Data Mining Analysis and Modeling for Marketing Based on Attributes of Customer Relationship

Transcription

1 School of athematcs and Systems Engneerng Reports from SI - Rapporter från SI Data nng Analyss and odelng for arketng Based on Attrbutes of Customer Relatonshp Xaoshan Du Sep 2006 SI Report Väö Unversty ISSN SE VÄXJÖ ISRN VXU/SI/DA/E/ /--SE

2 Data nng Analyss and odelng for arketng Based on Attrbutes of Customer Relatonshp Xaoshan Du School of athematcs and Systems Engneerng, Väö Unversty SE Väö, Sweden Supervsor: Joakm Nvre

3 Abstract. Wth the rapd growng marketng busness, Data nng technology s playng a more and more mportant role n the demands of analyzng and utlzng the large scale nformaton gathered from customers. To predct the consequent busness strategy by usng Data nng, the Customer Relatonshp anagement CR nowadays s requred to evaluate the customer performance, dscover the trends or patterns n customer behavor, and understand the factual value of ther customers to ther company. In ths paper, we present an effectve model to apply Data nng to the CR problem of categorzng the customers n marketng to search for potental clents based on ther propertes by 1 computng Dstance n Cluster Analyss and Lft n Assocaton Rules accordng to the Attrbutes of Customer Relatonshp ACR ncludng Self-Relance Inde, Impact Inde and atr for customer value, and 2 n the Data nng modelng theory, constructng the Regresson odel n the ACR and mplementng the correspondng algorthm to mne the most proftable customer group

4 1 Introducton Background and motvaton roblem State Thess Outlne Data nng ethods and odels Data nng rocess Data reparaton Knowledge dscovery n database odel Eplan and Estmate Categorzaton of Data nng ethods Categorzaton Based on nng Tasks Categorzaton Based on nng Obects Categorzaton Based on nng Technques Analyss and odelng for Data nng Fundamentals of odel Structures of redctve model Lnear Regresson odel redctve model for Classfcaton Stochastc arts of Data nng odel Summary Data nng n arketng Applcaton of Data nng n arketng Applcaton of Data nng n CR Introducton of CR Concept of acr Summary odelng Based on Attrbutes of Customer Relatonshp ACR roblem statement Crteron of Customer Value Dscusson based on Customer Classfcaton Segmentaton of Customer Value Concept of Attrbutes of Customer Relatonshp ARC Dssmlarty n Cluster Algorthm Lft n Assocaton Rules Search Reference ethod for Network Relaton Implementaton of Data nng odel

5 4.1 Symbolc System Estmate of urchase robablty Evaluate Customer Value Eperments Epermental Setup Data Set an Functons Name and Descrpton nng Algorthm The evaluated results Dscusson Concluson and Future Works Acknowledgement References

6 1 Introducton From ths Chapter you wll get a man dea of ths thess, nclude ts background, why we choose topc, and whch problems wll be solved n ths thess, as well as how can we solved them. 1.1 Background and motvaton Tradtonal Large-scale sales pattern s the most famlar sales pattern for companes. Based on ths atten, companes usually am at ther produces, products and then gve all the customers same sales promoton. However, ths knd of sales promotons neglects the dfferences among customers. In most cases, these promotons cost a lot, but only get few real profts from customers. That means many promotons are waste. In the meanwhle, data mnng technologes become more and more popular n commercal terran, such as n bankng ndustry, nsurance ndustry and retal trade. Data mnng can solve many typcal commercal problems, such as Database arketng, Customer Segmentaton and Classfcaton, rofle Analyss, Cross-sellng, Churn Analyss, Credt Scorng, Fraud Detecton, and so on. Snce those data mnng technologes appeared, companes have changed ther sales target from products to customers. How to classfy customers? How to fnd out the common character of customers from database? How to dg up the potental customers? How to fnd out the most valuable customers? These knds of questons become the most popular data mnng applcatons n marketng. Nevertheless, the recent customer relaton analyses have some serous drawbacks. The most mportant one s that based on those analyses company usually consder the customer as an solated obect and havng value only when he/she deals wth ths company. Neglect the network value of each customer and the value from potental purchase probablty. In ths paper, whch s based on the applcaton of data mnng n marketng and recent research result of Customer Relatonshp anagement, we would lke to try to use new vsual angle to mprove these drawbacks

7 1.2 roblem State In ths paper, two man problems wll be solved. The frst one s how to generally classfy the customers by ther value? The second one s how data mnng technques can be used to estmate the value of a customer gven a database contanng nformaton about hs/her name, age, professon, etc? Ths s the most mportant one. In ths paper the value of a customer should be consdered as how much proft he/she can brng to the company. Ths value should be calculated as a numercal scale. It can be prmarly defned lke ths: V= rm - rn C. Where r s the proft brought from a specfc produce when customer buys t. m s the customer s purchase probablty when there s a sales promoton, whle n s the customer s purchase probablty when there s no sales promoton. C s the cost of the sales promoton. To get the value of a customer V, the customer s purchase probablty must be calculated at frst. In ths paper, Attrbute of Customers Relaton ACR wll be defned to represent the probablty At last, the new model and algorthms should be epermented and the result should be evaluated. 1.3 Thess Outlne The frst Chapter s an ntroducton of the paper, ncludng background, motvaton and thess outlne. In the second Chapter, we are gong to ntroduce some related data mnng methods and models. Furthermore, gve some ntroductons of data mnng n marketng. Gve a short ntroducton of CR, ncludng ts categorzaton and applcatons of data mnng n CR. In thrd Chapter, we would lke to focus on modelng based on ACR. We are gong to defne some new concepts and algorthms n ths Chapter, n order to get ready for the followng chapter as well. In the fourth Chapter, the mplementaton of our data mnng model wll be defned. After that, the ffth Chapter wll gve out the general dea of eperments. In addton, we put concluson and future works, acknowledgement, and references at the end of ths paper

