Qiang Yang and Hong Cheng

Transcription

1 From: ICAPS-3 Proceedngs. Copyrght 23, AAAI ( All rghts reserved. Plannng for Marketng Campagns Qang Yang and Hong Cheng Department of Computer Scence Hong Kong Unversty of Scence and Technology Clearwater Bay, Kowloon, Hong Kong, Chna (qyang, Abstract In busness marketng, corporatons and nsttutons are nterested n executng a sequence of marketng actons to affect a group of customers. For example, a fnancal nsttuton may derve marketng strateges for turnng ther reluctant customers nto actve ones and a telecommuncatons company may plan actons to stop ther valuable customers from leavng. These marketng plans are amed at convertng groups of customers from an undesrable class to a desrable one. In ths paper, we formulate ths group marketng-plan generaton problem as a plannng problem. We desgn a novel search algorthm to fnd a cost-effectve and hghly probable plan for swtchng a group of customers from ther ntal states to some more desrable fnal states. We explore the tradeoff among tme, space and qualty of computaton n ths plannng framework. We demonstrate the effectveness of the methods through emprcal results. Introducton Marketng campagn plannng n busness marketng can be consdered as a process of plannng n whch the obectve s to convert groups of customers from one class to another, more proftable class. In busness marketng lterature (Dbb et. al 1996), plannng for marketng campagns corresponds to developng acton plans by takng nto account the customer segmentaton, marketng obectves and budgetary constrants nto consderaton. In the marketng practce today, t s a common practce to desgn acton plans by a human experts through focus group studes (Bank Marketng Assocaton 1989). The plannng process tself s both long and laborous. Marketng plannng can be dvded nto two types. Drect marketng or one-to-one marketng plans are amed at generatng plans to target ndvdual customers. Ths s an expensve process that s only appled to valuable customers. Drect marketng usually assumes that the next acton to be performed can be decded based on observaton of a customer s current state. In ths paper we consder a second knd of marketng plans called segmentaton-marketng plans. These plans are amed at Copyrght 23, Amercan Assocaton for Artfcal Intellgence ( All rghts reserved. marketng to multple groups of customers rather than a sngle customer, where a sequence of actons s executed on a segment of chosen customers untl they are completed. Essentally, the plannng process can be consdered as buldng a statstcal model based on past data and usng the model to formulate a plan to be executed on a group of customers. These marketng actons can hardly be formulated as tradtonal classcal plannng representatons. Often marketng plans are expected to be effectve on only a subset of the customers n the target group, and the plannng s currently done by hand through varous marketng studes such as focus groups. A marketng plan thus generated wll be appled to a chosen subset of customers to be effectve. For example, a cell-phone company may decde to reduce the monthly fee for a subgroup of ts customers who are both hghly valuable and lkely to leave the company for ts compettors. To llustrate, we consder the followng scenaro. Suppose that a company s nterested n marketng to a group of 1, customers n the fnancal market to promote a specal loan sgnup. We start wth a customerloan database wth hstorcal customer nformaton on past loan-marketng results n Table 1. Suppose that we are nterested n buldng a 3-step plan to market to the selected group of customers n the new customer lst. There are many canddate plans to consder n order to move as many customers as possble from non-sgnup status to a sgnup one. The sgnup status corresponds to a postve class that we would lke to move the customers to, and the nonsgnng up status corresponds to the ntal state of our customers. Our plan wll choose not only low-cost actons, but also hghly successful actons from the past experence. For example, a canddate plan mght be: Step 1: Send mals; Step 2: Call home #; Step 3: Offer low nterest rate Ths example also ntroduced a number of nterestng aspects for the plannng problem. Frst, not all people n the group of 1, customers should be consdered as canddates for the converson. Some people should not be consdered as part of marketng campagn because they are too costly or nearly mpossble to convert. To dentfy a group of applcable customers, certan data mnng 174 ICAPS 23

