A MULTI-OBJECTIVE OPTIMIZATION APPROACH USING THE RFM MODEL IN DIRECT MARKETING

Transcription

1 A MULTI-OBJECTIVE OPTIMIZATION APPROACH USING THE RFM MODEL IN DIRECT MARKETING By Arben Asllan, Ph.D. Marvn E. Whte Professor of Busness Analytcs Department of Management, #6056 (423) and Dane Halstead, Ph.D. Mary Harrs Dstngushed Professor of Entrepreneurshp Department of Maretng and Entrepreneurshp, #6156 (423) The Unversty of Tennessee at Chattanooga* College of Busness 615 McCalle Avenue Chattanooga, TN FAX: (423) Submtted to Academy of Maretng Studes Journal Internet Dvson, Las Vegas Conference Accelerated Journal Revew September 18, 2014 *Address all correspondence to Dane Halstead, Ph.D.

2 A MULTI-OBJECTIVE OPTIMIZATION APPROACH USING THE RFM MODEL IN DIRECT MARKETING ABSTRACT Gven the vast amount of data generated by customers onlne and offlne purchases, many organzatons today are turnng to data analytcs to help desgn ther drect maretng campagns and ntroduce personalzed promotons for customers. Data analytcs allows companes to mplement more effectve maret segmentaton strateges, customze promotonal offers, allocate maretng resources effcently, and mprove customer relatonshp management. The mplementaton of such strateges s often hampered by lmted budgets and the everchangng prortes and goals of maretng campagns. Ths paper suggests and demonstrates the use of a goal programmng approach to determne whch customer segments should be targeted to acheve proft maxmzaton gven varous prortes and budget constrants for a hypothetcal drect maretng campagn. Usng hstorcal data, the proposed model dentfes customer segments based on the classc RFM model.e., recency, frequency, and monetary value profles. Then, consderng dfferent maretng prortes, the goal programmng model helps dentfy the profle segments most worthy of pursut. Real maretng data are used to llustrate the proposed approach. Keywords: Mult-Objectve Programmng, RFM, Drect Maretng, Data Analytcs 2

3 A MULTI-OBJECTIVE OPTIMIZATION APPROACH USING THE RFM MODEL IN DIRECT MARKETING INTRODUCTION Drect maretng s all about customer data: ther characterstcs, ther buyng habts, and ther buyng potental. Data s obtaned from many sources, ncludng nternally generated data, publc databases, and thrd party lst vendors. The wdespread use of data analytcs by many drect maretng frms allows them to use ths customer data to fne-tune ther maretng strateges wth precson and accuracy. Data analytcs nvolves the strategc and extensve use of data and quanttatve analyss to mprove busness decson mang (Davenport and Harrs, 2007, 2010). Customer data and data analytcs are especally mportant n drect maretng because they are used to help frms mprove response rates, converson rates, and campagn proftablty (Davenport and Harrs, 2007; Dyer, 2003; Hambleton, 2013). One partcular analytcal tool used frequently n drect maretng s the RFM model. The recency-frequency-monetary value (RFM) framewor leads to hghly effectve drect maretng campagns by enablng companes to categorze customers nto homogenous segments based on ther prevous purchasng behavor and then desgn hghly customzed promotonal campagns to reach those customers. Accordng to ths approach, customer data on the recency of purchase (R), frequency of purchase (F), and monetary value of purchase (M) are captured and stored for each customer. Then, customers wth smlar values are grouped together, and targeted promotonal offers are created to reach them. For example, f a gven customer segment shows a low value for recency and relatvely hgh values for frequency and monetary value, these customers are typcally approached wth a we want you bac maretng strategy. If a gven customer segment shows a low monetary value and hgh values for frequency and recency, a more relevant up-sellng maretng strategy could be desgned to generate addtonal sales revenue. The RFM model typcally assumes unlmted maretng resources, however, and suggests that a company can reach all ts customers, even customers wth less than optmal RFM scores. Clearly, most organzatons operate under yearly budget constrants, and therefore such assumptons are mpractcal. Addng optmzaton to the well-nown RFM approach to help allocate resources most effectvely was recommended by Fader et al. (2005b) as an mportant next step for future research. In addton, the mportance of the R, F, and M components n the RFM approach for a gven maretng campagn mght not be the same. For example, a company tryng to mprove ts customer retenton rate mght be nterested prmarly n recency,.e., prortzng the return of lost customers who may have defected to the competton. For the same campagn, frequency and monetary values mght be second and thrd prortes, respectvely. When confronted wth both spendng lmts and dfferng goals, maretng managers should allocate maretng resources toward those customers wth the greatest long-term proft potental. Ths research proposes a mult-objectve optmzaton methodology based on a goal programmng (GP) approach to proft maxmzaton for drect mareters usng RFM data. One unque characterstc of ths (GP) model s the ncluson of varyng drect maretng objectves as well as correspondng budget constrants. In addton to balancng maretng prortes wth maretng budgets, companes must also strve to acheve a balance between two types of errors for any gven campagn: Type I and Type II. A Type I error would occur when organzatons gnore customers (mstaenly) who 3