8 2 Data nng ethods and odels Frst of all, we d lke to gve out the defnton of Data nng. Data nng s the process of dentfyng hdden patterns and relatonshps wthn data. [11] In another word, Data nng s the process fndng hdden nformaton n a database. [12] From the defnton of data mnng, we learn that t s a knd of technologes that can help us know the useful thngs hdden n the data. Therefore, data mnng should be an nterestng work. 2.1 Data nng rocess There are three man steps of data mnng process Data reparaton In the whole data mnng process, data preparaton s somehow a sgnfcant process. Some book says that f data mnng s consdered as a process then? Data preparaton s at the heart of ths process. However, nowadays databases are hghly susceptble to nose, mssng and nconsstent data. So preprocessng data mprove the effcency and ease of the data mnng process, ths becomes an mportant problem. Several consultng frms, such as IB, have approved that data preparaton costs 50%~ 80% resource of the whole data mnng process. From ths vew, we really need to pay attenton to data preparaton. There are three data preprocessng technques should be consdered n data mnng: 1 Data cleanng a Inconsstent data: Not all the data we get s clean. For eample, a lst of Natonalty may have the values of Chna,.R.Chna, and anland Chna. These values refer to the same country, but are not known by the computer. Therefore, ths s a consstency problem. b ssng values Data from a company s database often contans mssng values. Sometmes the approaches requre rows of data to be complete n order to mne them, but the database may contan several attrbutes - 7 -

9 wth mssng values. If too many values are mssng n a data set, t becomes hard to gather useful nformaton from ths data. c Nosy data Nose s a random error or varance n a measured varable. 2 Data ntegraton Usually the data analyss task wll nvolve data ntegraton. It combnes data from multplyng sources nto a coherent data store. Those sources nclude multple database or flat fles. Several ssues should be consdered durng data ntegraton, such as schema ntegraton, correlaton analyss for detectng redundancy, and detecton and resoluton of data value conflcts. Careful ntegraton of the data can help mprove the accuracy and speed of the mnng process. 3 Data reducton If you select data from a data warehouse, you probably fnd the data set s huge. Data reducton technques can be appled to obtan a reduced representaton of the data set. nng on reduced data set should be more effcent yet produce the same analytcal results. It ncludes several strateges, such as data cube aggregaton, dmenson reducton, data compresson, numerosty reducton, and dscretzaton and concept herarchy generaton Knowledge dscovery n database As a core data mnng technques, knowledge and nformaton dscovery has several man components: 1 Determne the type of data mnng tasks We must confrm that the functons and tasks to be acheved by recent system belong to whch knd of classfcaton or clusterng. 2 Choose sutable technologes for data mnng We can choose the approprate data mnng technologes based on the tasks we have confrmed. Such as, classfcaton model often use learnng neural network or decson tree to realze; whle clusterng usually use clusterng analyss algorthms to realze; assocaton rules often use assocaton and sequence dscovery to realze. 3 Choose the algorthms Based on the technologes have been chosen, we can select a specfc algorthm. Furthermore, a new effcent algorthm can be desgned by the specfc mnng tasks. To choce data mnng algorthms, we should determne the hdden pattern n selectng data

10 4 nng data We are supposed to use the selected algorthms or algorthms portfolo to do repeated and teratve searchng. Etract the hdden and nnovatve patterns from data set odel Eplan and Estmate Eplan and estmate the patterns got from data mnng, get the useful knowledge. For nstance, remove some rrespectve and redundant patterns, after fltraton the nformaton should be presented to customers; Use vsualzaton technology to epress the meanngful model, n order to translate t nto understandable language for users. A good applcaton of data mnng can change prmal data to more compact and easly understand form and ths form can be defned defntely. It also ncludes solvng the potental conflct between mnng results and prevous knowledge, and usng statstcal methods to evaluate the current model, n order to decde whether t s necessary to repeat the prevous work to get the best and sutable model. The nformaton acheved by data mnng can be used later to eplan current or hstorcal phenomenon, predct the future, and help decson-makers make polcy from the ested facts. 2.2 Categorzaton of Data nng ethods There are several data mnng methods, and there are some dfferent ways to classfy them as well Categorzaton Based on nng Tasks Based on the dfferent mnng tasks, we can categorze date mnng methods as classfcaton, clusterng, regresson, assocaton rules, sequence dscovery, predcton, and so on. [13] 1 Classfcaton Classfcaton maps data nto predefned group or classes. Because the classes are determned before eamnng the data, classfcaton s often consdered as supervsed learnng. Classfcaton algorthms requre that the classes be defned based on data attrbute values. They often descrbe these classes by lookng at the characterstcs of data whch are already known to belong to the classes. 2 Clusterng - 9 -