2 algorthms for customer segmentaton can be appled. Second, the group marketng problem s to use the same plan for dfferent customers n the ntended customer group, nstead of a dfferent acton plan for each dfferent customer. Ths makes the group marketng dfferent from the drect-marketng problem that some authors have consdered n the data mnng lterature (Domngos and Rchardson 21; Pednault et. al 22; Lng and L 1998; Yang and Cheng 22). For group marketng, we don t have the luxury of observng ntermedate states durng a plan executon n order to decde what to do next. Instead, we must buld an N-step plan ahead of tme, and evaluate the plan accordng to cross-valdaton from the hstorcal records. Thrd, for the customers n the group to be marketed to, there are potentally many possble actons that we can provde. Each acton comes wth an nherent cost assocated wth t. In addton, an acton s not guaranteed to produce ts ntended result. For example, t may be more costly to call a customer at hs home than to send mal. However, sendng a mal to a customer may have less effect than callng a customer at home. Nether malng nor callng can guarantee that all customers contacted wll be converted to sgnup status afterwards. Addtonally, callng a customer may have an adverse effect of annoyng the customer more than necessary. Fnally, t s dffcult to formulate ths problem as a classcal plannng problem, because the precondtons and effects of actons are only mplct n the database, rather than gven ahead of tme by experts n a crsp logcal formulaton. We formulate the above problem as a probablstc plannng problem, where the key ssue s to look for good plans for convertng customer groups. Our approach s to frst dentfy a state space and assgn the potental customers to ntal states. We classfy the customer states nto groups belongng to desrable or undesrable classes. Our obectve becomes one to convert customers from undesrable class to desrable one. We propose an algorthm called MPlan as a soluton to ths plannng problem usng the hstorcal database as an AND-OR tree search problem. The resultng plan wll provde a bass for the fnal marketng plans. Our am s to choose hghutlty actons to be ncluded n our plan where the noton of utlty s ntroduced to ncrease the probablty of success whle reducng costs. Ths research devates from the tradtonal plannng applcatons and formulaton of classcal plannng n several aspects. Compared to classcal plannng, n group marketng we cannot guarantee wth certanty the result of actons; each acton may result n an ntal group to be splt nto several subgroups, each landng n a potental state followng a probablty dstrbuton. Ths dstrbuton can be learned from hstorcal plan traces obtaned before. A second dfference from classcal plannng s that there s no easy way to formulate the actons n terms of relatons and logc formulas needed fro precondtons and effects. The only observable fact before and after an acton s executon s from the hstorcal databases, whch records the customer status n varous attrbutes. By applyng a statstcal classfer to the attrbutes, we could learn a customer s potental standng n terms of desrable versus undesrable classes. The problem s also dfferent from the tradtonal MDP approach (Sutton and Barto 1998) to solvng the probablstc plannng problem. In MDP, t s assumed that at all tme durng a plan s executon, the ntermedate states can be known ether completely or partally. The problem s that of fndng a polcy n whch to drect an agent s acton no matter where the agent s observed to land. The MDP formulaton s more sutable for drect marketng (Lng and L 1998), whch s geared towards fndng a plan for each ndvdual such that an acton s chosen based on the agent s observed resultng state. However, n group marketng, t s often the case that we have no such opportunty to obtan the ntermedate states for a group of customers n the mddle of plan executon. Instead, we have to fnd a sngle plan for a group of smlar customers and to execute ths plan to completon. Thus, ths aspect where a sequence of actons s bult and executed s more akn to classcal plannng. The problem s also dfferent from the probablstc plannng framework of (Draper et. al 1994), whch consdered modelng each acton n a probablstc verson of the pre-condtons and post-condtons. The problem there s stll to consder how to buld a plan from a sngle ntal state to a sngle goal state. In contrast, n our problem the actons logcal representatons are not avalable; all that we can observe are acton labels and ther assocaton wth states. In addton, we consder customer groups that are scattered nto multple ntal states. The goal state (Keeney and Raffa 1976) s not clearly defnable n our case ether, because the postve class n general defnes the potental goal state sets. In data mnng area, a related area s cost-senstve learnng and decson makng (Domngos 1999; Elkan, 21) n the machne learnng communty. However, sgnfcant dfferences reman. Cost-senstve methods try to mnmze the cost of a sngle decson. However, n many applcatons, sequences of decsons n the form of plans are needed. Marketng-Plannng Problem Formulaton We now consder how to formulate the marketng problem more formally as a plannng problem. We frst consder how to buld a state space from a gven set of customer records. As n any machne learnng and data mnng schemes, the nput customer records consst of a set of attrbutes for each customer, along wth a class attrbute that descrbes the customer status. A customer s attrbute may be hs age, ncome, gender, credt wth the bank, and so on. The class attrbute may be Appled, whch s a Boolean ndcatng whether the customer has appled and s approved for loan. As wth any real customer databases, the number of attrbutes may be extremely large; for the KDDCUP-98 data (Blake and Merz 1998), there are a total ICAPS