4 could have returned and repurchased, thereby provdng the frm wth addtonal revenue and proft. Type II errors occur when companes (unnowngly) target customers wth ther maretng campagns who are not ready to purchase (Venatesan & Kumar, 2004). The model proposed n ths research creates a balance between a Type I and a Type II error by dentfyng the proper RFM segments to target. It also dentfes the RFM segments whch should not be pursued because they are: a) not proftable; b) do not algn wth maretng prortes; or c) stran the maretng budget. That s, the model can help drect maretng frms maxmze proftablty by determnng whether they should contnue spendng on (or curtal ther relatonshps wth) gven RFM customer segments. A unque contrbuton of ths research s that RFM data are ncorporated nto a GP approach that ncludes both maretng goals and budgets to determne the most proftable customer segments to target. The research paper s organzed n the followng manner. Frst, a bref overvew of data analytcs n drect maretng s provded, along wth the RFM framewor. The next secton dscusses the GP formulaton to customer proftablty utlzng RFM data and provdes the GP mathematcal formulaton of the model. Varatons of the model are shown through the use of purchasng data from a CDNOW dataset 1 contanng almost 7,000 records. Research conclusons are then presented, and mplcatons of the goal programmng approach to proft maxmzaton are dscussed DIRECT MARKETING AND DATA ANALYTICS Overvew of Data Analytcs Usng data to mae decsons s crtcal to superor busness performance. Yet, another 2.5 quntllon bytes are added to the data unverse every day (Edala, 2012). Ths ncludes over 350 bllon corporate emals, 400 mllon tweets, and one bllon Faceboo posts (Hambelton, 2013). The era of bg data s here. Despte vast quanttes of data, however, a survey of 254 U.S. busness managers found that 40 percent of major busness decsons are made accordng to managers gut or ntuton, not on the bass of fact (Accenture, 2008). Data analytcs refers to the strategc and extensve use of data, quanttatve analyss, and explanatory and predctve models to mae better decsons and tae rght actons (Davenport and Harrs, 2007, 2010). Stated another way, data analytcs refers to the use of analyss, data, and systematc reasonng to mae decsons (Davenport et al., 2010). It s consdered a subset of busness ntellgence whch s the set of technologes and processes that use data to understand and analyze busness performance (Davenport and Harrs, 2007, p. 7). Busness ntellgence ncludes data access and reportng as well as data analytcs. The crtcal pont s that data alone s nsuffcent. The true value of data analytcs s the analyss of that data to mprove busness decsons and the subsequent actons an organzaton taes as a result of that analyss. Used properly, data analytcs can help frms antcpate and respond qucly to changes n the maretplace, mprove ther compettve standng, and acheve mportant goals such as proft maxmzaton (Frans, 2012). More specfcally, t can help frms optmze prces (Advertsng Age, 2013), reduce costs, mprove effcency, manage rs, and n the long run, dramatcally mprove a company s decson-mang process and outcomes (Davenport et al., 2010). Data Analytcs and Drect Maretng Drect maretng frms collect huge quanttes of customer data such as contact nformaton, demographcs, geographc data, lfestyle data, fnancal data, purchase hstory, 1 Source: 4