11 Clusterng s smlar to classfcaton; the dfference s the groups are not predefned. It s alternatvely referred to as unsupervsed learnng. It s usually acheved by determnng the smlarty among the predefned attrbutes of the data. The most smlar data are grouped nto clusters. 3 Regresson Regresson s used to map a data tem to a real valued predcton varable. Regresson assumes that the target data ft nto some known type of functons and then determnes the best functon of ths type. A smple eample of regresson s the standard lnear regresson. 4 Assocaton rules Assocaton rules alternatvely referred to as affnty analyss. An assocaton rule s a model that dentfes specfc types of data assocatons. They are usually used n the retal sales communty to dentfy tems whch are often purchased together. 5 Sequence dscovery It s used to determne sequental patterns n data. Those patterns are based on a tme sequence of actons. The relatonshp of those patterns s based on tme, and they are smlar to assocatons. 6 redcton Based on past and current data, many real-world data mnng applcatons can be consdered as predctng future data states. redcton s vewed as a type of classfcaton. The dfference s that predcton s predctng a future state rather than a current state. redcton applcatons nclude floodng, speech recognton, machne leerng, and pattern recognton Categorzaton Based on nng Obects If we categorze based on mnng obects, the data mnng methods can be dvded nto based on Relatonal Database, Obect Orented Database, Spatal Database, Tet Data Sources, Temporal Database, ultmeda Database, Heterogeneous Database, and Web source Categorzaton Based on nng Technques There are many dfferent technques used to acheve D tasks, so we can prmarly dvded D methods nto achne learnng methods, Statstcal

12 methods, and Neural Networks methods. And then subdvson them as follows:[13] 1 achne Learnng methods a Decson Trees Decson tree s one of the most popular classfcaton algorthms n current achne Learnng. A decson tree s usually used n classfcaton, clusterng and predcton tasks as a predctve modelng technque. They are deal methods for makng fnancal decsons where lots of comple nformaton needs to be taken nto account. b Genetc Algorthms Genetc algorthms are a method of breedng computer solutons to optmzaton problems by smulated evoluton. The processes based on crossover and mutaton repeatedly appled to a populaton of bnary strngs. Tme after tme, the better ft ndvduals and average ndvduals are created, and a good soluton to the problem s found. Genetc algorthms usually are used to predct and used to replace the mssng attrbutes. It means when there are some attrbutes mssed, we can analyses ts specmens usng genetc algorthms to get the possble value and replace the mssng one. 2 Statstcal ethods a Statstcal Analyss Statstcal analyss s one of the most mature and proven data mnng methods. The key of ths method s construct approprate statstcal models and mathematcal models to nterpret the data models. Ths approach requres the user has abundant knowledge of ths feld. Generally, statstcal analyss has two steps: frstly, the user chooses the approprate data from data warehouse. Secondly, the user uses vsualzaton functons and analyss functons provded by statstcal analyss tools, n order to fnd the relatonshp between the data and statstcal models and constructed mathematcal models to nterpret data. The second step needs to be repeated and contnuous carefully. b Cluster Analyss Cluster analyss classfcaton s based on ther characterstcs n order to dscover typcal pattern. When the data that should be analyzed mss the descrbe nformaton or can not be organzed nto any classfcaton model, the use of cluster analyss wll be automatcally dvded nto categores accordng to certan

13 characterstcs. The substance of cluster analyss s an overall optmal problem, commonly used n the market subdvson, customer orentaton, performance evaluaton, and other aspects. 3 Neural Networks Neural Networks s an nformaton processng system that conssts of a graph representng the processng system as well as varous algorthms that access ths graph. It s structured as a graph wth many nodes and arcs between them. It can be consdered as a drected graph wth nput, output and nternal nodes. After accept a varety of nput, every nerve calculates the total nput value and then uses flterng mechansms to compare the total nput, n order to determne ts own output value. When change the lnk weght between two nerves or two layers, neural network s on a study or "tranng." After "tranng" the neural network can be used to predct the lkely outcome of the estng cases, the analyss could also be appled n customer relatons, or other felds. 2.3 Analyss and odelng for Data nng In the prevous sectons we brefly descrbed the process of data mnng and data analyss methods. As some of desgn modelng wll nvolved n ths paper, ths subsecton wll be used to gve more n-depth dscusson about the concept of modelng, and nspect several maor types of models for data mnng, n order to provde a theoretcal bass for the follow-up sectons. odel s hgh-level data sets, and a global summary. It usually treats the nteger through a large group of samples. odels can be descrptve, whch nduce data usng a concse manner; they can also be ratonal, whch allowng makng some certan nferences for the data nteger or future data. In ths paper, we wll ntegrate several forms of theoretcal models as a bass, such as Lnear Regresson odels, Hybrd odels, and arkov odel. In modelng, t must be noted that when summarzed data we should take account nto factors, such as tme factor. If the used method has certan lmtatons, t may lead to a dstorton n the model. Therefore, a model s good or bad, that needs to be tested by realty Fundamentals of odel

14 odels are abstract descrpton of the real world process. We start from the smplest model to epatate ts meanng. For eample, a smple model mght take the form of Y = ax + c, where Y and X are varables and a and c are parameters of the model constants determned durng the course of the data mnng eercse. Here we would lke to say that the functonal form of the model s lnear, snce Y s a lnear functon of X. We can set a=1, c=2 to smplfy ths model. ore generally, we also can mprove ths model to Y = ax +c +e, where e s a random varable component of the mappng from X to Y. Normally, a, c are consdered as the parameters of the model, and we often use the notaton θ to epress a generc parameter or a parameters set, where θ = {a, c}. Gven the form of structure of a model, we choose the approprate values for ts parameters. Ths s acheved by mnmzng or mamzng an approprate score functons measurng the ft the model to the data. odelng s to dscuss ssues from a theoretcal perspectve, but we must be recognzed that the theory and practcal phenomenon are always dfferent. We should recognze that "all models are not perfect, but some are useful. For eample, we may assume that the estence of a lnear model abstracts a process, but the realty s that there are always some nonlnear roles. Ths s what the models can not take nto account. What we want s to fnd a model whch can summarze the man features of a process. The followng are eamples of several common model structures [16] Structures of redctve model In a predctve model, the varable s epressed by a functon of the other varables. We take Y = ax +c +e as an eample. The values of the response varable Y are predcted from gven values of predctor varables X. Generally, the responsor varable n predctve models s often denoted by Y, and the p predctor varables are denoted by 1,2, p. The model wll be yeld predctons, y=f 1, 2, p ; θ, where y s the predcton of the model and θ represents the parameters of the model structure. When Y s quanttatve, ths task of estmatng a mappng from p-dmensonal X to Y s known as Regresson. When Y s categorcal, the task of learnng a mappng from X to Y s called Classfcaton learnng. Both of them can be referred as Functon Appromaton problems n whch we are learnng a mappng from a p-dmensonal varable X to Y Lnear Regresson odel

15 Net, we are gong to dscuss a lnear predctve model. Its structure s smple and easy to understand. Its response varable s a lnear functon of the predctor varables: Y = a 0 + p = 1 a X a a a Whereθ = { 0, 1,..., p }. We note that the model s purely emprcal, so that the estence of a well fttng and hghly predctve model does not mply any causal relatonshp. We can retan the addtve nature of the model, whle generalzng beyond lnear functons of the predctor varables: Where the Y = a p 0 + a f X = 1 f functons are smooth functons of X. f could be log, square-root, or related transformatons of orgnal X varables. The model assumes that the dependent varable Y depends on the ndependent varables X of the model n an addtve fashon. Ths may be a strong assumpton n practce, but t wll lead to a model n whch t may be easy to nterpret the contrbuton of each ndvdual X varable. Ths can be found n our frst model n ths paper. Furthermore, we can generalze ths lnear model structure to allow general polynomals n the Xs wth cross-product terms to allow nteracton among the X n the model. Note that by allowng models wth hgher order terms and nteractons between the components of X we can estmate a more comple surface than a smple lnear model. However, we note that as the dmensonalty p ncreases, the number of possble nteracton terms X n the model ncrease as a combnatoral functon of p. The nterpretaton and understandng of such a model makes the problem more dffcult, moreover t wll become more dffcult as p ncreasng. But the response varables compared to the parameters of model are stll lnear. So the estmate of the parameters wll become much easer. The generalzaton to polynomals s called the complety of the model. The more comple models contan the smpler models as specal cases. For eample, the 1st order a 1 X 1 +a 0 model can be consdered as a specal case of 2 the 2nd order polynomal model a 2X1 + a1 X1 + a0 by set a 2 = 0. So t s clear that a comple model can always ft the observed data at least as well as any smpler model can. Ths rases the complety of how we should choose one

16 model rather than others when the complety of each s dfferent. There s always a queston. We may want a model whch s closest to some hypothess, a model that captures the man features of the data wthout beng too complcated, and so on. We must know how to fnd a model balance both precson and effcency. To generalze a lnear structure we can transform the predctor varables X. We can also transform the response varable. A good way for further generalzaton s to assume that Y s locally lnear n the X s, wth a dfferent local dependence varous regons of the X, that s a pecewse lnear model. The pecewse lnear model s a good way to solve how we can buld relatvely comple models for nonlnear phenomena by pecng together smple components. Ths s also why we use t as an mportant fundamental of our model redctve model for Classfcaton By now we have dscussed about predctve models whch the varable s predcted, where Y s quanttatve. Now we consder the case of a categorcal varable Y, whch only take a few possble categorcal values. Ths s a classfcaton problem. The am s to assgn a new obect to ts correct Y category on the bass of ts observance X value. In classfcaton problem what we need to do s to set dfferent data to dfferent categores. A classc approach s to use a lnear hyperplane n the p-dmensonal X space to defne a decson boundary between two classes. The model parttons the X space nto dsont decson regons, where the decson regons are separated by lnear boundares. We can use the hgher-order polynomal terms, yeldng smooth polynomal decson boundares, to get a more comple model Stochastc arts of Data nng odel In the prevous dscusson, we brefly referred to stochastc parts of data mnng model. Now we are gong to talk about the functons of stochastc parts. It s very hard to fnd a perfect functonal relatonshp between the predctor varables X and the response varable Y. The fact s for any gven predctor varables, more than one value of Y can be observed. The dstrbuton of the values Y at each value of X represents an aspect of varaton. The varaton can be dvded nto two categores: 1 Uneplanable varaton

17 Ths knd of varaton wll be reduced by decreasng the complety of the model. We call t uneplanable or nonsystematc or random parts of the varaton. 2 Eplanable varaton Ths knd of varaton s also called Systematc varaton. The varaton n Y can be eplaned by the X varables. For eample: We have mentoned the regresson modelng before. We can etend t to nclude a stochastc part. We assume that for each X we can observe a partcular Y, but the Y s added some nose. So the relatonshp between X and Y become: y = g, θ + e Where g, θ s a determnstc functon of X, whle e s usually set to zero and assumed to be a random varable wth constant 2 varance σ, whch s ndependent from X. The random term e reflects the nose n the measurement process. ore generally, e reflects the fact that there are hdden varables. Ther affectons on Y can not be epressed by the determnstc functon of X. Rasng the stochastc parts gves a good way to mprove the accuracy of models Summary In the dscusson so far, we brefly ntroduced the modelng theory n data mnng. The core prncple s ncorporatng the relatvely smple models nto comple model, or usng dfferent methods generalze the smple model to comple model. Accordng to the modelng theory, none of the models for data mnng s absolutely solated, but nterconnected by a varety of relatons. Ths s not hard to understand. As a comple functon s ncorporated by several basc functons, each model for data mnng s a generalzaton of other models, or a specal case of other model. As we all know, n data mnng the key of establshng an effectve model s to select the best model form n order to solve current problem. Ths s not only the process of selectng a model, dealng wth date, and gvng out the results. The process of modelng needs contnuous fttng work, and repeated mprovng work. Ths process s knd of endless

18 2.4 Data nng n arketng In the recent decades, the development of nformaton and communcatons technologes nects new vtalty for enterprse marketng. For eample, bar code technology and the emergence of onlne stores greatly both enhance the effcency of the enterprse. Resultng, company managers are begnnng to face the enormous data. The data s ncreasng at a very rapd pace, probably 1000 tmes than fve years ago. However, the data and busness profts are not drectly proportonal. Unfortunately, the human bran can not handle so much data. In the meanwhle, data mnng technology becomes very mature n theory. Thess the technology-orented applcatons for enterprse decson makers wth a new perspectve to look at market. Those advanced technologes let enterprses obtan a lot of resources from dfferent channels, and use those effectve tools to translate data nto unlmted opportuntes Applcaton of Data nng n arketng D technology n the marketng s a relatvely unversal applcaton. Such applcatons are referred to a Boundary Scence, because t sets a varety of scentfc theores n all. Frst, two basc dscplnes: Informaton Technology and arketng. Another very mportant bass s Statstcs. In addton, t relates to the psychology and socology as well. The charm of ths area s ust about the wde scope of dscplnes study. Generally speakng, through the collecton, processng and dsposal of the large amount of nformaton nvolvng consumer behavor, dentfy the nterest of specfc consumer groups or ndvdual, consumpton habts, consumer preferences and demand, moreover nfer correspondng consumpton group and the net group or ndvdual consumpton behavor, then based on them sale produces to the dentfcaton consumer groups for a specfc content-orented marketng. Ths s the basc dea. As automaton s popular n all the ndustry operate processes, enterprses have a lot of operatonal data. The data are not collected for the purpose of analyss, but come from commercal operaton. Analyss of these data does not am at studyng t, but for gvng busness decson-maker the real valued nformaton, n order to get profts. Commercal nformaton comes from the market through varous channels. For eample, purchasng process by credt card, we can collect the customer s consumpton data, such as tme, place, nterestng goods or servces nterested, wllng prce and the level of recepton capacty; when buyng a brand of cosmetcs or fllng n a member form can collect customer purchase trends and frequency. In addton,

19 enterprses can also buy a varety of customer nformaton from other consultng frms. arketng based on data mnng usually can gve the customer sales promoton accordng to hs prevenent purchase records. It should be emphaszed data mnng s applcaton-orented. There are several typcal applcatons n bankng, nsurance, traffc-system, retal and such knd of commercal feld. Generally speakng, the problems that can be solved by data mnng technologes nclude: analyss of market, such as Database arketng, Customer Segmentaton & classfcaton, rofle Analyss and Cross-sellng. And they are also used for Churn Analyss, Credt Scorng and Fraud Detecton. Fg 2.1 shows us the relaton between applcaton and data mnng technques clearly and completely. Fg.2.1 Applcaton of data mnng for marketng The basc process of data mnng n marketng show as follows: Fg.2.2 shows the prncple of data mnng applcaton n marketng a repare prmtve data. It ncludes ndvdual character nformaton such as age, gender, hobby, background, professon, address, postcode, and ncome, the prevous purchase eperence, and

20 the relatonshp wthn customers. The preprocessng of prmtve data s very mportant for selectng potental customers. b Establsh a certan model. Ths model may utlze plenty of tradtonal data mnng technologes and many technologes from other related subects. However, the problem whch those technologes should solve s seekng for the best or acceptable market plan, wthn lmted data source, lmted tme, and lmted epense. The three lmts are the fundamentalty of modelng algorthm. c At last, accordng to the model, utlze testng data to get each pattern or parameter. Ultmately, use ths model to select customers and decde marketng plan. Data from nsde arketng Data from outsde Sample D methods Test data usng other models Create model & Eperment Evaluate epermental Fg Schematcs for D applcaton n arketng 2.5 Applcaton of Data nng n CR In ths subsecton, we gve out the ntroduce of CR. It ncludes both ocr and acr. Whle what we focus on s acr Introducton of CR Customer Relatonshp anagement CR s a strategy to acqure new customers, to retan them and to recover them f they defected. [15] In the recent days, ndvdual customer has brought pressure of change n marketng