3 of 481 attrbutes to descrbe each customer. Of the many attrbutes, some may be removed when constructng a state. For convenence, we refer to ths database table as the Customer Table. Table 1 s an example of Customer Table. Table 1. An example customer-loan database. The last attrbute s the class attrbute. Customer Salar y Cars Mortga ge Table 2. An example Marketng-log database. Loan Sgnup? John 8K 3 None Y Mary 4K 1 3K Y Steve 4K 1 None N S# A# S# A# S# A# S# S1 A1 S2 A2 S3 A3 S4 S A S1 A1 S2 A4 S5 S2 A2 S3 A4 S4 A4 S7 S A S3 A4 S4 A4 S7 A second source of nput s the prevous marketngrecord database. Ths s a database that descrbes how the prevous marketng actons have changed each customer s attrbutes as a result of the actons executon. For example, after a customer receves a promotonal mal, the customer s response to the marketng acton s obtaned and recorded. As a result of the malng, the acton count for the customer n ths marketng campagn s ncremented by one, and the customer may have decded to respond by fllng out a general nformaton form and malng t back to the bank. The status of the customer at any nstant of tme s referred to as a state, and state may change as a result of executng an acton. Thus, the hstorcal marketng-record database conssts of state-acton sequences, one for each partcpatng customer. Ths sequence database wll serve as the tranng data for our planner. For convenence, ths hstorcal marketng database table s referred to as the Marketng-log Table. Table 2 s an example of Marketnglog Table. Gven the Customer table and the Marketng-log table, our frst task s to formulate the problem as a plannng problem. In partcular, we wsh to fnd a method to map the customer records n the customer table nto states usng a statstcal classfer. Ths task n tself s not trval because t maps a large attrbute space nto a more concse space. The problem s more complcated when there are mssng values n the database. In ths paper, we wll not delve nto ths ssue, because t nvolves the ssues of data cleanng and data mnng (Han and Chamber 1998). After the state space s obtaned, we wll use a second classfer to classfy the states nto ether desrable or undesrable states based on the tranng data provded n the Customer table. Classfcaton algorthms such as decson tree or Naïve Bayes are possble choces as long as the classfcaton error rate s low enough. Next, the state-acton sequences n the Marketng-log table wll be used for obtanng acton defntons n a state space, such that each acton s represented as a probablstc mappng from a state to a set of states. To make the representaton more realstc, we wll also consder the cost of executng each acton. To summarze, from the two tables we can obtan the followng nformaton: f s ( r ) = s maps a customer record r to a state s. Ths functon s known as the customer-state mappng functon; p c ( s ) s a probablty functon that returns the probablty that state s s n a desrable class. We call ths classfer the state-classfcaton functon; p( sk s, a ) returns the transton probablty that, after executng an acton a n state s, one ends up n state s k. Once the customer records are converted to states and the state transton through actons are learned from the Marketng-log table, the state space can be formulated as an AND-OR graph. In ths graph, there are two types of nodes. A state node represents a state. From each state node, an acton lnks the state node to an outcome node, whch represents the outcome of performng the acton from the state. An outcome node then splts nto multple state nodes accordng to the probablty dstrbuton gven by the p ( sk s, a ) functon. Ths graph essentally s an AND-OR graph, where each state s an OR node, wth the actons that can be performed on the node formng the OR-branches. Each outcome node s an AND node, where the dfferent arcs connectng the outcome node to the state nodes are the AND edges. A fgure llustratng the scenaro s shown n Fgure 1. Gven a set of customers for whom the marketng plan s desgned, we use a customer-state mappng functon to convert the customer records to a set of ntal states whch these customers belong to ntally n the state space. Note that because of the potentally large number of customers nvolved, there could be a set of ntal states correspondng to the customers, nstead of a sngle ntal state as n classcal plannng. These ntal states provde an ntal segmentaton of the customers. In ths settng, we can gve a defnton of the marketng-plan plannng problem. Gven a set of ntal customers, our goal s to fnd a sequence of actons for each ntal state that converts as many of the customers n that state from the undesrable class to the desrable one whle ncurrng mnmal costs. The plan must satsfy some constrants, n one of the followng forms: length constrant: the number of actons must be at most N; 176 ICAPS 23

