Hgh School and Occupaon no Managemen Age and Gender F and Annual Income $ $50000 and Educaon Bachelor Degree and Occupaon Profeonal Probably 93.8% 90.48% 89.93% 89.9% 88.06% 87.3% 86.89% 85.93% 85.0% 5. Concluon Knowledge of he CCV and CPV of ndvdual cuomer can a organzaon o egmen marke o pecfy markeng m elemen and hen furher o allocae markeng reource n a way ha reurn hgh level of prof. Parcularly n oday' commercally compeve envronmen bunee are ncreangly capable of orng a large amoun of daa n her daabae. Such knowledge hereby become more and more mporan. herefore how o oban knowledge of cuomer' CCV and CPV become a very eenal ue. h udy propoe he concep of combnng CLV analy and daa mnng echnque. Whle SAS Inue Inc. have propoe a mlar concep h udy furher analyze ranacon daa and oco-demographc daa and emprcally fnd ou nne cuomer egmen baed on CCV and CPV and her purchae paern a well a characerc by ung Daa Mnng echnque. he 0/80 rule behnd nne cuomer egmen alo obaned. he enre operang procedure mgh be helpful for enhancng CRM. Afer analy h udy aemp o conruc a cuomer value mar denfe he arbue of he 0/80 rule and furher underand he mplcaon of he 0/80 rule. ha among he cuomer group 6

14 (cluer ) wh prof n he op 0% 3.% of he cuomer belong o he hgh-cpv and hgh-ccv "golden cuomer group". h ype of cuomer ha he greae conrbuon o he company. However ome mporan ue hould be concerned by manager wh hee cluer. Whle he value provded by hee group he hghe cluer 4 and 7 do no have grea poenal and loyaly n he fuure. herefore manager hould have o pay aenon on hee wo cluer by underandng her purchang endency and fnd a way a well a ue dfferen markeng raege o wn hey back. Such a concep very crucal for CRM n ha f he ame markeng raegy employed he cuomer group of he op 0% wll no be managed effecvely. Underandng wha he mo mporan and whch cuomer mu be won back would benef he deploymen of markeng reource. he pa CRM concep alo noed ha he conrbuon of cuomer of he la 80% no hgh and no worh for manager o pend me and effor on hem. hu percenage of markeng effor hall no need o nve oo much on hem. h udy however fnd ha cuomer of he boom 80% are no necearly unworhy of beng developed. Inead here a group o whch aenon mu be pad. I he "worhy-of-beng-developed" group. Whle currenly h group of cuomer doe no conrbue very much would have a grea poenal and pobly would make a grea conrbuon o he company n he fuure f manager ue he rgh raegy for hem. Hence worhwhle o place markeng reource o h ype of cuomer. In he end h udy am a he golden cuomer o fnd ou her behavoral paern by ung daa mnng echnque ncludng aocaon rule OLAP a well a decon ree. 5.. Manageral Implcaon On a ba of he fndng of he 9 cluer n our udy we buld a pyramd-ype cuomer value rucure a hown n Fg 4. Color change from lgh o dark boom-up whch repreen level of conrbuon of eng value. he cluer a he boom are group of cuomer wh low curren value. Whle here are many cuomer n h group hey do no conrbue o he company' prof very much (makng up abou 0% of he oal prof). Hence her color lgher. hey are followed by he econd layer n erm of level of conrbuon of eng value and he op layer ha he hghe level of conrbuon of eng value. he mddle a (nde he pyramd wh an up arrow) called he CRM core a and he cuomer group n conac wh he a of he company' operaon he hgher he more mporance. hee cuomer all have a poenal and are worhy of beng much cared for. he farher away from he a (oward he lef and he rgh) he le mporance. Cuomer group lyng n he a area are a follow: "golden cuomer" "hgh-cpv and low-ccv" "mddle-cpv and hgh-ccv" "cuomer needng o wn back" and worhy-of beng-developed. For hee cluer a daa mnng model need o be ued o furher dcu her poenal quale. In parcular for he golden cuomer group a ound markeng oluon and verale ervce are needed o be provded wh reenon of cuomer relaon a he goal. In repec of he "cuomer needng o wn back" group a deeper nervew needed and every effor hould be made o fnd ou he poble caue of he group' leavng make mprovemen and wn hem back. A o he "worhy-of-beng-developed" group care neceary and poble conrbuon n he fuure canno be negleced. A markeng oluon hould be creaed for. A for he "hgh-cpv and low-ccv" and he "mddle-cpv and hgh-ccv" group apar from reanng relaon conderaon need o be gven a o how o enhance he level of relaon. Cuomer lyng away from he a are: "Low-CPV and Mddle-CCV" "nomadc cuomer" "mddle-cpv and mddle-ccv" and "mddle-cpv and low-ccv". Invemen hould be avoded a much a poble n hee cuomer group. Bede he "mddle-cpv and mddle-ccv" group a well a "low-cpv and Mddle-CCV" whoe relaon need o be reaned nvemen of reource mu be reduced n he oher group n order o avod wang. Epecally for he nomadc cluer only came o purchae 7

15 once or wce and fuure poenal que low. herefore fuure nvemen hould be avoded beng launched no h group. On he whole he company hould renghen monorng and creaon over cuomer value and enhance managemen of poenal and loyaly o ncreae he effec of CRM. 5.. Lmaon and Suggeon for Fuure Reearch In h paper we propoe a concepual framework and he proce of negrang CLV analy and daa mnng echnque. Currenly few CRM yem have conduced cluerng baed on he cuomer prof. hu Fuure ude may buld a e of CRM yem baed on our model. Addonally becaue h udy lay more re on he buldng of a CCV and CPV procedure no on comparon wh dfferen mehodologe. Whle we employ he common model o calculae CCV and CPV ll ha ome oher mehodology whch could be ued o analyze cuomer' value. Hence poble ha here anoher beer mehod whch could be ued o fnd ou more accurae nformaon of cuomer poenal buyng paern. hu fuure ude can conder dfferen mehodologe o compare wh our udy and n urn fnd ou he mo uable mehodology for enhancng he effecvene of CRM. 8

16 9 Append he Probably ha Gven Cuomer Sll Acve Cae: >. he probably ha a cuomer ll acve gven an oberved hory of X purchae n me (0) nce ral wh he mo recen purchae a me P[Acve γ>x] () ( ) ( ) ( ) ; ; ; ; z c b F a z c b F a r γ (A) Where ( ) y y z c b a γ γ ; ; ; Cae:< P[Acve γ<x] () ( ) ( ) ( ) ; ; ; ; z c b F a z c b F a r r γ (A) where ( ) y y z c b a γ γ γ ; ; ;.. Cae3: [ ] r X Acve P γ γ (A3) In (A) and (A) F(a b; c; z) he Gau hypergeomerc funcon Reference. Bauer H. H. Hammerchmd M. and Brahler M. (003) "he cuomer lfeme value concep and conrbuon o corporae" Yearbook of Markeng and Conumer Reearch Berry M.J.A. & Lnoff G. (997) Daa mnng echnque for markeng ale and cuomer uppor: Wley New York. 3. Frawley Wllam J. Gregory Paeky-Shapro and Chropher J. Maheu (99) "Knowledge dcovery n daabae: an overvew" AI Magazne 3 (3) Gerry Chan Kn Leong and Koh Han Chye (00) " Daa mnng and cuomer relaonhp markeng n he bankng ndury" Sngapore Managemen Revew 4 () Goebel Mchael and Le Gruenwald (999) "A urvey of daa mnng and knowledge dcovery ofware ool" ACM SIGKDD Eploraon -0.

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