21 practces. One of the man goals of CR s: Generatng addtonal product benefts by means of communcatons and servces whch are desgned and delvered to match the ndvdual needs of customers. There are two knds of CR [15]: 1 Operatonal CR ocr actvty s mplemented n the enterprse processes: sales, marketng or servce. ocr nvolves all actvtes about the drect customer contact. 2 Analytcal CR acr provdes all components to analyze customer characterstcs n order to accomplsh ocr actvtes, wth respect to the customers needs and epectatons. There, the dealstc goal s to provde all nformaton necessary to create a talored cross-channel dalogue wth each sngle customer on the bass of hs or her actual reactons. We d better to look at CR whch ncludes ocr and acr as a cross enterprse process, n order to acheve the goal to show merely one company mappng to a customer. arketng, sales and servce departments have to coordnate ther responsbltes, actvtes, nformaton systems and data. The Fg.2.3 shows the cross functonal process of CR. arketng Sales Servce CR as cross-enterprse process Concept of acr Indvdual Fg.2.3. CR as cross functonal process Data mnng n the CR applcaton s prmarly emboded n: customer classfcaton, analyss of customer relatons, market orentaton, and establshng predctve models. That s CR analyss module. Fg.2.4 shows the structure of acr

22 CR analyzng and estmatng subsystem Customer Classfcaton odule Analyss of Customer Behavors odule Analyss of arket odule Other Subsystems Customer Database & Routne Data Deposted Fg.2.4. Structure of acr 1 Customer classfcaton module Ths module classfes customers by customers value and set relevant customer level. It can lead the enterprses to dstrbute the resources of market, sales, and servces to the valuable customers. The enterprses can am at the valuable customers gvng them specal sales promotons, and provdng more personalzed servces, n order to get the mamum returns by least nvestment. Ths s what we are nterested n. Generally, classfcaton can be consdered through three aspects: a. Eteror attrbutes These attrbutes nclude the customer s regonal dstrbutng, holdng produces, and organzatonal attrbute Customer can be dvded to enterprse customer, government customer and ndvdual customer. Usually, ths knd of classfcaton s smple and ntutonst. The data s also very easy to get. However, ths knd of classfcaton s general. We stll don t know that those hgh valuable customers wthn n each classfcaton are. What we know s only whch category of customers have more purchase power than other category. b. Inherent attrbutes These attrbutes nclude age, gender, nterest, ncome, credt, and so on. c. Classfcaton of consume behavors The analyss of consume behavor s usually consdered as three aspect. That s RF: recent consume, frequency of consume, and magntude of consume. All of these data can be acheved from the accountng system. However, ths knd of classfcaton can only be used on estng customers. Snce

23 there s no consume, the potental customers can not be classfed by t. CR can dvde customers nto many categores. The customers wthn one category have the same attrbutes, whle the attrbutes of customers from dfferent category are certanly dfferent. We can provde dfferent servce to customers from dfferent category, n order to enhance the satsfacton. We can easly fnd the advantages of classfcaton. Even a smple classfcaton can brng the enterprse a satsfyng result. 2 Analyss of customer behavors module Ths module manly process the analyss of customer s satsfacton, loyalty of customer, correspondence of customer, predcton of customer s lost, and cross sales. 3 Analyss of market module arket s the man goal of enterprse. The enterprse can wn the competton only by handlng the trend of market. redcton of market trend ncludes analyzng and predctng the development of produces, predctng the dfferent consume trend of customer from dfferent regon, and predctng the changes appearng as the season s change. 2.6 Summary Whle ocr and acr are accepted by enterprses, data mnng technologes also get a more mportant role wthn acr. The key of acr s how to fnd out the most valuable customer and customer group for enterprses by data mnng technologes. Ths s what we are gong to solve n the followng chapters

24 3 odelng Based on Attrbutes of Customer Relatonshp ACR In ths chapter, we are gong to let you know why we use Attrbutes of Customer Relatonshp ACR and how does t look lke. Ths chapter s basc nformaton for Chapter roblem statement In ths subsecton, we focus gve a general dea about customer value. Ths wll help us defne how to fnd bg customer or more valuable customer Crteron of Customer Value Applcaton of data mnng n CR s helpng enterprse to dg out the most valuable customers. any managers and marketng decson-makers usually focus on the ncome-flu brought to enterprses by customers. Commonly, consume quantum s the crteron of customer value. It means that customer wth hgh consume quantum wll get more attentons. oreover, they wll get more favorable prce and better servce. Nevertheless, ths crteron of customer value s doubted lately. ore and more companes fnd that many bg customers are not large proftless customers, furthermore, sometmes they brng the company negatve proft. The reason s that company ddn t use a reasonable crteron to estmate customer value. Usng consume quantum as the crteron, the company potentally thought the more consume quantum s, the more value customer have. As a result, they gve too many servces to the bg customers whch dd not get a good result. Now we gve out two customers who have the same consume quantum, after comparng you wll fnd the problem of the crteron mentoned above. Assumng we have two bg customers, named A and B. A and B look smlar, because they brought almost same purchase to the company n last 12 months, furthermore ther consume trends are smlar. If we only use ncome-flu as the crteron of customer value, A and B should have the same value. But the probable fact s A s a loyal customer of ths company for years. A do not only buy produces from the company, he also recommend them to hs colleagues