4 probablty constrant: the expected probablty of beng n a desrable class of all termnal states a plan leads to must be at least Success_Threshold. S3 A2 O3 S1 O1 A1 S2 Fgure1. An example of AND-OR graph. Not all customers n the gven set of customers are convertble to the desrable class. In ths case, we also want to dentfy a subset of customers who can be converted wthn the constrant. The marketng plan generaton problem can be consdered n several forms. A varaton of the problem s to fnd a unform plan for all dfferent customer segments, regardless of whch ntal states they start from, so that as many customers as possble are converted to the desrable class under the length and probablty constrant. Ths formulaton corresponds to the need for corporatons to market to an entre group of customers wth the same actons for consstency and cost-cuttng. In ths paper, we focus on the frst problem where we can have dfferent plans for dfferent segments of customers. Marketng-Campagn Plannng Algorthm Algorthm Overvew A maor dffculty n solvng the marketng-plannng problem stems from the fact that there are potentally many states and many connectons between states. Ths potentally large space can be reduced sgnfcantly by observng that the states and ther connectons are not all equal; some states and acton sequences n ths state-space are more sgnfcant than others because they are more frequently traveled by traces n the Marketng-log table. Ths observaton allows us to use an approach n whch we explot plannng by abstracton. S S7 S4 S5 S6 A2 O4 A2 O2 S8 In partcular, sgnfcant state-acton sequences n the state space can be dscovered through a frequent strngmnng algorthm. We start by defnng a mnmumsupport threshold for fndng the frequent state-acton sequences. Support represents the number of occurrences of a state-acton sequence from the Marketng-log table. More formally, let count(seq) be the number of tmes sequence seq appears n the database for all customers. Then the support for sequence seq s defned as sup( seq ) = count( seq), (1) Then, a strng-mnng algorthm based on movng wndows wll mne the Marketng-log table database to produce state-acton subsequences whose support s no less than a user-defned mnmum-support value. For connecton purpose, we only retaned substrngs both begnnng and endng wth states, n the form of < s, a, s+ 1, a+ 1,..., sn >. Once the frequent sequences are found, we pece together the segments of paths correspondng to the sequences to buld an abstract AND-OR graph n whch we wll search for plans. If < s, a1, s2 > and < s 2, a3, s4 > are two segments found by the strngmnng algorthm, then s, a, s, a s > s a new path < 1 2 3, 4 n the AND-OR graph. Snce each component of the AND-OR graph s guaranteed to be frequent, the AND-OR graph s a hghly concse and representatve state space. Suppose that we wsh to fnd a marketng plan startng from a state s, we consder all acton sequences n the AND- OR graph that start from s satsfyng the length or probablty constrant. We used a functon f ( s, = g( + h( s, to estmate how good a plan s. Let s be an ntal state and p be a plan. Let g( be a functon that sums up the cost of each acton n the plan. Let h ( s, be a heurstc functon estmatng how promsng the plan s for transferrng customers ntally belongng to state s. h( s, s a knd of utlty estmaton of the plan. Ths functon can be determned by users n dfferent specfc applcatons. In our work, we estmated h( s, n the followng manner. We start from an ntal state and follow a plan that leads to several termnal states s, s + 1, s,, s For each of these termnal states, we estmate the state-classfcaton probablty P ( + s ). Each state has a probablty of 1 P ( + s ) to belong to a negatve class. The state requres at least one further acton to proceed to transfer the 1 P ( + s ) who reman negatve, the cost of whch s at least the mnmum of the costs of all actons. We compute heurstc estmaton for termnal states where the plan leads. For an ntermedate state leadng to several states, an expected estmaton s calculated from the heurstc estmaton of ts successve states weghted by the transton probablty p ( s k s, a ). The process starts from termnal states and propagates back to the root, untl reachng the ntal state. Fnally, we obtan the estmaton of h( s, for the ntal state s under the plan p. ICAPS