5 preferences, meda usage, and more. Today s dgtal world has opened up new maretng channels to drect mareters (e.g., socal, moble, emal, and locaton-based maretng), but that also means more data comng from more sources nternal and external, onlne and off-lne. Yet ntegratng customer data from across maretng channels s the number one challenge for customer ntellgence professonals (Srdharan, Franland and Smth, 2011). Even wth enterprse resource plannng (ERP) systems to help ntegrate data across busness functons, companes stll need to access and analyze data from a varety of systems to mae better decsons (Davenport et al., 2010,). Thus, successful drect maretng requres a substantal nvestment n bg data and data analytcs. In fact, mareters external costs of data ntellgence and software n the U.S. were around $60 bllon n 2011 (Brner, 2012). Notably, ths does not nclude n-house expenses of maretng ntellgence such as IT departments, data analysts, or CIOs. Bg customer data and data analytcs are especally mportant to drect mareters because they help ncrease response rates, converson rates, total sales, and the ROI of maretng campagns (Davenport and Harrs, 2007; Dyer, 2003; Hambleton, 2013). And when data from loyalty programs s mned, analytcs can be used to ncrease customer loyalty and retenton (Hambleton, 2013; Srdharan et al., 2012). To do so, however, drect mareters need flexblty when desgnng promotonal campagns. Flexblty n campagn management allows for more targeted, specfc, customzed, and personalzed maretng offers, all of whch lead to hgher response rates (Frans, 2012). The ablty to customze offers and messages depends on havng customer data that s accurate, accessble, tmely, relevant, and fully ntegrated wth other maretng and operatonal data. Data analytcs can then help the creaton of many dfferent maretng campagns, utlzng varables such as customers demographc characterstcs, credt scores, or prevous purchases (Martnez, 2011). Campagn results are collected and stored, then used to fuel the next analyss and the next customzed maretng campagn. To enable such customzaton, drect maretng managers need customer data to create sets of potental buyers,.e., to generate a lst for ts promotons. One way to generate a customer lst s to use a scorng model. Scorng models ran customers accordng to a set of predetermned crtera, assgn a score to each customer, and then group customers wth the same or smlar scores so as to send them a specfc type of promoton. Some scorng models are qute smple; others nvolve complex statstcal analyss. A well-nown and popular scorng model used n drect maretng s the RFM model. Drect Maretng and the RFM Model As noted earler, usng RFM nvolves choosng customers based on when they last purchased (recency), how often they purchased (frequency), and how much they spent (monetary value) on past purchases (Blattberg et al., 2009; Fader et al., 2005a; Rhee & McIntyre, 2009). The RFM crtera are used frequently because, as measures of customers pror behavor, they are ey predctors of ther future purchase behavor (Berry and Lnoff, 2004; Bolton, 1998; Fader et al., 2005b; Malthouse and Blattberg, 2005; Srdharan et al., 2012). Many frms consder recency especally mportant because a long perod of purchase nactvty can be a sgnal that a customer has permanently ended hs/her relatonshp wth the frm (Dwyer, 1989). Accordngly, many companes wll assgn maxmum value to recency, wth lesser mportance attached to monetary value and frequency (Renartz & Kumar, 2000; Venatesan et al., 2007). Regardng the monetary value of customer purchases, sometmes the average purchase amount per customer transacton s used rather than a total (e.g., Fader et al., 2005b). Customers are then categorzed by ther RFM probabltes to ndcate ther proftablty 5

6 potental. They are subsequently selected (or not selected) for the next drect maretng campagn based on ths proft profle. Thus, RFM analyss helps gude maretng resource allocaton n a way that maxmzes proftablty (Venatesan et al., 2007). The RFM model has been used for many years as an analytcal technque, even though more sophstcated methods have been developed recently. It has the advantage of smplcty (McCarty & Hasta, 2007), and many data mnng algorthms are based on the RFM framewor. The research descrbed here combnes RFM data wth maretng budget constrants, and then uses a goal programmng approach to evaluate a drect maretng campagn. The analytc model can be used to gude maretng spendng vs-à-vs varous customer segments,.e., ether contnue nvestng n or scalng bac nvestments n any gven RFM segment. A novel characterstc of ths approach s the combnaton of maretng prortes and preferences for gven customer segments whle recognzng the realty of annual spendng lmts on drect maretng programs. In addton, n the complex area of data analytcs, the RFM framewor offers even small frms wth lmted resources the opportunty to use data analytcs farly easly and capably. Another contrbuton of ths research s that RFM data s ncorporated nto a GP approach nto a sngle model for all customers who are potental targets of a drect maretng campagn. A prevous approach (e.g., Bhasar et al., 2009) utlzed mathematcal programmng (MP) and RFM analyss n a study of personalzed promotons for multplex customers n a customer loyalty program, ncorporatng busness constrants. However, the algorthm n the Bhasar et al. research separated RFM analyss from mathematcal programmng. RFM was used for nonrecent customers, and MP was used for current customers. Ths research ncorporates everythng nto a sngle model. GOAL PROGRAMMING FORMULATION The GP Approach Goal programmng s a mult-objectve mathematcal programmng approach n whch there are a number of objectves, and some of them are treated as constrants nstead of objectves. When developng a specfc drect maretng campagn, managers must determne ther cutoff ponts for recency (R), frequency (F), and monetary values (M) wth the goal of maxmzng customer proftablty wthn a lmted budget. If a manager s not concerned about F and M, then a smple lnear program to determne the cutoff pont for R can be generated. Ths soluton wll generate a maxmum proftablty of, let s say VR. Smlar calculatons show that the maxmum proft for the cutoff value of F s VF, and the maxmum proft for the M cutoff pont s VM. The modeler could tae each of the values VR, VF, and VM as maretng goals and try to fnd a soluton that comes closest to all of the goals. Snce t may not be possble to reach all goals smultaneously, the modeler should create a set of penaltes for not reachng each goal. Ths penalty would depend on the mportance of reachng a partcular segment. If the modeler values R more than F, and then F more than M, the penaltes could be P1, P2, and P3 respectvely, where P1>P2>P3>0. The modeler then creates a new set of varables s1, s2, and s3. The problem can then be formulated as: Mnmze Z = P1s1 + P2s2 + P3s3 subject to: {objectve functon of the R model} + s1 = VR {objectve functon of the F model} + s2 = VF {objectve functon of the M model} + s3 = VM + all constrants n the orgnal LPs (ncludng budget constrants) 6

