Analysis of marketing data to extract key factors of telecom churn management

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1 fca Joual of usess Maagemet Vol. 5(0) pp eptembe 011 valable ole at DOI: /JM IN cademc Jouals Full Legth Reseach Pape alyss of maketg data to extact key factos of telecom chu maagemet Hao-E Chueh Depatmet of omato Maagemet Yuape Uvesty No. 306 Yuape teet Hsechu Tawa R. O. C. E-mal: Tel: /89. Fax: ccepted 16 May 011 Fo telecom dustes facg custome ad maket chages due to apd developmets ea satuato ad tese competto avodg chu s ctcal. Most chu maagemet studes attempt to locate a lst of potetally lost customes fom exstg custome data fo use subsequet custome eteto actvtes. Howeve the effectveess of a telecom compay s chu maagemet s detemed by whethe t deceases the chu ate ad successfully etas lost customes. To help telecom dustes acheve effectve chu maagemet ths study utlzes fuzzy coelato aalyss to extact the key factos of telecom chu maagemet pocesses. Usg a dataset of a telecom compay Tawa a data mg-based chu maagemet model was costucted pevous wok. The key factos detfed by the data mg-based chu maagemet model ae cofmed by fuzzy coelato aalyss. Key wods: Telecom dusty chu maagemet fuzzy coelato aalyss data mg custome eteto stategy. INTRODUCTION Whe a dusty s custome maket tastos fom apd developmet to ea satuato ad tese competto that dusty faces sevee chu poblems. Examples of ths sceao clude telecom dustes teet sevces dustes bakg-elated dustes ad eve the cultual ad educato dustes (h et al. 006; uckx et al. 005; Keaveey 1995; Km et al. 004; Km et al. 004). Numeous etepses have eteed the telecom dusty to acque a poto of the emegg telecom maket eve sce the govemet fully lbealzed the moble phoe dusty. Itally thee was a massve demad fo emegg telecom sevces because moble phoe sevce uses wee ucommo. s a esult the moble telecom dusty developed apdly. Howeve sce the demad fo moble telecom sevces has tastoed fom apd gowth to ea satuato ecet yeas lage telecom compaes have become tesely compettve. Compoudg ths stuato s the Tawa govemet s toducto of potable moble umbes allowg uses to swtch moble phoe caes wthout chagg the moble phoe umbes. Uses ca easly swtch the moble phoe sevce caes to maxmze the beefts meag telecom compaes could face seous chu poblems (h et al. 006; Km et al. 004; Km et al. 004; Matteso 001). Nowadays moble telecom sevces have become a dspesable commucato chael. ce eveyoe eques moble telecom sevces customes lost by compay wll evtably become customes of compay o compayc. Pevous studes dcate that to ga a ew custome a compay must sped fve to te tmes the amout eeded to eta a custome. Theefoe etag log-tem loyal customes s moe poftable tha gag ew shot-tem customes. To mata maket shae ad poftablty telecom compaes have used vaous appoaches o maagemet mechasms to eta customes ad pevet seous chu poblems. Matag exstg customes locatg potetally lost customes advace ad effectvely mplemetg custome eteto stateges ae all seous coces faced by telecom compaes (Matteso 001). commo chu maagemet pocess volves costuctg a chu pedcto model usg past chu data ad detemg key factos affectg chu. Ths chu model s the used to locate a lst of potetally lost customes fom exstg custome data to pefom eteto actvtes (eso et al. 000; Chu et al. 007;