5 Based on the above heurstc estmaton methods, we can perform a best-frst search n the space of plans untl the termnaton condton s met. The termnaton condtons are determned by the probablty or the length constrants n the problem doman. Search for Plans usng MPlan In the AND-OR graph, we carry out a procedure MPlan Search to perform a best-frst search for plans. We mantan a prorty queue Q by startng wth a sngle-acton plan. Plans are sorted n the prorty queue n terms of the evaluaton functon f ( s,. In each teraton of the algorthm, we select the plan wth mnmum value of f ( s, from the queue. We then estmate how promsng the plan s. That s, we compute the expected state-classfcaton probablty E ( + s ) from back to front n a smlar way as wth h( s, calculaton, startng wth the P ( + s ) of all termnal states the plan leads to and propagatng back to front, weghted by the transton probablty p ( s k s, a ). We compute E ( + s), the expected value of the state-classfcaton probablty of all termnal states. If ths expected value exceeds a predefned threshold Success_Threshold,.e. the probablty constrant, we consder the plan to be good enough and the search process termnates. Otherwse, one more acton s attached to ths plan and the new plans are nserted nto the prorty queue. E ( + s ) s the expected state-classfcaton probablty estmatng how effectve a plan s at transferrng customers from state s. Its calculaton can be defned n the followng recursve way: E ( + s ) = p( sk s, a)* E( + sk ) ; f s s a nontermnal state; or E + s ) = P( + s ) f s s a termnal state. (2) ( We defne Success_Threshold as a lower bound on E ( + s ). We conduct the above search procedure for all ntal states, fndng one plan for each. It s possble that n some AND-OR graphs, we cannot fnd a plan whose E ( + s ) exceeds the Success_Threshold, ether because the AND-OR graph s not good enough or because the Success_Threshold s too hgh. To address ths, we defne a parameter Max_Step whch defnes the maxmum length of a plan,.e. the length constrant. We wll dscard a canddate plan whch s longer than the Max_Step and E ( + s ) value less than the Success_Threshold. Table 3 s the pseudo code of the MPlan Search algorthm. Consder an example of MPlan Search algorthm usng the AND-OR graph n Fgure 1. Suppose that we are lookng for a plan for customers startng at state s. Suppose we have a fnte set of actons and the mnmum cost among these actons s denoted by MnC. Step 1. We nserted two sngle-step plans <A1> and <A2> nto Q wth the evaluaton functon as follows: Table 3. The MPlan Search Algorthm 1. Insert all possble one-acton plans nto Q. 2. Whle (Q not empty) { 3. Get a plan wth mnmum value of f ( s, from Q. 4. Calculate E ( + s) of ths plan. 5. If ( E ( + s) >= Success_Threshold) Return Plan; 6. If (length(plan) > Max_Ste Dscard Plan; 7. Else 7.1 Expand plan by appendng an acton. 7.2 Calculate f ( s, for the new plans and nsert nto Q. 8 } end whle 9 Return plan not found ; f(s, A1)=Cost(A1) + P(S1 S, A1) * (1-P(+ S1)) * MnC + P(S2 S, A1) * (1-P(+ S2)) * MnC f(s, A2)=Cost(A2) + P(S7 S, A2) * (1-P(+ S7)) * MnC + P(S8 S, A2) * (1-P(+ S8)) * MnC Step 2. Suppose <A1> s the plan wth mnmum f ( s, value n Q. Therefore, <A1> s deleted from Q and examned to see whether t s a qualfed plan. E ( + s) =P(S1 S, A1) * P(+ S1) + P(S2 S, A1) * P(+ S2) If E ( + s) s less than Success_Threshold, then <A1> s not a good plan. Thus, actons A1 and A2 are appended to the end of plan <A1> to form two new plans <A1A1> and <A1A2>. These two plans are then nserted nto Q. Because there s no path <A1A1> n the AND-OR graph, we dscard the canddate <A1A1> from Q. The f ( s, value of <A1A2> s: f(s, A1A2)=Cost(A1A2) + P(S1 S, A1) * (P(S3 S1, A2) * (1-P(+ S3)) * MnC + P(S4 S1, A2) * (1-P(+ S4)) * MnC) + P(S2 S, A1) * (P(S5 S2, A2) * (1-P(+ S5)) * MnC + P(S6 S2, A2) * (1-P(+ S6)) * MnC)) Step 3. Now we have plans <A2>, <A1A2> n Q. Suppose <A1A2> s the plan wth mnmum f ( s,. Therefore, <A1A2> s deleted from Q to see whether t s a promsng plan. E ( + s) = P(S1 S, A1) * (P(S3 S1, A2) * P(+ S3) + P(S4 S1, A2) * P(+ S4)) + P(S2 S, A1) * (P(S5 S2, A2) * P(+ S5) + P(S6 S2, A2) * P(+ S6)) If ( s) good plan wth mnmum cost from Q. E + >= Success_Threshold, then <A1A2> s a 178 ICAPS 23