25 and frends. After he bought one produce, hs frends maybe buy ten same produces. B as another bg customer buys produces, but he s always alone and rarely recommends others. Thus, we can fnd out the value of A and B s clearly dfferent, but ths dfference can not be udged by the smple crteron mentoned above. Va ths eample, we can fnd the lmt of untary crteron. Customer A certanly should get more attenton from the company. So we should fnd the more comprehensve crteron of customer value Dscusson based on Customer Classfcaton If we are not gong to use ncome-flu as the crteron to evaluate customers, we must fnd out another prncple for customer classfcaton. Customer classfcaton s the customer set parttoned by any attrbute of customer. We referred to much nformaton about customer classfcaton. Furthermore, we fnd that there are many questons when we make the classfcaton: 1 What s the dfference between customers? 2 What s the most comprehensve udgment of customer value? 3 What s the dfference of the customers who have the same purchase record? 4 Whch factor wll mpact the loyalty of customer? 5 What fashon does customer classfcaton have? Is there any other classfcaton varable besdes the proft brought by customer to company. 6 Is classfcaton untve? Thus, once a customer s classfed to a category, all the departments of ths company should have the same classfcaton behavor, rght? In order to consder all the questons above, we d better fnd the approprate crteron of classfcaton, and establsh approprate model to smulate customer group. Furthermore, use data mnng technology to partton certan customer group. And then use relatve marketng strategy on the most valuable customers. 3.2 Segmentaton of Customer Value Tradtonal concept of customer classfcaton n CR usually ust consders customers as many ndvdual unts or an obect. The methods we mentoned before are all based on the purchase probablty of potental customer and the profts from whch company can get. It assumes customers as a large obect

26 Every ndvdual customer s solaton and wthout any connecton. It merely consders the profts whch customers brng to enterprse. Ths s what we call self-value. However, customers and enterprses are all n the certan socal relatonshp network. When the customer s gong to make the decson of a purchase, the decson s not only depended on hs own nterest, but also mpacted by the opnons from others. In the meanwhle, he can mpact on the probablty of others of purchase the produce. Ths s what we call network-value. When two customers have the same self-value, we should gve more sales promotons to the one wth hgher network-value. In ths paper, we are gong to add the concept of network-value to the customer consderng system of CR. We dvde the large obect of customer, n order to do some deeper researches on the comple relatonshp of customer. Ths wll gve CR a new vsual angle of customer classfcaton. Frst of all accordng to the concept of network-value, we prmly dvde customer value nto two dmensonaltes. They are customer s self-value and network-value. Furthermore, we gve each dmensonalty two levels: hgh level and low level. By now we can dvde customers nto four groups. Ths subdvson wll be epressed as a matr, whch s atr for Customer Value Fg.5. III IV Network-value I II Self-value Fg 3.1. atr for Customer Value Accordng to ths Fg 3.1, we can fnd that IV category of customer s the one enterprse should focus on, whle I category s opposte. eanwhle, II and III category of customer should be dscussed. Category II s the favorte customer to tradtonal CR. They often can get the best prces and servces from enterprses. However, category III s usually neglected by tradtonal CR customer classfcaton. They rarely get the attentons from enterprse

27 because of the low self-value. After a long tme, ths category of customer wll lose confdence of the enterprse, so they leave. Accordng to ther hgh network-value, ths wll cause a large loss for the enterprse. After the analyss above, we thnk that evaluatng both self-value and network-value of customer carefully s sgnfcant to the marketng of enterprses. How to evaluate customers self-value? How to get the network-value by data mnng tools? To solve these problems s an nterestng work and we wll talk about them later. Therenafter we are gong to do more analyss of the atr for Customer Value. 3.3 Concept of Attrbutes of Customer Relatonshp ARC As we talked before, prevenent sales market has the lmt of nformaton communcaton. The enterprse only consders customer as an solatve unt. As the development of nformaton and network technologes, a comple customer network s formed. Accordng to the matr from last subsecton, self-value s epressed by the purchase after sales promotons. Network-value s epressed by the mpact of the purchase to other customer. We must menton that customer network s an unordered network. The mpact s mutual. Whle the customer affects to others, he also get the mpact from others. For eample, before many purchases the customers wll ask for comments from others who have bought the produce or they wll search from webstes. And then they wll make ther own decsons. The same after ther purchases they may gve out ther comments to others va nternet. Wthn ths process, some customers have more self-leadng. They mostly make decsons by ther own nterests. However, there are some others may change ther thought after readng comments. At the same tme, some of the customers would lke to gve ther comments of produces to others, but some others maybe not lke to. So we thnk the network-value of a customer should be referred to the mpact on others. Thus, others attrbutes should be used as the crteron of a customer s network-value. Accordng to ths knd of comple stuatons, tradtonal CR analytcal strategy looks powerless. We must use a new strategy to evaluate customer value. So we subdvde customers based on the matr of customer value. We change the dmensonalty nto self-value and mpact-value. Lke Fg.3.2:

28 Impact-value III Bdrectonal I Dvng IV Consultng II Self-centered Self-value Fg.3.2. atr for subdvson of Customer Value I. We call them Dvng customer. Ths group of customers would lke to search for the comments of a produce before they makng ther purchase decsons. And they may change ther mnd easly accordng to other s mpact. However, they don t lke to gve out ther comments to others. II. We call them Self-centered customer. Ths group of customers has more defnte dea. They usually make purchase decsons by themselves and they are not gong to make others lsten to ther opnon. III. We call them Bdrectonal customer. Ths group of customers would lke to lsten to others opnons and also lke to gve out ther own comments. IV. We call them Consultng customer. Ths group of customers s very sut to do consultng to potental customer. They have a lot defnte deas. In the meanwhle, they would lke to share ther purchase eperences wth others. After ths categorzng, enterprses should be able to dg out the most valuable customers and gve them approprate sales promotons. However, when we check the estent data we fnd out that the most valuable customers are not from the same category. For eample, seemngly the customers from category III should be gve the most sales promotons. Snce they are easly to accept others opnons and they have more opportuntes to persuade others to buy some produces. However, ths s not the truth. Let s take the customers from category I as an nstance. Although they rarely consder others opnon, they may have very hgh self-desre to buy a produce. Ths desre may be

29 much more than the desre of Bdrectonal customer whch s gotten after others mpact. Therefore, we consder the purchase trend of customer as the synthetcal ehbton of customer relatonshp attrbutes. Thus, customer value depends on both self-relance nde wthn purchase behavor and mpact nde after purchase. In ths paper, we call them Attrbutes of Customer Relatonshp ACR. ACR s the key of customer classfcaton. And how to effectvely dg out these ACs s the man work of ths paper. 3.4 Dssmlarty n Cluster Algorthm In ths subsecton we are gong to dscuss about how to evaluate whether a customer trend for hearng hs own dea more. Here we would lke to use the concept of Dssmlarty n Clusterng Algorthm for data mnng, n order to gve out an evaluatng method whch suts to our model. Dssmlarty n Clusterng Algorthm for data mnng s used to represent the smlar degree of two obects. When the attrbutes of represented obects are dfferent, the algorthm for dssmlarty s dfferent as well. In the Clusterng problem doman, for hgh dmensonal sparse data of bnary varables, a dssmlarty measure algorthm named Spare Feature Dssmlarty SFD of a set was put forward [4]. SFD of a set represents the smlar degree of all the obects n a set. Defnton 3.1 SFD of a set: Gven n obects, each obect s descrbed by m attrbutes. m equals 1 or 0. X s a set of obects, n whch the number of obects s denoted as X, the number of attrbutes that equal 1 for all obects s ndcated by a, and the number of attrbutes that equal 1 for some obects and equal 0 for other obects s ndcated by e. SFD of set X, denoted as SFDX s defned as: [18] e X SFD X = a 3.1 SFDX represents the smlarty of the obects n set X. The smaller the SFD s, the more smlar the obects are. Ths feature of SFD totally accord wth the logstc defnton of customer s self-relance nde. The more the dssmlarty s, the more trend of hearng hs own dea the customer has. Thus, we gve out the defnton of self-relance nde based on the concept of dssmlarty. We abstract the behavor of customer based on the purchase record n database as follow:

30 In database each customer s purchase record relates to produce ID. In order to make the follow data processng convenent, we defne each customer as an obect. All the produces wthn database are the attrbutes of ths obect. When the customer buyng a produce, the relatve attrbute of ths customer wll be set as 1, otherwse the attrbute wll be set as 0. We know that ths abstract completely accord wth the defnton of SFD. So we defne self-relance nde SR nde as follows: Defnton 3.2 SR nde: Gven n obects of customers, each obect has m attrbutes for produces, set as 1 or 0. X={ 1, 2 n } s an ordered obect subset, n whch the number of obects s denoted as X. The number of attrbutes, that equal 1 for the frst obect and equal 0 for the net X -1 obects and the attrbutes that equal 0 for the frst obect and equal 1 for the net X -1 obects, s ndcated by e; and the number of attrbutes that equal 1 for the frst obect and the equal 1 for at least one of net X -1 obects s ndcated by a. Thus, dssmlarty of the frst obect n X s defned as: e SFD 1 = X a 3.2 And self-relance nde s defned as SFD 1 SR 1 = 1+ SFD So we can learn from ths defnton: the bgger the SFD s, the bgger the SR nde s. 3.5 Lft n Assocaton Rules By now we have confrmed Self-relance nde for each customer. That means we know how much the customer wll hear others opnon. The followng work s evaluatng how much the customer can mpact others. Now we are gong to defne our evaluatng method based on the concepts from assocaton rules. Here are some concepts from assocaton rules [4]: Defnton 3.3 Confdence: In the transacton set D, transacton T support temset X. There are C% transactons from T also support temset Y. C% s named as Confdence of assocaton rule X { Y. That s, T T DAnd X U Y T } ConfdenceX Y = 3.4 T T DAndX T { }

31 Defnton 3.4 EpectedConfdence: In the transacton set D, e% of transacton T support temset Y. Thus e% s named EpectedConfdence of assocaton rule X Y. That s, { T T DAndY T } EpectedconfdeceX Y = 3.5 D Defnton 3.5 Lft: Lft s the rate of confdence and epectedconfdence. That s, confdence X Y LftX Y = 3.6 epectedconfdence X Y Lft represents how much mpact that the estng of temset X wll affect on the estng of temset Y. When the frst tme we saw ths defnton, we were very ectng. Because ths s ust the evaluatng method what we need to represent our mpact nde of customer. Thus, we gve out our defnton of mpact nde. Defnton 3.6.1: In the database D, customer X ests n the customer purchase record T, and c% of the stuaton ests customer Y as well. c% s called the confdence of X and Y purchases a produce at the same tme. That s the rate of when customer X purchases one produce customer Y also purchases t. { T T DAnd X U Y T} Confdence Y X = 3.7 T T DAndX T { } Defnton 3.6.2: In the database D, e% of the customer purchase record T has customer Y. e% s called as the epectedconfdence of X and Y purchases a produce at the same tme. Epectedconfdence represents the probablty of customer Y purchase one produce wthout any condton. { T T DAndY T} Epectedconfdence Y X = 3.8 D Defnton 3.6: Lft represents the rate of confdence and epectedconfdence. In our model, that s mpact nde. It represents when customer X purchase one produce how much mpact wll beng brng to customer Y. confdence Y X Impact Y X = 3.9 epectedconfdence Y X By then, we already can mne the self-relance nde and mpact nde of a customer. The net task s fndng an approprate model to estmate the value of each customer