7 In order to llustrate the GP model, a sample of a CDNOW dataset, as used n Fader et al. (2005a), s utlzed. The sample conssts of hstorcal buyng data for 2,357 customers. It contans 6,696 records. Each ndvdual record contans a customer ID, a transacton date, and a dollar value for each transacton. Ths data set was prevously used to show how Excel could be employed to automate calculaton processes when groupng customers nto varous RFM segments (Fader et al., 2005a). Notatons Used for the Optmzaton Models = ndex used to dentfy the group of customers n a gven recency category; j = 1 5 ndex used to dentfy the group of customers n a gven frequency category; = 1 5 ndex used to dentfy the group of customers n a gven monetary category; V = expected revenue from a returned customer; p = probablty that a customer of recency maes a purchase; pj = probablty that a customer of frequency j maes a purchase; p = probablty that a customer of monetary group maes a purchase; N = number of customers who are presently n recency ; Nj = number of customers who are presently n frequency j; N = number of customers who are presently n monetary group ; C = average cost to reach a customer durng the drect maretng campagn; B = budget avalable for the drect maretng campagn. Model Formulaton for the Recency Case Let the decson varable for ths case be a 0-1 unnown varable as follows: x = 1 f customers n recency are reached through the drect maretng campagn; 0, otherwse. Usng the above notatons, a 0-1 mxed nteger GP formulaton s presented: Maxmze: subject to: Z R r 1 R 1 N Cx N ( p V C) x B x 0,1 = 1 R (3) Equaton (1) s the objectve functon. It maxmzes the expected proft (Zr) of the drect maretng campagn. As noted earler, a customer n a state of recency has a p chance of purchasng and a (1- p) chance of not purchasng. The proft from a customer who purchases s calculated as (V - C). When a customer does not purchase, the expected proft s smply (-C). Therefore, the expected value of the proft from a sngle customer n state s: p ( V C) (1 p )( C) (4) Ths can be smplfed to: p V C (5) Snce there are N customers n the recency, the expected proft from ths group of customers s: (6) Thus, (1) ndcates the sum of profts for all groups of customers for whch a maretng decson to advertse to them (x=1) s made. Equaton (2) assures that the avalable budget for the campagn (B) s not exceeded. The actual cost of the maretng campagn s represented on N ( p V C) (1) (2) 7