6 Step 4. Return the plan <A1A2>. Stop. Analyss The Marketng-Plan algorthm has two maor components n terms of tme complexty one s state space abstracton by strng mnng algorthm; the other s Mplan, the bestfrst algorthm. In the strng-mnng algorthm, we fnd frequent strngs whch satsfy the predefned mnmum support threshold. Suppose that there are N sequences n the Marketng-log table. The average length of the sequences s K. We scan the sequences wth a fnte wndow of sze W. We need to fnd all the frequent strngs wth length less than or equal to W. For each sequence wth an average length K, tme complexty s O ( K( W + 1) W ( W + 1) / 2). If W << K, then t s O (K). For totally N sequences, t s O (NK). 2 If W s comparable to K, then t s O ( NK ). In the MPlan Search Algorthm, the number of teratons s bounded by the parameter Max_Step. Suppose the number of dfferent actons s A. In the worst case when the algorthm exts wth No plan found, the number of Max _ Step teratons s O ( A ). However, n an average case the plan should complete faster than the worst case. The tme complexty for a sngle teraton s determned by the sze of the state space. In general, t takes more tme to calculate f ( s, and E ( + s ) n a complex state space than n a smple one because the planner has more states and paths to explore. Note that although our proposed state space abstracton method usng strng mnng does not reduce the number of teratons of MPlan algorthm, t saves a lot of tme n explorng n the state space n each teraton because many statstcally trval paths are dscarded before the search process. Expermental Setup Although we are able to obtan Customer data, t has been dffcult to obtan Marketng-log data from real world. To test our deas, we used a smulator to generate the customer-log data accordng to some customer dstrbutons we can specfy. The Customer data are used for tranng a classfer for state-classfcaton functon p c ( s ). The Marketng-log data are used n two ways: (1) Frequent state-acton subsequences are mned from the Marketnglog data to construct a hghly concse and representatve AND-OR graph; (2) Models of transton probablty p( sk s, a ) are estmated from the statstcs of the Marketng-log data. Data set We used the IBM Synthetc Generator ( to generate a Customer dataset wth two classes and nne attrbutes. The postve class has 3, records representng successful customers and negatve has 7, representng unsuccessful ones. Those 7, negatve records are treated as startng ponts for Marketng-log data generaton. We carred out the state abstracton and mappng by feature selecton, only keepng four attrbutes out of nne. Those four attrbutes were converted from contnuous range to dscrete values. The state space has 4 dstnct states. A classfer s traned usng the C4.5 decson tree algorthm (Qunlan 1993) on the Customer dataset. The classfer wll be used later to decde on the class of a state. We generated the Marketng-log data usng another smulator. Each of the 7, negatve records s treated as an ntally faled customer. A trace s then generated for the customer, transformng the customer through ntermedate states to a fnal state. We also defned four types of actons, each of whch has a cost and mpacts on attrbute transtons. We can llustrate the defnton of an acton s mpact on attrbute transtons through an example M 13 = In ths matrx, M 13 s a matrx representng the mpact of acton A 1 on Attrbute three. The matrx s n by n f attrbute has n dfferent values. Suppose attrbute 3 has three dstnct values, 1, 2. The frst row n the matrx means f attrbute 3 takes value, after acton A 1, Attrbute three wll take on the frst value wth 8% probablty, value 1 wth 15% probablty and value 2 wth 5% probablty. After an acton s taken, attrbutes for a customer wll change probablstcally accordng to the defnton of mpact of actons. The Marketng-log data generaton algorthm s shown n Table 4. In ths procedure, we defne termnal* as follows: once a customer changes from the negatve class to the postve class, the state for the frst tme he s classfed as postve s a termnal state. If a customer receved actons for a predefned number of tmes, say, 2 tmes and stll remaned negatve, the 2 th state s the termnal state. Usng ths method, we generated 7, traces for the 7, faled records. Fgure 2 llustrates the dstrbuton of dfferent-length traces. Table 4. The Marketng-log data generaton algorthm. Input: A set of ntally faled states S and a set of actons A wth state-transton matrces. Output: Sequences of trace data preservng temporal order, n the form of s a, s, a,..., s > Algorthm: For each ntally faled state s whle s s not a termnal* state randomly select an acton a; generate the next state s accordng to the mpact of acton a on attrbutes; end whle end for <, n. ICAPS