2 Chueh 843 Hug et al. 007; Lavee et al. 004; Moze et al. 000; Nga et al. 009). The success of a compay s chu maagemet s detemed by whethe t effectvely deceases chu ate ad ot by whethe t ca locate a lst of potetally lost customes. The ctcal elemet of chu maagemet s successful eteto of potetally lost customes. Howeve most telecom compaes udeestmate the mpotace of effectve custome eteto stateges. Cosequetly ths study utlzes fuzzy coelato aalyss (Chag et al. 1999; Chag et al. 000) to aalyze the esults of maketg actvtes to extact the key factos of telecom chu maagemet. Ths pape s ogazed as follows: ecto pesets seveal studes o telecom chu maagemet. ecto 3 toduces the aalyss method fuzzy coelato aalyss (Chag et al. 1999; Chag et al. 000) to extact the key factos of telecom chu maagemet. ecto 4 pesets the expemetal esults whle ecto 5 povdes the coclusos. TELECOM CHURN MNGEMENT Whe telecom compaes face chu poblems they mplemet feasble chu maagemet pocedues ad stegthe exstg custome elatoshp maagemet. Pevous studes utlzed vaous data mg techologes to assst telecom compaes esolvg chu poblems (Coussemet et al. 008; Hug et al. 006; Km et al. 004; Km et al. 004; Moze et al. 000; Tsa et al. 010; Tsa et al. 009; We et al. 00; Xa et al. 008). Data mg efes to usg automatc o semautomatc methods to extact latet ukow meagful ad useful fomato o models fom lage datasets (ey et al. 004; Duham 003; Fayyad et al. 1996; Ha et al. 001; Katadzc 003; Ta et al. 006). Futhe dscusso descbes the ma techques volved ths pocess. Classfcato aalyss classfes a ew example wthout a clea categoy to a pedetemed categoy. Ths techque s boadly appled to esolve poblems dffeet felds fo example usg documet ttles ad cotet fo classfcato ad usg vaous medcal test esults to deteme whethe tumos ae malgat o beg. Commo classfcato aalyss methods clude decso tees eaest eghbo classfcato ayesa classfcato eual etwoks ad suppot vecto maches. Pedcto aalyss utlzes pevously o cuetly ecoded fomato o codtos to deteme possble futue esults. Most methods applcable to classfcato aalyss ca also be appled to pedctve models. The dffeece betwee these appoaches s that classfcato aalyss s used to aalyze cuet codtos whle pedcto aalyss s used to pedct futue codtos. Clusteg aalyss poduces hghly smla cluste combatos by aalyzg the smlates betwee umeous examples o ecods. The pmay pupose of clusteg degees of smlaty wth examples o ecods wth othe clustes. Moe dscmatoy cluste sets ae poduced whe thee s hgh smlaty wth clustes ad hgh vaato betwee clustes. ssocato ule aalyss locates the ules of specfc assocatos fom data. The most popula assocato ule s depedecy elatoshps tem sets of databases. ssocato ules establshed usg smlaty elatoshps ca exhbt sets of tems that appea smultaeously databases. equetal patte aalyss s a techque smla to assocato ule aalyss; howeve t dffes by emphaszg depedece tem sets ad copoatg tempoal sequeces. The pmay pupose of sequetal patte aalyss s locatg the tempoal sequece elatoshps of tem sets that commoly appea a database wth a specfc tme teval. Data mg s a tegated techque that volves aalyss flteg extacto ad statstcal aalyss fo lage amouts of data; t has umeous applcatos addessg busess poblems (u et al. 003; eso et al. 000; Chu et al. 007; Coussemet et al. 008; Lavee et al. 004; Lejeue 001; Luo et al. 007; Nga et al. 009). We et al. (00) costucted a chu pedcto model that detfes chues fom subscbe cotactual fomato ad call patte chages fom call detals. Ths study poposes a mult-classfe class-combe appoach to addess the challege of a hghly skewed class dstbuto betwee chues ad o-chues. The poposed appoach acheves satsfactoy o easoable pedcto wth the oe-moth teval betwee model costucto ad chu pedcto. Hug et al. (006) poposed the use of custome demogaphy data bll paymet fomato call detal ecods custome cae/sevce status ad sevce chage logs to detfy potetally lost customes usg data mg techques. Ths study compaes vaous data mg techques capable of assgg a chu scoe to each moble subscbe. Results dcate that both the decso tee ad eual etwok methods ca delve accuate chu pedcto models.xa et al. (008) poposed a VM-based custome chu pedcto model to mpove pedcto abltes by applyg stuctual sk mmzato. Ths study compaes the poposed method wth atfcal eual etwok decso tee logstc egesso ad ave ayesa classfe methods demostatg that the poposed method outpefoms othe methods egadg accuacy ate ht ate coveg ate ad lft coeffcet. Coussemet et al. (008) establshed a decso suppot system fo telecom chu pedcto. Ths study evaluates the beefts of addg the voce of the customes though call cete e-mals (textual fomato) to a covetoal chu pedcto system usg oly tadtoal maketg fomato. These esults show a sgfcat cease pedctve