8 the left sde of the equaton, whch s calculated as the sum of campagn costs for each group of customers. Equaton (3) represents the bnary constrants for the decson varables x. Solvng the Model for the Recency Case The model s appled as follows. Customers are frst placed nto fve groups n whch group one represents those customers wth the least recent purchases, and group fve conssts of those customers who have purchased most recently. Then, the total number of customers belongng to each group can be determned usng a pvot table. Pvot tables can also be used to calculate the probablty (p) that a customer n group wll mae a purchase. Appendx A shows that, gven a campagn budget of B= $12,500, a cost to reach a customer of C= $7.50, and the average revenue from the purchasng customer of V= $35, the company should only select customers of recency 3, 4, and 5 for future promotonal efforts. Ths soluton wll generate a total proft of $24,851 (see Appendx A). Model Formulaton for the Frequency Case In ths secton, frequency s consdered as a dmenson n our 0-1 GP model. Agan, the goal s to stay wthn the maretng budget constrants whle maxmzng the profts from potental customer purchases. Let the decson varable for ths case be a 0-1 unnown varable as follows: xj = 1 f customers n frequency j are reached n the promotonal campagn; 0 otherwse. The 0-1 mxed nteger GP formulaton s presented for the Frequency Case: Maxmze: subject to: Z F f j1 x j N j F j1 Cx 0,1 N ( p j j B j jv C) x j (7) (8) j=1 F (9) The objectve functon whch maxmzes the expected proft (Zf) of the maretng campagn s shown n Equaton (7). Equaton (8) assures that the avalable maretng budget B for ths campagn s not exceeded. The left sde of the equaton represents the actual cost of the campagn, whch s calculated as the sum of campagn costs for each group of customers. Equaton (9) represents the bnary constrants for the decson varables xj. Solvng the Model for the Frequency Case Ths case s, of course, applcable to frms where frequency and recency are the only sgnfcant values n ther maretng campagns. In these cases, customers are organzed frst nto fve groups. Each group Gj contans customers who belong to frequency value j (1, 2, 5). Le the prevous example, pvot tables can be used to calculate the probablty of purchase (pj) by a customer n group j. The results ndcate that customers n the frequency 3, 4, and 5 must be reached. Ths soluton wll generate a total proft of $41,876 (see Appendx B). Model Formulaton for the Monetary Value Case In ths secton, the model consders monetary value. As n the prevous cases, the objectve remans the same: maxmze profts from potental customer purchases whle stayng wth the annual budget constrant. Let the decson varable for ths case be a 0-1 unnown varable as follows: = 1 f customers n monetary group are reached; x 8

9 0, otherwse. Maxmze: subject to: Z x m M 1 M 1 N 0,1 N ( p V C) x Cx B =1 M (12) Equaton (10) s the objectve functon for the model whch maxmzes the expected proft (Zm) of the maretng campagn. As stated earler, a customer n a state monetary has a p chance of purchasng and a (1- p) chance of not purchasng. Equaton (11) assures that the avalable budget for the campagn (B) s not exceeded. The left sde of Equaton (11) represents the campagn s actual cost, whch s calculated as the sum of campagn costs for each group of customers. Equaton (12) represents the bnary constrants for the decson varables x. Solvng the Model for the Monetary Value Case Appendx C provdes a summary of the optmal soluton for the monetary model. Ths fgure shows the proftable segments for the frm. The results ndcate that any future drect maretng campagn must exclude the customer segments wth monetary values of M=1, M=2, and M=3 as they are clearly unproftable. Ths soluton wll generate a total proft of $51,858 (see Appendx C). Incorporatng Prortes nto the Model The above three models ndcate that M s the most mportant varable of the RFM framewor as the total proft generated s the hghest at $51,858. However, the maretng department s nterested n nvestgatng the mpact of settng the followng prortes: Prorty 1 (P1 = 200): Recency Prorty 2 (P2 = 100): Frequency Prorty 3 (P3 = 50): Monetary Value The followng s the GP formulaton whch mnmzes the penaltes of not reachng the maretng goals. Mnmze Z = 200s s2 + 50s3 (13) subject to: R 1 N ( pv C) x + s1 = VR (14) (10) (11) F j1 N ( p jv j j C) x j + s2 = VF (15) M 1 N ( p V C) x + s3 = VM (16) R N Cx F N Cx f f 1 f 1 1 M N Cx B (17) 9