7 In Fgure 2, the horzontal axs represents the number of actons n a trace. The vertcal axs represents the number of n-actons traces. For example, the frst dot represents that there are about 18, traces that has only one acton before success. We can see that long traces wth more than 6 actons are very rare Fgure2. Dstrbuton of number of traces as a functon of plan length. Test Crtera We evaluated the qualty of the plans va smulaton. Agan, we used the IBM Synthetc Generator to generate 1, customer records that correspond to the faled class. Our goal s to fnd marketng plans to convert them to a successful class. Ths testng process corresponds to the testng phase for a traned model n machne learnng. In ths smulaton, f there s a plan sutable for convertng a customer record, a sequence of actons s carred out on that record. The plan wll then change the customer record probablstcally accordng to mpact of actons on attrbutes. At the end, the classfer s used to decde whether the changed record has turned nto a successful one. We defne a number of quanttes to measure the success of the test results. Let { s1, s2,... sn} be a set of termnal states n a current plan from an ntal state. We wsh to estmate the success probablty of the plan. We defne a quantty -- the expected success probablty E ( + s ) recursvely as equaton (2). As mentoned before, a user-defned Success_Threshold s used as a lower bound on E ( + s ) ; we consder a plan s found successfully for an ntal state only when E ( + s) s no less than the Success_Threshold. We defne the Max_Step as a length constrant, whereby plans longer than ths lmt wll not be examned. Let N be the number of customers who are to be converted through the marketng plans. Those are the customers who belong to the negatve class ntally. Let PlanSet be the set of plans that are found by the MPLan planner for these N customers. Then the Transton Rate s defned as the proporton of people who are transformed to the successful class after the applcaton of the plan. Let M be the number of customers among the N people who belong to the successful class after the plans are appled. Then M Transton Rate =, (3) N Fnally, let L be the number of ntal segments whch corresponds to L ntal states. Let K be the number of ntal states among the L where a marketng plan s successfully found wthn the stated lmts. Then the Plan Rate s defned as: K PlanRate =, (4) L We also measure the CPU tme used to search for the plan. Ths s denoted as Tme. Evaluaton Results Fgure 3 (a) llustrates the Transton Rate, Plan Rate of a plan as a functon of Success_Threshold. Success_Threshold corresponds to a threshold on the expected probablty that the termnal states are consdered belongng to the postve class. Ths parameter determnes how easy t s to fnd a successful plan. When Success_Threshold s low, many states are consdered postve, and thus plans can be easly and quckly found for most of the ntal states n the graph. Thus we can observe from Fgures 3 (a) and (b) that searchng Tme s low and Plan Rate s hgh wth low Success_Threshold. However, because Success_Threshold s low, the plans found don t guarantee hgh probablty of success. So Transton Rate s also low at frst. As Success_Threshold ncreases, so does the Transton Rate and searchng Tme. When Success_Threshold s too hgh, no plan can be found for some ntal states. Therefore, both of the Plan Rate and Transton Rate decrease. The Tme s much hgher because the searchng process doesn t termnate untl all the plans expanded longer than Max_Step. Fgures 4 and 5 llustrate the Transton Rate, Plan Rate and searchng Tme of a plan as a functon of mnmum support MnSupport. MnSupport =N means a sequence has to appear at least N tmes n Marketng-log data to be frequent. An AND-OR graph wth mnmum support MnSupport=1 corresponds to a state space constructed wthout undergong frequent strng mnng procedure. Generally speakng, MnSupport determnes the frequent state-acton sequences mned from Marketng-log data and thus the sze of the AND-OR graph the search space for a plan. When MnSupport s low, the search space s very large and complex; searchng wll then take a lot of tme to complete. As MnSupport gets larger, the search space becomes more and more compressed, and search tme wll be shorter. When MnSupport becomes extremely hgh, the search space may lose many mportant states and transtons, makng plan searchng harder agan. However, the search effcency also depends on other parameters. Success_Threshold s another mportant factor. When Success_Threshold s low, approxmately the same Plan Rate and Transton Rate can be found no matter how large the state space s, as shown n Fgure 4. When Success_Threshold s hgher, plan searchng becomes more dffcult. In a large search space, searchng for a soluton plan takes a lot more tme. In an extremely concse state space, no plans can be found because many states and 18 ICAPS 23