3 844 f. J. us. Maage. pefomace by addg ustuctued textual fomato to a covetoal chu pedcto system. Fom a maageal pespectve ths fomato ca eable decsomakes to detfy customes most poe to swtch. Tsa et al. (009) adopted hybd eual etwok based models to maage telecom chu poblems. Ths study costucts two hybd models by combg two dffeet eual etwok techques fo chu pedcto. The fst model s based o back popagato atfcal eual etwoks (NNs) ad self-ogazg maps (OMs). The secod model s the fst model combed wth NN othe wods NN + NN. Expemetal esults show that the two hybd models outpefom the sgle eual etwok basele model egadg pedcto accuacy. The NN + NN hybd model pefoms bette tha the NN + OM hybd model sgfcatly. The pupose of the studes evewed s costuctg a effectve chu pedctve model to deteme a lst of potetally lost customes povdg telecom compaes wth a efeece fo mplemetg custome eteto actvtes. Howeve fo telecom customes locatg potetally lost customes does ot equate to etag customes ad effectvely educg chu ates. Theefoe ths study emphaszes famg a effectve custome eteto stategy to help telecom compaes effectvely educe chu ates. Ths study utlzes fuzzy coelato aalyss (Chag et al. 1999; Chag et al. 000) to aalyze the esposes of dffeet custome goups to eteto actvtes to detfy the key factos of telecom chu maagemet ad deteme a effectve custome eteto stategy. Ths appoach povdes telecom compaes wth a efeece to deteme optmal eteto tmg ad eteto method combatos fo dffeet custome goups. MTERIL ND METHOD Fuzzy coelato aalyss Ths study emphaszes extactg the key factos of telecom chu maagemet eablg telecom compaes to deteme the most appopate eteto tmg ad eteto method combatos fo dffeet custome goups. Theefoe a fuzzy coelato aalyss (Chag et al. 1999; Chag et al. 000) s used. Reseaches fequetly use coelato aalyses to deteme the elatoshps betwee the attbutes of databases (Dowdy et al. 1983). Covetoal statstcs have compehesvely dscussed the vaous coelato aalyses defed o oday csp sets. Howeve attbutes ecoded databases may be fuzzy (Zadeh 1965) but mpotat ad ae watg to be exploed. Theefoe methods to vestgate these fuzzy attbutes ae equed. Ths secto toduces fuzzy smple coelato aalyss (Chag et al. 1999) o Zadeh s fuzzy sets (Zadeh 1965). The smple coelato coeffcet betwee two fuzzy sets s efeed to as the fuzzy smple coelato coeffcet (Chag et al. 1999). uppose thee ae two fuzzy sets F whee F s a fuzzy space the fuzzy set ad ae defed o a csp uvesal set X wth membeshp fuctos ad. The the fuzzy sets ad ca be expessed as: = ( x ) x X ) (1) = ( x ) x X ) () : X [ 0 1 ] : X [ 0 1 ] whee ad. ( x x x X ssumg thee s a adom sample 1 L ) aloe wth a sequece of paed data (( x ( x1 ) ( x1 )) ( x ( x) ( x)) L ( x ( x ( x ))) 1 whch coespod to the gades of the membeshp fuctos of fuzzy sets ad defed o X. The the smple coelato coeffcet betwee the fuzzy sets ad al. 1983): = 1 s as follows (Chag et al. 1999; Dowdy et ( ( x ) )( ( x ) ) /( 1) ( x ) = 1 ( x ) = 1 = = 1 1 = = 1 ( ( x ) ) 1 ( ( x ) ) whee ad deote the aveage membeshp gades of fuzzy sets ad ove the adom sample ad ae the sample stadad devatos of fuzzy sets ad espectvely. eveal mpotat popetes of the fuzzy smple coelato (8) (9) (4) (5) (6) (7) (3) )