10 x x f x 0,1 0,1 0,1 = 1 R (18) f = 1 F (19) = 1 M (20) In the above formulaton, (13) represents the objectve functon. Mnmzaton of s1 has prorty over mnmzaton of s2 snce s1 has a larger contrbuton coeffcent (200>100). Smlarly, mnmzng s3 has the lowest prorty. (14), (15), and (16) represent the new set of constrants added to the model to ensure that prevous achevement of proft goals from each respectve model (VR= $24,851, VF= $41,876, and VM= $51,858) stll need to be acheved. (17) assures that the overall budget (B=$12,500) s not exceeded. Fnally, (18), (19), and (20) ensure bnary soluton values for the decson varables. Solvng the Overall Model Appendx D shows the optmal soluton to the goal programmng approach. As seen, the total proft for the soluton s $42,274, and the soluton suggests that the drect maretng campagn must reach customers wth a recency value of 5 and frequency values of 4 and 5. Because prorty was gven prmarly to recency, then to frequency, wth the lowest prorty gven to monetary value, the soluton suggests no promotonal offers should be based on monetary value. SUMMARY OF RESULTS The optmal solutons for four varatons of the RFM model proposed here are provded n the data analyss and ullustrated n Appendces A-D: a recency model, a frequency model, a monetary value model, and a full RFM model. The Excel templates for each model are avalable upon request by contactng the frst author. The optmal soluton for the recency model suggests that only customers wth recency values of 3, 4, and 5 should be targeted for future promotonal efforts. Ths soluton wll generate a total proft of $24,851. In the frequency model, the results ndcate that any future maretng campagn should be focused on those customers wth frequency values of 3, 4, and 5. Ths soluton generates a proft of $41,876. The results for the monetary value model show that addtonal maretng resources should not be allocated toward the customer segments wth monetary values of M=1, M=2, and M=3 as they are clearly unproftable. That s, these segments should not be targeted n a future drect maretng campagn. The monetary value soluton wll generate a total proft of $51,858. The optmal soluton to the goal programmng approach ndcates that only customers wth a recency value of 5 and frequency values of 4 and 5 should be selected by the frm for future promotonal efforts,.e., addtonal maretng nvestment should be made. Customers wth recency values of 1, 2, 3, and 4, as well as customers wth frequency values of 1, 2, and 3, would be excluded as targets of future campagns. The total proft for the goal programmng soluton s $42,274. No prorty should be placed on the monetary value data; therefore no dfferental maretng acton should be based on monetary value. Excludng certan customer segments from drect maretng efforts should provde managers wth greater ROI for a gven maretng nvestment as greater resources wll be avalable to spend on the most lucratve segments. CONCLUSIONS AND DISCUSSION Pressure to maxmze maretng return on nvestment s ncreasng, and chef maretng offcers (CMOs) everywhere have been forced to reduce budgets n recent years (e.g., Wong, 2009). At the same tme, the drect maretng ndustry s currently outpacng the overall 10

11 economy (DMA, 2013), representng almost 53 percent of all U.S. advertsng expendtures n 2012, spendng over $168 bllon (accountng for 8.7 percent of GDP) and generatng a ROI of over $12 for every dollar spent (Drect Maretng Assocaton, 2012). The top fve drect maretng agences earned over $3.5 bllon n 2011, and that represented only ther U.S. revenue. Thus, drect maretng contnues to play an effectve and growng role n the overall maretng arsenal of many organzatons. As CMOs are ncreasngly forced to acheve superor results wth nferor budgets, analyzng maretng data and prortzng maretng spendng become even more crucal. Low response rates n drect maretng mae budget constrants an even greater challenge for the drect response frm (e.g., 1-4 percent average response for drect mal to outbound telemaretng). Investng scarce resources on customers who are not yet wllng to buy (a Type II error) s not only neffcent, but could represent a possble threat to a frm s long-term fnancal vablty (Ferrante, 2009; Venatesan & Kumar, 2004). The mult-objectve optmzaton approach used n ths research acheves a balance between Type I (mssng proftable customers) and Type II errors. It helps dentfy both approprate and napproprate RFM segments based on three core characterstcs: proftablty, maretng objectves, and budget constrants. By fndng the most proftable customer segments (gven varous maretng objectves and spendng lmts), a GP approach appled to RFM data can provde a frm wth optmal solutons to and flexblty n maretng spendng decsons n a sngle model. Dependng upon a gven RFM segment s proft potental, a maretng frm can determne whether to contnue targetng that segment n efforts to generate even more sales, or whether t should spend ts scarce resources on alternatve (.e., more proftable) groups. Ths research can therefore be used as a type of scorng model for practtoners to enable the transformaton of purchasng hstory data,.e., RFM data, nto a useful decson model whch can be appled to many maretng stuatons and to any mposed budget lmtaton. Because ths research factors n budget constrants and dfferent maretng prortes, the decson model demonstrated here has consderable long-term utlty for maxmzng the proftablty of customer segments. Ths study has lmtatons, but these can provde avenues for future research n the area. For example, because RFM framewors represent hstorcal behavor, ther ablty to accurately capture and predct future behavor and proft potental has been questoned (Blattberg et al., 2009; Rhee & McIntyre, 2009). Whle predctng any consumer behavor, usng any type of model, s nherently uncertan (and ths GP model s no excepton), accuracy s always a potental lmtaton when forecastng s based on hstorcal data. As the current model addresses only a sx month tme perod, and Venatesan et al. (2007) argue that up to three years s consdered an acceptable horzon for estmates n customer selecton models, ths may perhaps mtgate forecastng accuracy concerns. In other words, the shorter the tme horzon consdered, the less varaton there s lely to be between past and future purchasng behavor (.e., there s less tme and opportunty for ntervenng exogenous varables to dsrupt behavoral patterns). As noted by Davenport et al. (2010, p. 159), however, a company must stll constantly revew and manage ts analytcal models, be alert to model decay, montor relevant external events, and eep trac of all competng models. Ideally, frms wll eventually ntegrate addtonal customer data wth RFM data as RFM focuses on customer purchasng behavor, not necessarly customer search behavor. That s, t doesn t consder the value of customer nformaton when no purchase s made. Wth respect to future data collecton, drect maretng managers should consder capturng web browsng data 11