8 transtons are dscarded by state space abstracton, as shown n Fgure 5. Notce that n Fgure 5(a), Transton Rate reaches a maxmum for a state space when MnSupport=1. Ths means that wth approprate frequent strng mnng, plans found n the resulted state space won t lose n terms of effectveness compared wth orgnal state space wthout strng mnng; the feature of strng mnng even provdes better performance. It also saves tme n search space constructon and plan searchng process, as shown n Fgure 5(b). Fgures 6(a) (b) llustrates the Transton Rate, Plan Rate and searchng Tme of a plan as a functon of Max_Step. As we can see from the fgure, ncreasng the length of Max_Step has lttle effect on Transton Rate and Plan Rate. Ths s due to the fact that once a group of customers are converted to a postve class boundary; any further actons wll not mprove ther chance of beng postve. However, Tme ncreases greatly because searchng process often contnues untl all possble plans wthn the length of Max_Step are explored PlanR(%) TransR(%) Fgure 4(a). Plan Rate, Transton Rate vs. MnSupport. Success_Threshold=.5. Max_Step = Tme(s) PlanR(%) TransR(%) Fgure 4(b). CPU Tme vs. MnSupport. Success_Threshold=.5. Max_Step = Fgure 3(a). Plan Rate, Transton Rate vs. Success_Threshold. MnSupport = 1, Max_Step = PlanR(%) TransR(%) Tme(s).4 Fgure 3(b). CPU Tme vs. Success_Threshold. MnSupport = 1, Max_Step = Fgure 5(a). Plan Rate, Transton Rate vs. MnSupport. Success_Threshold=.2. Max_Step = Tme(s) Fgure 5(b). CPU Tme vs. MnSupport. Success_Threshold=.2. Max_Step =5. ICAPS

9 PlanR(%) TransR(%) Acknowledgments We thank Professors Charles Lng, D.Y. Yeung and Nevn Zhang for ther valuable comments on ths work. Ths work s supported by Hong Kong Research Grant Commttee and SSRI grants Fgure 6(a). Plan Rate, Transton Rate vs. Max_Step. MnSupport=1, Success_Threshold= Tme(s) Fgure 6(b). CPU Tme vs. Max_Step. MnSupport=1, Success_Threshold=.5. Summary of the Test Results In ths test we used sequence mnng as a flter before state space constructon. We observe that usng frequent strng mnng can ndeed save a lot of searchng tme whle at the same tme, provde much more desrable plans n terms of ther performance. Increasng the Success_Threshold can brng about much more promsng plans. Increasng Max_Step does not brng any sgnfcant mprovement n plan searchng when ths parameter s over a certan threshold. Conclusons and Future Work In ths paper, we explored plannng n the marketng plannng doman, where the problem formulaton s sgnfcantly dfferent from the classcal or probablstc plannng stuatons. Our approach combnes both data mnng and plannng n order to buld an abstracton space n whch the plans are obtaned. The plans are no longer transformng an agent s state from one ntal state to a goal state; nstead, n our stuaton we formulate plans that transform groups of customers from a set of ntal states to postve class states. Ths formulaton has many realstc applcatons n the real world, well beyond marketng plannng. In the future, we wsh to consder dfferent varatons of the problem n marketng and other related domans. We wsh to obtan some more realstc data from the customer relatonshp management doman and buld a realstc system for marketng plannng. References S. Dbb, L. Smkn, and J. Bradley The Marketng Plannng Workbook. Routledge. Bank Marketng Assocaton, Buldng a Fnancal Servces Plan: Workng Plans for Product and Segment Marketng. Bank Marketng Assocaton, Fnancal Sourcebooks, Napervlle, Illnos. C. L. Blake and C.J. Merz UCI Repostory of machne learnng databases Irvne, CA: Unversty of Calforna, Department of Informaton and Computer Scence. P. Domngos, MetaCost: A general method for makng classfers cost senstve. In Proceedngs of the Ffth Internatonal Conference on Knowledge Dscovery and Data Mnng, pages ACM Press. P. Domngos and M. Rchardson, 21. Mnng the Network Value of Customers. In Proceedngs of the Seventh Internatonal Conference on Knowledge Dscovery and Data Mnng, pages San Francsco, CA: ACM Press. D. Draper, S. Hanks, and D. Weld, Probablstc plannng wth nformaton gatherng and contngent executon. In Proceedngs of the Second Internatonal Conference on A.I. Plannng Systems. C. Elkan, 21. The foundatons of cost-senstve learnng. In Proceedngs of the Seventeenth Internatonal Jont Conference on Artfcal Intellgence, pages R. L. Keeney and H. Raffa, Decsons wth Multple Obectves: Preferences and Value Trade-offs. Wley, New York. C. X. Lng and C. L Data mnng for drect marketng: Problems and solutons. In Proceedngs of the 4 th Internatonal Conference on Knowledge Dscovery and Data Mnng (KDD 98), pages 73-79, New York. E. Pednault, N. Abe and B. Zadrozny. 22. Sequental Cost-Senstve Decson Makng wth Renforcement Learnng. In Proceedngs of the 8 th ACM Internatonal Conference on Knowledge Dscovery and Data Mnng (KDD 2), pages Edmonton, Canada. 182 ICAPS 23

10 J. R. Qunlan C4.5: Programs for Machne Learnng. Morgan Kaufmann Publshers, Inc., San Mateo, CA. R. Sutton and A. Barto, Renforcement Learnng: An Introducton. MIT Press, Cambrdge, MA. Q. Yang and H. Cheng, 22. Mnng Case Bases for Acton Recommendaton. In Proceedngs of 22 IEEE Internatonal Conference on Data Mnng (ICDM 2). Maebash, Japan. ICAPS