4 Chueh 845 coeffcet ca be obtaed ad stated as follows: elated. elated. >0 s close to 1 the the fuzzy sets ad ae hghly s close to 0 the the fuzzy sets ad ae baely the the fuzzy sets ad ae postvely elated. < 0 the the fuzzy sets ad ae egatvely elated. = 0 the the fuzzy sets ad have o elatoshp at all. Though the values of the fuzzy membeshp fucto ae costaed betwee [01] the value of the fuzzy smple coelato coeffcet les betwee [-11]. Ths ot oly eveals the degee of the elatoshp betwee the fuzzy sets but also dcates whethe these two sets ae postvely o egatvely elated. CVP P MM x = 0 x = 1 x = x > (10) x = 0 x = 0 ~ 300 x = 301 ~ 800 x > 801 (11) x = dect maketg x = dect maketg (1) Key factos of telecom chu maagemet Expemetal settg The expemetal dataset used ths study esulted fom the adomly sampled custome eteto actvtes ad the esposes of customes of a telecom compay Tawa whose cotacts wee due to expe betwee Jue ad July 008. The compay developed two maketg pogams to eta the customes. The fst pogam volved a dscout o mothly blls whle the secod pogam was a cellula phoe puchase pomoto. Fom the customes whose cotacts wee due to expe Jue ad July customes wee adomly selected fom each of the followg goups: customes wth mothly blls of NT$ 0 to 300 customes wth mothly blls of NT$ 301 to 800 ad customes wth mothly blls of NT$ 801 to Each goup of 400 customes wee the dvded futhe to two subgoups of 00 customes each. Custome eteto maketg pogams wee mplemeted usg dect mal (dect maketg) ad telemaketg (dect maketg). Dug ths eteto maketg pocess customes could choose the maketg pogams they wated. Table 1 dsplays the esults of the ete eteto maketg pocess. NLYE ND REULT To extact the key factos of telecom chu maagemet ths study uses a smple fuzzy coelato coeffcet to aalyze the coelato elatoshp betwee eteto ate ad othe attbutes Table 1. The attbutes Table 1 must be coveted to fuzzy attbutes. CVP s the membeshp fucto of cotact valdty peod; P s the membeshp fucto of bll paymet; MM s the membeshp fucto of maketg method; whle the membeshp fucto of eteto ate. MM RR ae show as follows: RR s CVP P RR x 0 0 < x < x < x < x < x (13) ccodg to Equato (3) the fuzzy coelato coeffcet betwee eteto ate ad cotact valdty peod RR CVP s equal to 0.06; the fuzzy coelato coeffcet RR P betwee eteto ate ad bll paymet s equal to 0.38; the fuzzy coelato coeffcet betwee eteto RR MM ate ad maketg method s equal to Theefoe the most mpotat facto of chu maagemet s maketg method. Pevous eseach (Chueh et al. 011) used the decso tee techque (ey et al. 004; Duham 003; Fayyad et al. 1996; Ha et al. 001; Katadzc 003; Qula 1986; Ta et al. 006) to costuct a effectve chu maagemet model ad educe chu ates based o custome esposes to eteto actvtes pefomed by custome sevce cetes. Fgue 1 shows the poposed custome eteto model. ccodg to Fgue 1 the chu eteto model cofms the key factos detemed by fuzzy coelato aalyss. The custome eteto model dcates that fo customes wth mothly blls of NT$ 301 to 800 ad NT$801 to 1000 telemaketg was a effectve custome eteto stategy fo customes whose cotacts exped ethe Jue o July. Howeve fo customes whose mothly blls wee NT$ 0 to 300 ad whose cotacts

5 846 f. J. us. Maage. Table 1. The esults of custome eteto actvtes. Custome goup Cotact valdty peod (moth) ll paymet (NT$) Maketg method Reteto ate (%) Dect maketg Idect maketg Dect maketg Idect maketg Dect maketg Idect maketg Dect maketg Idect maketg Dect maketg Idect maketg Dect maketg Idect maketg 11 Fgue 1. Telecom chu maagemet model. exped Jue oly a dect mal set Jue (the cotact expato moth) was a effectve custome eteto stategy. Table 1 also dcates that fo customes whose mothly blls wee NT$ 801 to 1000 ad whose cotacts exped Jue telemaketg Jue was the most effectve custome eteto stategy. The custome eteto ate fo ths custome goup was 87%. Howeve fo customes whose mothly blls wee NT$ 801 to 1000 ad whose cotacts exped July telemaketg pefomed Jue (oe moth befoe cotact expato) acheved a 74% custome eteto ate 13% lowe tha the best custome goup. Telemaketg pefomed dug the moth of cotact expato had the geatest effect o custome eteto. Cocluso Most studes of the telecom chu poblem focus o