12 as well as transactonal data, e.g., X percent of customers clcng on Ln Y ultmately vsted Ste Z and purchased Brand A. Ths helps dentfy customers search behavors, choce crtera, and decson-mang paths, all of whch help us understand customer behavor better, and therefore predct t more accurately. One European retaler dentfed products that customers browsed on the company webste but dd not purchase. Follow up emals were then sent to customers wth personalzed messages that encouraged purchase and ncluded promotonal offers for products vewed but not bought (Frans, 2012, p. 17). In addton to webste browsng, other customer contact ponts can be valuable as well. For example, customer emals, socal networng messages (e.g., Faceboo les ), and customer phone calls can all ndcate customer nterest and propensty to buy n the future, thus generatng sales and profts for the frm. At the very least, these data could provde a greater understandng of customer behavor whch can lead to more effectve maretng offers and messages. Data analytcs s a future goal that does not represent present realty for many U.S. frms (Accenture, 2008). Yet sound manageral decson-mang reles on effectve data analytcs. The value of any customer data s n how t s analyzed and then used to nform managers and help them mae better busness decsons (Frans, 2012). The GP approach used n ths RFM analyss offers several advantages to drect mareters. It s smple, easy to use, and can account for a large number of varables, constrants, and objectves. REFERENCES Accenture. (2008) Most U.S. Companes Say Busness Analytcs Stll Future Goal, Not Present Realty. Accenture press release, December 11, retreved on August 12, Advertsng Age. (2013) Retalers Embrace Data Tools for Rapd-Fre Prce Changes after Bg Holday Season Push, Trend s Here to Stay. January 24, 2013, accessed on January 24, Berger, P. D. and Nasr, N.I. (1998). Customer Lfetme Value: Maretng Models and Applcatons. Journal of Interactve Maretng. 12(Wnter), Berry, M.J.A. and Lnoff, G. (2004). Data Mnng Technques, 2ed. Indanapols, IN: John Wley & Sons. Bhasar, T., Subramanan, G., Bal, D., Moorthy, A., Saha, A. and Rajagopalan, S. (2009) An Optmzaton Model for Personalzed Promotons n Multplexes. Journal of Promoton Management. 15(1/2), Blattberg, R.C., Malthouse, E.C., and Nesln, S.A. (2009) Customer Lfetme Value: Emprcal Generalzatons and Some Conceptual Questons. Journal of Interactve Maretng. 23(2), Bolton, R.N. (1998) A Dynamc Model of the Duraton of the Customer s Relatonshp wth a Contnuous Servce Provder: The Role of Satsfacton. Maretng Scence, 17(1), Brner, S. (2012) We are the 1% of global maretng spend. July 27, 2012, retreved on November 18, Chng, W.-K., Ng, M. K., and So, M. K. (2004) Customer Mgraton, Campagn Budgetng, Revenue Estmaton: The Elastcty of Marov Decson Process on Customer Lfetme Value. Advanced Modelng and Optmzaton, 6(2),

13 Davenport, T. H. and Harrs, J. G. (2007) Competng on Analytcs: The New Scence of Wnnng. Boston, MA: Harvard Busness School Press Davenport, T. H. and Harrs, J. G. (2008) Competng wth Analytcs. Boston, MA: Harvard Busness School Publshng. Davenport, T. H., Harrs, J. G., and Morson, R. (2010) Analytcs at Wor: Smarter Decsons, Better Results. Boston, MA: Harvard Busness School Press. Drect Maretng Assocaton (DMA) Press Release. (2012) DMA Releases Quarterly Busness Revew (QBR) for Q November 20, 2012, accessed on October 3, Drect Maretng Assocaton (DMA) Press Release. (2013) DMA Releases New Power of Drect Report; DM- Drven Sales Growth Outpace Overall Economc Growth. March 14, 2013, accessed March 14, Drect Maretng News. (2012) Drect Maretng News 2012 Agency Busness Report. June 2012, accessed on March 14, Dwyer, R. F. (1989) Customer Lfetme Valuaton to Support Maretng Decson Mang. Journal of Drect Maretng. 3(11), Dye III, Thomas A. (2003) Drect Maretng Database Strategy. Atlanta, GA: Gulf Atlantc Consultng, Inc. Edala, S. (2012), Bg Data Analytcs: Not Just for Bg Busness Anymore, December 28, retreved January 23, Fader, P. S., Harde, B. G. S., and Lee, K. L. (2005a) Countng Your Customers the Easy Way: An Alternatve to the Pareto/NBD Model. Maretng Scence. 24(Sprng), Fader, P. S., Harde, B. G. S., and Lee, K. L. (2005b) RFM and CLV: Usng Iso-Value Curves for Customer Base Analyss. Journal of Maretng Research. 42(November), Frans, B. (2012) Tamng the Bg Data Tdal Wave: Fndng Opportuntes n Huge Data Streams wth Advanced Analytcs. Wley and SAS Busness Seres, Hoboen, NJ: John Wley & Sons. Ferrante, A. (2009) New DMA Response Rate Study Shows Emal Stll Strong for Converson Rates. DemandGen Report, The Scorecard for Sales & Maretng Automaton, retreved March 16, Forbes, T. (2007) Valung Customers. Journal of Database Maretng & Customer Strategy Management. 15(1), Hambelton, K. (2013) Bg Data s Effects on Drect Maretng. March 7, 2/bg-datas-effects-drect-maretng/, retreved March 14, Kaplan, R.S., and Norton, D.P. (1992) The Balanced Scorecard Measures that Drve Performance. Harvard Busness Revew. 70(January-February),

14 Kumar, V., Raman, G. and Bohlng, T. (2004) Customer Lfetme Value Approaches and Best Practce Applcatons. Journal of Interactve Maretng. 18(3), Martnez, Juan. (2011) Mareters Ramp Up Real-Tme Strateges. Drect Maretng News. December 1, 2011, retreved March 13, McCarty, J. A. and Hasta, M. (2007) Segmentaton Approaches n Data-mnng: A Comparson of RFM, CHAID, and Logstc Regresson. Journal of Busness Research. 60(6), Renartz, W. J. and Kumar, V. (2000) On the Proftablty of Long-Lfe Customers n a Noncontractual Settng: An Emprcal Investgaton and Implcatons for Maretng. Journal of Maretng. 64(October), Rhee, E. and McIntyre, S. (2009) How Current Targetng Can Hnder Targetng n the Future and What To Do About It. Journal of Database Maretng & Customer Strategy Management. 16(1), Srdharan, S., Franland, D., and Smth, A. (2012) Use Customer Analytcs to Get Personal, Analytcally Drven Personalzaton Increases Retenton and Return. Forrester Research Report, February 17, Cambrdge, MA. Srdharan, S., Franland, D. and Smth, A. (2011) CI Teams: Blocng and Taclng Is Not Enough, Forrester Research Report, July 19, Cambrdge, MA. Stahl, H.K., Matzler, K., and H.H. Hnterhuber. (2003) Lnng Customer Lfetme Value wth Shareholder Value. Industral Maretng Management 32(4), Venatesan, R. and Kumar, V. (2004) A Customer Lfetme Value Framewor for Customer Selecton and Resource Allocaton Strategy. Journal of Maretng 68(October), Venatesan, R. Kumar, V., and Bohlng, T. (2007) Optmal Customer Relatonshp Management Usng Bayesan Decson Theory: An Applcaton for Customer Selecton. Journal of Maretng Research 44(November), Wong, E. (2009, March 3) Study: Drect Mal Spendng Down 3 Percent, retreved March 17,

15 Appendx A: Optmal Soluton for the Recency Model Appendx B: Optmal Soluton for the Frequency Model 15

16 Appendx C: Optmal Soluton for the Monetary Model Appendx D: Optmal Goal Programmng Soluton 16