6 Chueh 847 costuctg a effectve chu pedcto model to locate lsts of potetally lost customes advace. Howeve detfyg potetally lost customes does ot mea those potetally lost customes ca be etaed. effectve custome eteto stategy must be employed to effectvely educe chu ates. Ths study utlzed fuzzy statstcs aalyss to detfy the key factos of telecom chu maagemet. These key factos wee cofmed by a data mg-based chu maagemet model pevous eseach. alyss of custome esposes to maketg actvtes s useful detemg optmal eteto tmg ad eteto method combatos whe developg effectve custome eteto stateges fo dffeet custome goups. REFERENCE h JH Ha P Lee Y (006). Custome chu aalyss: Chu detemats ad medato effects of patal defecto the Koea moble telecommucatos sevce dusty. Telecomm. Polcy 30: u W Che KCC Yao X (003). ovel evolutoay data mg algothm wth applcatos to chu pedcto. IEEE Tas. Evol. Comput. 7: ey MJ Loff G. (004). Data mg techques: Fo maketg sales ad custome suppot. NY: Joh Wley & os. eso mth Thealg K (000). uldg data mg applcatos fo CRM NY: McGaw-Hll. uckx W Va de Poel D (005). Custome base aalyss: patal defecto of behavoually loyal clets a o-cotactual FMCG etal settg. Eu. J. Ope. Res. 164: Chu H Tsa M Ho C (007). Towad a hybd data mg model fo custome eteto. Kowl. ased yst. 0: Chag D L NP (1999). Coelato of Fuzzy ets. Fuzzy ets yst. 10: 1-6. Chag D L NP (000). Patal Coelato of Fuzzy ets. Fuzzy ets yst. 110: Chueh HE L C Ja NY (011). Mg the Telecom Maketg omato to Optmzg the Custome Reteto tateges. ICIC Expess Lett. Pat ppl. : 1-6. Coussemet K Va de Poel D (008). Chu pedcto subscpto sevces: applcato of suppot vecto maches whle compag two paamete-selecto techques. Expet. yst. ppl. 34: Coussemet K Va de Poel D (008). Itegatg the voce of customes though call cete emals to a decso suppot system fo chu pedcto. om. Maage. 45: Dowdy Weade (1983). tatstcs fo eseach. Joh Wley ad os NY. Duham MH (003). Data mg toductoy ad advaced topcs. NJ: Peaso Educato. Fayyad U Uthuusamy R (1996). Data mg ad kowledge dscovey databases. Commu. CM. 39: 4-7. Ha J Kambe M (001). Data Mg: Cocepts ad Techques. NY: Moga Kaufma. Hug Y Ye DC Wag HY (006). pplyg data mg to telecom chu maagemet. Expet. yst. ppl. 31: Katadzc M (003). Data mg-cocepts models methods ad algothms. Joh Wley & os NY. Keaveey M (1995). Custome swtchg behavo sevce dustes: exploatoy study. J. Mak. 59: Km H Yoo CH (004). Detemats of subscbe chu ad custome loyalty the Koea moble telephoy maket. Telecomm. Polcy 8: Km MK Pak MC Jeog DH (004). The effects of custome satsfacto ad swtchg bae o custome loyalty Koea moble telecommucato sevces. Telecomm. Polcy 8: Lavee Va de Poel D (004). Ivestgatg the ole of poduct featues pevetg custome chu by usg suvval aalyss ad choce modelg: The case of facal sevces. Expet. yst. ppl. 7: Lejeue M (001). Measug the mpact of data mg o chu maagemet Iteet Reseach. Elect. Netwok ppl. Polcy 11: Luo hao P Lu D (007). Evaluato of thee dscete methods o custome chu model based o eual etwok ad decso tee PH. Poceedgs of the fst teatoal symposum o data pvacy ad e-commece Matteso R (001). Telecom chu maagemet. NC: PDG Publshg. Moze MC Wolewcz R Gmes D Johso E Kaushaky H (000). Pedctg subscbe dssatsfacto ad mpovg eteto the weless telecommucatos dusty. IEEE T. Neual Netw. 11: Nga EWT Xu L Chau DCK (009). pplcato of data mg techques custome elatoshp maagemet: lteatue evew ad classfcato. Expet. yst. ppl. 36: Qula JR (1986). Iducto of Decso Tees. Mach. Lea. 1: Ta PN tebach M Kuma V (006). Itoducto to data mg osto: Peaso ddso Wesley. Tsa CF Che MY (010). Vaable selecto by assocato ules fo custome chu pedcto of multmeda o demad. Expet. yst. ppl. 37: Tsa CF Lu YH (009). Custome chu pedcto by hybd eual etwoks. Expet. yst. ppl. 36: We CP Chu IT (00).Tug telecommucatos call detals to chu pedcto: a data mg appoach. Expet. yst. ppl. 3: Xa GE J WD (008). Model of custome chu pedcto o suppot vecto mache. yst. Eg. Theoy Pact. 8: Zadeh L (1965). Fuzzy sets. om. Cotol 8: