USING GOAL PROGRAMMING TO INCREASE THE EFFICIENCY OF MARKETING CAMPAIGNS

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1 Journal of Internatonal & Interdscplnary Busness Research Volume 2 Journal of Internatonal & Interdscplnary Busness Research Artcle USING GOAL PROGRAMMING TO INCREASE THE EFFICIENCY OF MARKETING CAMPAIGNS Arben Asllan Unversty of Tennessee at Chattanooga, Alreza Lar Wae Forest Unversty, Follow ths and addtonal wors at: Recommended Ctaton Asllan, Arben and Lar, Alreza (2015) "USING GOAL PROGRAMMING TO INCREASE THE EFFICIENCY OF MARKETING CAMPAIGNS," Journal of Internatonal & Interdscplnary Busness Research: Vol. 2, Artcle 6. Avalable at: Ths Artcle s brought to you for free and open access by FHSU Scholars Repostory. It has been accepted for ncluson n Journal of Internatonal & Interdscplnary Busness Research by an authorzed admnstrator of FHSU Scholars Repostory.

2 Asllan and Lar: USING GOAL PROGRAMMING TO INCREASE THE EFFICIENCY OF MARKETING CA USING GOAL PROGRAMMING TO INCREASE THE EFFICIENCY OF MARKETING CAMPAIGNS Arben Asllan, Unversty of Tennessee at Chattanooga Alreza Lar, Wae Forest Unversty Organzatons allocate a part of ther fnancal resources to optmze ther maret segmentaton strateges, plan maretng campagns, and mprove customer relatonshps. Throughout ths process, they use a vast amount of electronc records generated by onlne and offlne purchases to desgn effectve maretng campagns and ntroduce personalzed promotons for ther customers by employng data analytcs. The problem of selectng target customer segments, gven varous prortes and the budget constrant, can be modeled as a mult-obectve optmzaton problem wth flexble goals and dfferent prortes, nterdependences and resources constrants. The man obectve of ths paper s to demonstrate the use of the goal programmng approach to address ths challenge. Keywords: Goal Programmng, RFM, CLV, Lnear programmng, Maretng Campagns INTRODUCTION Due to the scarcty of resources, companes commonly face the problem of prortzng the maretng actvtes n whch the frm wll nvest and determnng the levels of fundng for those actvtes. Selectng the best set of maretng actvtes s not easy as there are numerous factors that must be accounted for. Organzatons must select the most vable maretng actvtes to maxmze the outcomes (e.g. mprove customer relatonshps), and mnmze any negatve results (e.g. hgh costs). Ths requres dentfyng the most costbenefcal maretng campagns. In order to effectvely target maretng actvtes, t s assumed that dfferent groups of customers want dfferent nds of servces and products, and as a result maret segmentaton technques and customer segmentaton are wdely used. One opton n attemptng to select the most effectve maretng campagn s the RFM (Recency-Frequency- Monetary) approach. Recency, as defned by Fader, Harde, and Lee (2005), s the tme of a customer s most recent purchase, whle frequency s the number of past purchases. The lterature offers varyng defntons of monetary value (Fader et al., 2005; Blattberg, Malthouse, and Nesln, 2009; Rhee & McIntyre, 2009). These defntons nclude average spendng per transacton (essentally equvalent to M/F), and the total amount spent by a customer on all purchases over a specfed tme perod. The RFM framewor allows for more effectve maretng campagns by categorzng customers nto homogenous segments that allow for the desgn of promoton campagns that are customzed for the partcular segment at ssue. In ths approach, values for R, F, and M are assgned to each customer and are then used to categorze customers to help determne the most effectve types of promotons for that specfc customer. For example, f a gven customer segment shows a low value for recency and relatvely hgh values for frequency and monetary, then ths group of customers s typcally approached wth a we want you bac maretng strategy. If a gven customer segment shows a low value for monetary and hgh values for frequency and recency, then ths group of customers s approached wth a cross sellng maretng strategy. One drawbac s that the RFM approach assumes an unlmted maretng budget and complete access to all the organzatons customers, even those who have low RFM scores; however, these assumptons are not realstc because organzatons tend to operate under annual maretng budget constrants. In addton, the mportance of the R, F, and M components n the RFM approach mght not be the same. For example, a company mght be 53 JOURNAL OF INTERNATIONAL & INTERDISCIPLINARY BUSINESS RESEARCH - VOL. 2 - SPRING 2015 Publshed by FHSU Scholars Repostory, 2015 ISSN (prnt); ISSN (onlne) 1

3 Journal of Internatonal & Interdscplnary Busness Research, Vol. 2 [2015], Art. 6 mostly nterested n the R component, mang t a prorty to brng bac those customers who have taen ther busness to compettors and thereby placng frequency and monetary values as the second and thrd prortes, respectvely. To properly address and account for budget constrants and maretng prortes, managers must gear ther promotonal spendng strateges toward customers who wll yeld the greatest growth n cash flows and profts wthn the gven constrants. Kotler and Armstrong (1996) defne a proftable customer as a person, household, or company whose revenues over tme exceed, by an acceptable amount, the company costs of attractng, sellng, and servcng that customer. Ths excess s called customer lfetme value (CLV). CLV s the sum of cumulated cash flows, dscounted usng the weghted average cost of captal, of a customer over hs/her entre lfetme wth the company (Kumar, Raman, & Bohlng, 2004). In the context of customer relatonshp management, CLV becomes mportant because t s a metrc to evaluate maretng decsons (Blattberg & Deghton, 1996). CLV provdes a tool for frms to apply dfferent types of maretng nstruments toward dfferent customers based upon ther expected values, whch may result n better return on the frm s maretng nvestment. In maretng campagns, managers also strve to fnd a balance between two types of errors: gnorng those customers who could have returned to create more revenue for the organzaton, and nvestng on those customers who are not yet ready to purchase. Venatesan and Kumar (2004) refer to these errors as Type I and Type II errors. It s therefore mportant for maretng campagn decson-maers to understand the mportance of these two error types and to adust ther decsons accordngly. In order to create effectve maretng campagns, companes use a vast amount of electronc records generated by onlne and offlne purchases and use data analytcs to desgn effectve maretng campagns and ntroduce personalzed promotons for ther customers. As a result, for any effort n determnng the most effectve campagn strategy, there wll be a need for analytcal tools that can help the decson maers n choosng the optmal strategy. Ths paper presents a goal programmng model that balances Type I and Type II errors by dentfyng the RFM segments that should be reached and the RFM segments that are not worthy of pursut because they lac proftablty, do not follow prortes, or exceed maretng budget constrants. The model can help mareters determne whether to follow or cut bac on ther relatonshp wth a gven customer segment. A novel characterstc of ths model s the ncluson of campagn prortes and budget constrants to determne whch segment of customers should be deemed the optmal targets of a drect maretng campagn. LITERATURE SURVEY Ths secton dscusses the use of analytcs n maretng, summarzes the exstng lterature on RFM and CLV and explans how these concepts can be used n the proposed goal programmng model. Managers use vast amounts of electronc records to mae better decsons. Many onlne retalers have developed web-based nformaton systems to collect data and use dfferent analytcal tools n order to mae sound maretng decsons. Aberdeen Group Inc.'s survey of 458 busnesses shows that 120 of these busnesses are usng customer analytcs tools and processes as part of ther customer management actvtes (Mnara, 2012). Analytcal tools have been used n several maretng research studes. Hung et al. (2012) present a hybrd mult-crtera decson mang (MCDM) model that ncludes a decson-mang tral and evaluaton laboratory-based analytcs networ process for onlne reputaton management, to evaluate performances and mprove professonal servces of maretng. They found that the dmenson that professonal servces of maretng should mprove frst when carryng out onlne reputaton management s onlne reputaton. Kwa, Schnederans, and Warenn (1991), used lnear goal programmng to determne the optmal dstrbuton structure of a manufacturer of food products. The model helps decson maers determne the optmal dstrbuton structure n terms of the percentage of all commodty volume. In ths model, the goal constrants are maret share, proft and budget. To address the decreasng response to drect maretng campagns, mareters use data mnng technques. Many data-mnng applcatons have been developed to dscover useful customer and maret nformaton such as customer proflng, cross sellng (Ln, Chen, Chen, & Chen, 2003), and product recommendaton. Predctve data mnng technology helps mareters provde more value to ther customers by

4 Asllan and Lar: USING GOAL PROGRAMMING TO INCREASE THE EFFICIENCY OF MARKETING CA communcatng the rght offer to the rght customer at the rght tme. Breur (2007) argues that when both the maretng offer and targetng are tested at the same tme, t s not clear whether the hgher response to the new campagn can be attrbuted to an mprovement of the maretng offer or to better targetng. He proposes a comprehensve test-desgn to evaluate the relatve contrbuton of the maretng offer and targetng (data mnng models). Ths provdes decson maers wth a framewor for campagn plannng and evaluaton. Wth the ncrease n usng large data sets and analytcal tools for better decsons, the trend of ntroducng new modelng approaches wll contnue. In order to dentfy segments of le-mnded customers, the SAS System has ntroduced dfferent clusterng algorthms that provde a range of algorthms for dscoverng maret segments. The cluster analyss, whch s an emprcal technque, has been used as a tool for nformaton vsualzaton. Pratter (n.a.) has used the Fsher rs data of 1936 from the lbrary of SAS sample programs to show that cluster analyss wll always result n a set of segments. He warns the researchers about the face valdty of the segments. Bose & Chen (2009) present dfferent methods for customer clusterng and pattern recognton. The concepts of cluster analyss for segmentaton have also been dscussed by Venatesan (2007). He defnes segmentaton as: a way of organzng customers nto groups wth smlar trats, product preferences or expectatons. Once segments are dentfed, maretng messages and n many cases even products can be customzed for each segment. The better the segments chosen for targetng a partcular organzaton, the more successful t s assumed to be n the maretplace. Segments are constructed on the bass of customers a) demographc characterstcs, (b) psychographcs, c) desred benefts from product/servces, and d) past-purchase and product-use behavors. Clusterng analyss as a data mnng approach was used by Saglam, Salman, Sayn, and Turay (2006) to dentfy groups of enttes that are smlar to each other wth respect to certan measures. They proposed a mxed nteger programmng model based on the clusterng approach to a dgtal platform company s segmentaton problem that ncludes demographc and transactonal attrbutes related to the customers. The obectve functon was to mnmze the maxmum cluster dameter among all clusters wth the goal of establshng evenly compact clusters. The authors used a real problem from a satellte broadcastng company wth 800,000 customers, to test the performance of the proposed approach and found that t creates meanngful segmentaton of data. Many researchers have addressed the subect of customer preferences measurement. Scholz, Messner, and Decer (2010) have used a compostonal approach based on pared comparsons to measure customer preferences for complex products. Ths approach accounts for response errors and thus allows for elctaton of more precse preferences. They benchmar ths technque aganst adaptve conont analyss and computerbased self-explcaton of mult-attrbute preferences to show the relatve valdty and accuracy n two emprcal studes. Knowng what the customer wants s stll the base for choosng proper strateges for maretng campagns. In an effort to select the most effectve maretng campagns, researchers and practtoners have made use of CLV and RFM concepts. CLV s the net present value of cash flows expected over the lfe of the relatonshp between the customer and the frm. In the calculaton of CLV, Blattberg et al. (2009) suggest to nclude factors such as the expected length of the customer-frm relatonshp, the expected maretng costs and expected revenues generated throughout the lfe of ths relatonshp, and the dscount rate. In addton, Blattberg et al. (2009) have dentfed varables such as customer satsfacton, maretng efforts, cross-buyng, and multchannel purchasng to have a postve mpact on CLV. CLV s a metrc used to measure the proftablty of each customer, and serves as a target metrc when desgnng maretng campagns (Pfefer & Carraway, 2000; Venatesan, Kumar, & Bohlng, 2007; Forbes, 2007, Blattberg et al., 2009), as well as a gude for customer relatonshp management decsons (Zethaml, Btner & Gremler, 2009; Haenlen, Kaplan, & Beeser, 2007; Jacson, 2007; Zethaml, Rust, & Lemon, 2001). An accurate estmaton of CLV may prove to be dffcult for many frms (Stahl, Matzler, & Hnterhuber, 2003; Vogel, Evanschtzy, & Ramaseshan, 2008), whch ndcates a need for a method that would allow for Publshed by FHSU Scholars Repostory,

5 Journal of Internatonal & Interdscplnary Busness Research, Vol. 2 [2015], Art. 6 relatvely smple predctons of customers' potental proftablty and effectve customer relatonshp management (CRM) decson nputs. Borle, Sharad, Sngh, Sddharth, and Jan (2008) have used a herarchcal Bayes approach to estmate the lfetme value of each customer at each purchase occason by ontly modelng the purchase tmng, purchase amount, and rs of defecton from the frm for each customer. The results show that longer nter-purchase tmes are assocated wth larger purchase amounts and a greater rs of leavng the frm. In order to predct CLV, Enc, Ulengn, Uray, & Ulengn (2014) proposed a model that predcts the potental value of the current customers rather than measurng the current value. The Marovan-based model proposed by these authors helps companes wth several types of products to mae future maretng decsons. The emprcal valdty of the model was tested n the banng sector. One of the tools used to categorze customers accordng to proftablty potental for future drect maretng nvestment s RFM. It s used as a promotonal tool that allocates spendng based on the amount of customers purchases nstead of the length of ther relatonshp wth the frm (Renartz & Kumar, 2000). Customers who create hgh revenues for the organzaton wll receve a hgher level of promotonal spendng (Venatesan et al., 2007). Recency, frequency and monetary values are not always equally weghted, and recency often has a greater weght as t may be used to sgnal the end of the customer-frm relatonshp; a long perod of customer nactvty may be ndcatve of ths termnaton (Dwyer, 1989). Less emphass s put on monetary value, and the least on frequency (Renartz & Kumar, 2000; Venatesan et al., 2007). Among the many analytcal tools used for maretng campagns, RFM remans popular due to ts smplcty of use. Many maretng data mnng algorthms, and n partcular the ones for drect maretng, are based on ths concept (McCarty & Hasta, 2007). The use of such data mnng technques allow mareters to better manage ther customers databases for segmentaton and generate more effectve and cost effcent promotonal strateges for drect maretng campagns. Bose & Chen (2009) revewed research n the area of quanttatve models for drect maretng from a system perspectve. In ths study, they show that two types of models, statstcal and machne learnng based are popular. They present the advantages and dsadvantages of each type of model. Venatesan et al., 2007 have reported other methods n addton to RFM analyss for evaluaton of customer selecton durng a maretng campagn, and estmaton of the future value of customers. Some of these methods (e.g. return on equty) evaluate the fnancal return from partcular maretng expendtures such as drect mal and sales promoton (Rust, Lemon, & Zethaml, 2004). Elsner, Krafft, and Huchzermeer (2003) dscuss other applcatons of RFM that go beyond ts tradtonal drect maretng approach and provde a dynamc heurstc model. Ths model combnes a ch-square automatc detecton nteracton algorthm wth recency, frequency, and monetary value segmentaton to determne the optmal frequency of catalog malngs for a company n the mal order busness. Ths wll help mareters predct the tme when customers should receve reactvaton pacages. Bhaser et al. (2009) utlzed mathematcal programmng (MP) and RFM analyss for personalzed promotons for multplex customers, ncorporated busness constrants, and then, provded useful nsghts, adng the multplex n mplementng an effectve loyalty program. The researchers separated RFM from MP n ther algorthm, usng RFM s for non-recent customers and MP for current customers. Ths paper presents a goal programmng model that uses data from an RFM analyss and the annual budget constrants on the maretng campagns. Incorporatng RFM data nto a sngle goal programmng model for all potental drect maretng campagn target customers s a maor contrbuton of ths research

6 Asllan and Lar: USING GOAL PROGRAMMING TO INCREASE THE EFFICIENCY OF MARKETING CA THE PROPOSED GOAL PROGRAMMING FORMULATION The Approach Goal Programmng (GP) s a mult-obectve mathematcal programmng approach wth several obectves where some are treated as constrants nstead of obectves. In ths type of mathematcal programmng, the model automatcally adusts the level of certan resources to satsfy the goal of the decson maer. When developng an advertsng campagn, the manager needs to decde the cutoff ponts for recency (R), frequency (F), and monetary (M) values wth the goal of maxmzng overall customer lfetme value (CLV) wthn a lmted budget. If the modeler s not concerned wth F and M, then we would have a smple lnear program to determne the cutoff pont for R. For ths smple lnear program, the soluton process wll generate a maxmum CLV based on recency only, whch s VR. Smlarly, for Frequency only, not consderng M and R, we wll have a smple lnear program that fnds VF as the maxmum CLV for the cutoff value of F. In the case of modelng the lnear program for M, the maxmum CLV for the M cutoff pont s VM. The modeler may tae each of the values VR, VF, and VM found n solvng the correspondng lnear programs as a "goal'' and try to fnd a soluton that comes close to all of the goals. Snce t may not be possble to reach all of the goals smultaneously, the modeler wll create a set of penaltes for not reachng a goal. These penaltes depend on the mportance of reachng a partcular segment. For example, f 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 wll create new varables s1, s2, and s3 to represent the falure of meetng goal 1, 2, and 3 respectvely, and creates the followng lnear program: Mnmze P1s1 + P2s2 + P3s3 Subect to {Obectve functon of the R model} + s1 = VR {Obectve functon of the F model} + s2 = VF {Obectve functon of the M model} + s3 = VM + any other constrants, ncludng budget constrants In order to llustrate the proposed GP model, a sample of 543,311 real customer transactons from a chan of brewery-based restaurants s used. These data represents transactons of 23,239 customers who had vsted one of the restaurants at least three tmes. Snce frequency s an mportant varable n the proposed model, the data were fltered to show those customers who have shown some degree of loyalty. Each data pont n ths study contans the customer s ID, the last transacton date (as recency), number of transactons (as frequency), and the average sales per customer (as monetary value). Summary statstcs for number of vsts and average sales are shown n Fgure 1. The selected customers have vsted the stores on average about tmes and every tme they have spent an average of $ Fgure 2 shows the dstrbuton of the most recent vst. As shown, about 20 percent of the customers stll contnued to vst the store at the tme of data collecton. Fgure 1: Summary Statstcs for Frequency and Monetary Value Publshed by FHSU Scholars Repostory,

7 Journal of Internatonal & Interdscplnary Busness Research, Vol. 2 [2015], Art. 6 Fgure 2: Hstogram for Most Recent Vst THE OPTIMIZATION MODELS NOTATIONS = ndex for the group of customers n a gven recency category (=1,,5); = ndex for the group of customers n a gven frequency category (=1,,5); = ndex for the group of customers n a gven monetary category (=1,,5); V= Expected revenue from a returned customer; p= the probablty for a group recency customer to mae a purchases; p= the probablty for a group frequency customer to mae a purchase; p= the probablty for a group monetary customer to mae a purchase; N= number of current customers n recency group ; N= number of current customers n frequency group ; N= number of current customers n monetary group ; C= average cost of reachng a customer durng the maretng campagn; B= budget celng for the maretng campagn; Indces,, and and ther respectve categores are defned on consultaton wth company management. The cut-off ponts for each category are shown n Fgure 3 and are based on prevous experence wth smlar groups of customers, busness dynamcs, and sales data. The goal of the company s to decde whether to reach or not reach customers n a certan category consderng a lmted maretng budget of $150,000 per campagn. Three separate LP models, one for each category, are formulated and solved. Then, each of these solutons s ncorporated nto the last goal programmng model whch ams to dentfy best customer categores to acheve several prorty goals. Fgure 3: Cut-off Ponts for Each Category

8 Asllan and Lar: USING GOAL PROGRAMMING TO INCREASE THE EFFICIENCY OF MARKETING CA FORMULATION AND SOLUTION PROCESS OF INTEGER LINEAR PROGRAMMING MODEL FOR THE RECENCY CASE The obectve for ths 0-1 LP model wth recency dmenson s to maxmze the profts from potental customer purchases wthn the gven budget. Consder the decson varable as: x = 1 f the maretng campagn reaches customers n recency ; 0, otherwse The mxed 0-1 nteger lnear programmng formulaton s: Obectve Functon: Maxmze: subect to: Z R r =1 = R = 1 N Cx N ( p V C) x (1) { 0,1 } B x = = 1 R. (3) The obectve functon maxmzes the expected proft (Zr) of the maretng campagn. A customer n a state of recency has a p chance of purchasng (wth a proft of V-C ) and a (1- p) chance of not purchasng (wth the expected proft of C ). Therefore, the expected value of the proft from a sngle customer n state s: p ( V C) + (1 p )( C) (4), whch canbe wrtten as: p V C (5), (2) For the N customers n recency, the expected proft from ths group of customers s: N ( p V C), (6) In the above formulaton, equaton (1) ndcates the sum of profts for all the groups of customers who are targeted for advertsement (x=1) and equaton (2) mposes the budget lmtatons (B) for ths maretng campagn. The left sde of the equaton that shows the sum of campagn costs for each group of customers represents the actual cost of the campagn. In order to solve the above problem, the followng steps are followed: 1. The customers are dvded nto groups 1 through 5 where group 1 represents the customers wth the least recent purchases and group 5 represents the ones wth the most recent transactons. 2. The number of customers n each group s calculated by usng a pvot table that shows np, the number of customers n recency who mae a purchase wthn the next month. The probablty that a customer n group wll purchase s calculated as: Publshed by FHSU Scholars Repostory,

9 Journal of Internatonal & Interdscplnary Busness Research, Vol. 2 [2015], Art. 6 np p = (7) N As ndcated n Fgure 4, gven a campagn budget of B= $150,000, a cost of C= $7.50 to reach a customer, and the average revenue of V=$32.81 from the purchasng customer, the company should only select customers of recency 2, 4, and 5 for future promotonal efforts. Customers who belong to recency 2 can smply be gnored due to the small contrbuton n the overall proft as shown graphcally. Ths soluton wll generate a total proft of $214,789. Fgure 4: LP Formulaton and Soluton for the Recency Case FORMULATION AND SOLUTION PROCESS OF INTEGER LINEAR PROGRAMMING MODEL FOR THE FREQUENCY CASE The obectve for ths 0-1 LP model wth frequency dmenson s to maxmze the profts from potental customer purchases wthn the gven budget. The decson varable for ths case s a 0-1 varable wth the followng defnton: X = 1 f the maretng campagn reaches customers n frequency ; 0, otherwse; The mxed 0-1 nteger lnear programmng formulaton for the frequency case s shown below:

10 Asllan and Lar: USING GOAL PROGRAMMING TO INCREASE THE EFFICIENCY OF MARKETING CA Obectve Functon: Maxmze: subect to: Z F f =1 = N F = 1 Cx N ( p V C) x (8) B (9) { 0,1 } x = =1 F. (10) The obectve functon n equaton (8) maxmzes the expected proft (Zf) of the maretng campagn. There s a chance of p for a customer n a state of frequency to purchase and a chance of (1- p) not to purchase. The proft from a customer s calculated as (V-C) when there s a purchase, otherwse the expected proft s (-C). Therefore, the expected proft from a sngle customer n state s: or n a smpl;fed form: p ( V C) + (1 p )( C) (11) p V C (12) For N customers wth frequency, the expected proft of ths group of customers s: N ( p V C) (13) Wth ths explanaton, equaton (8) shows the total proft for groups of customers for whch the maretng decson to reach them s made. The left sde of equaton (9) represents the actual cost of the campagn, whch s calculated as the sum of campagn costs for each group of customers and should not exceed the budget celng of B. There are companes that consder recency and frequency as the only two sgnfcant values n ther drect maretng campagn. In ths stuaton, customers are frst organzed nto groups (n ths case 5 groups), wth each G group contanng customers from frequency value (1, 2, 5). Agan, companes are nterested n determnng the customer groups that should be targeted and reached. Smlar to the prevous case, the number of customers n each group s calculated usng a pvot table. If the number of customers n group G s consdered to be N, then the probablty that a customer n ths group wll purchase s calculated as: n p p = (14) N Publshed by FHSU Scholars Repostory,

11 Journal of Internatonal & Interdscplnary Busness Research, Vol. 2 [2015], Art. 6 Fgure 5: LP Formulaton and Soluton for the Frequency Case The results of the frequency soluton are shown n Fgure 5. The soluton ndcates that customers n the frequency 2, 3, 4, and 5 must be reached. Ths soluton wll generate a total proft of $772,902. FORMULATION AND SOLUTION PROCESS OF INTEGER LINEAR PROGRAMMING MODEL FOR THE MONETARY CASE The LP model of ths secton ncludes monetary value. The obectve functon s to maxmze the proft from potental customer purchases wthn the budget lmts. The decson varable s defned as: x = 1 f the maretng campagn reaches the customers n monetary group 0, otherwse; subect to: Obectve Functon Maxmze: Z m M = =1 M = 1 N N Cx ( p V C) x (15) B (16) { 0,1 } x = =1 M (17)

12 Asllan and Lar: USING GOAL PROGRAMMING TO INCREASE THE EFFICIENCY OF MARKETING CA The obectve functon n equaton (15) maxmzes the expected proft (Zm) of the maretng campagn. There s a p chance for a customer n a state monetary of purchasng and a (1- p) chance of not purchasng. When purchasng, the proft from a customer s calculated as (V-C). When not purchasng, the expected proft s smply (-C). The expected value of the proft from a sngle customer n state s: or: p ( V C) + (1 p )( C) (18) p V C (19) Wth N customers wth monetary, the expected proft s: N ( p V C) (20) Equaton (16) creates a budget lmt of B for ths maretng campagn. An optmal soluton summary for the monetary model s presented n Fgure 6. Ths fgure ndcates the segments that are proftable for the company. The future promotonal campagn must nclude customers wth monetary values of 2, 3, 4, and 5 as these segments are clearly proftable. Ths soluton wll generate a total proft of $800,442. Fgure 6: LP Model Formulaton and Soluton for the Monetary Value Case Publshed by FHSU Scholars Repostory,

13 Journal of Internatonal & Interdscplnary Busness Research, Vol. 2 [2015], Art. 6 GOAL PROGRAMMING MODEL: INCORPORATING PRIORITIES The comparson of the maxmum proft from each of the above three models ndcates that monetary value score s the most mportant varable of the RFM framewor. The next mportant varable s frequency, followed by recency. However, the maretng department s nterested n nvestgatng the mpact of settng the followng prortes: Prorty 1 (P1 = 3): Recency Prorty 2 (P2 = 2): Frequency Prorty 3 (P3 = 1): Monetary The followng s the LP formulaton, whch mnmzes the penaltes of not reachng the goals: Mnmze 3s 1 + 2s 2 + 1s 3 (21) Subect to: R = 1 F = 1 M = 1 N ( p V C) x + s 1 = V R (22) N ( p V C) x + s 2 = V F (23) N ( p V C) x + s 3 = V M (24) R F N Cx + N f Cx f + N Cx = 1 f = 1 = 1 M B (25) { 0,1 } { 0,1 } { 0,1 } x = = 1 R (26) x = f = 1 F (27) f x = =1 M (28) In the above formulaton, (21) represents the obectve functon. Mnmzaton of s1 has prorty over mnmzaton of s2, snce s1 has a larger contrbuton coeffcent (3>2). Smlarly, mnmzng s3 has the lowest prorty. Equatons (22), (23), and (24) represent the new set of constrants added to the model to mae sure that proft goals VR= $214,789, VF= $772,902, and VM= $800,442 are set to be acheved. Equaton (25) assures that the overall budget (B=150,000) s not exceeded. Fnally, equatons (26), (27), and (28) ensure bnary soluton values for the decson varables. SOLVING THE OVERALL MODE Fgure 7 shows the optmal soluton to the goal programmng approach. As seen, the total proft for the soluton s $1,254,064 and the soluton suggests that the manager must reach customers wth a recency score of 2 or 5, a frequency score of 3, 4, or 5 and a monetary value score of 4 or

14 Asllan and Lar: USING GOAL PROGRAMMING TO INCREASE THE EFFICIENCY OF MARKETING CA Fgure 7: Optmal soluton for the GP model CONCLUSIONS AND RECOMMENDATIONS The analyss presented here provdes the optmal solutons for dfferent varatons of the RFM model: a recency model, a frequency model, a monetary value model, and a full RFM-goal programmng model wth prorty constrants. Table 1 compares the fnal solutons for each model. Because of set prortes, the optmal soluton for the GP model s dfferent from the solutons suggested by each ndvdual LP model. For example, whle the recency LP model suggests reachng customers wth a score of 2, 4, and 5, the GP model suggests that only customers wth a recency score of 2 and 5 must be reached. Droppng the segment wth recency score 4 seems contradctory to the hgh prorty gven to recency. However, whle the LP model only consders recency, GP model consders all three factors, although recency has the hghest prorty. When movng from frequency LP model to GP model, the customers wth a frequency score of 2 are dropped from the maretng campagn. Smlarly, due to the lowest prorty gven, the monetary value score of 2 and 3 are not consdered n a maretng campagn wth set prortes. Ths soluton s constraned by the campagn budget of $150,000 and generates a total proft of $1,254,064. Table 1: Summary of Results of Dfferent Approaches Organzatons have lmted maretng resources and managers are forced to prortze ther promotonal spendng. Gven the tradtonally small response rates n many drect maretng campagns (e.g., 1.65% for drect mal prospect lsts to 4.41% for outbound telemaretng house lsts), spendng scarce resources to reach Publshed by FHSU Scholars Repostory,

15 Journal of Internatonal & Interdscplnary Busness Research, Vol. 2 [2015], Art. 6 customers who are not ready to purchase (a Type II error) may not be the best strategy (Farrante, 2009; Venatesan & Kumar, 2004). Use of analytcs can help managers better manage ther scarce resources and establsh a balance between Type I (mssng customers who are potentally proftable) and Type II errors by dentfyng RFM segments that should be reached and RFM segments that are not worthy of pursung because they are unproftable or may lead to exceedng budget constrants. By dentfyng the most proftable customer segments (gven certan maretng costs to reach a customer and total maretng budget constrants), a goal programmng approach appled to RFM data can, n a sngle model, provde drect maretng companes wth the capabltes of mang optmum decsons regardng future promotonal nvestments. Dependng on a customer segment s proft maxmzaton potental, a drect maretng frm can determne whether to contnue ts promotonal spendng n an attempt to generate future sales, or whether t should curtal spendng and allocate those maretng resources to other, more proftable customer targets. Ths paper can be used as a template for practtoners to utlze and transform purchasng hstory data nto a decson model. The specfc contrbuton of ths paper s on consderng budget constrants and assgnng prortes to customer segments whch are based on RFM. The study has several lmtatons, all of whch provde avenues for ongong research. Frst, some have rased the ssue of whether RFM can accurately predct future behavor or proftablty, gven that RFM framewors represent past or hstorcal behavor (Blattberg et al., 2009; Rhee & McIntyre, 2009). Of course, uncertanty n predctng behavor s nherent n any consumer decson model, and ths example s no excepton. Accuracy n predcton wll always be a potental lmtaton when forecastng s based on hstorcal data. In addton, the current model s lmted to a sx-month tme frame, whereas Venatesan et al. (2007) note that three years s generally consdered a good horzon for estmates of CLV and for CRM decsons such as customer selecton. Whle ths research does not estmate CLV, future applcatons mght go beyond sx months. The statc nature of ths model could be perceved as a potental lmtaton, although t does have the advantage of smplcty and ease of use for most organzatons (as compared to CLV calculatons). Ths study made no assumptons about the nature of the costs used n the RFM model. Ultmately, assumptons regardng costs have an mpact on CLV, and therefore may mpact any RFM model as well. For example, f only varable costs of servng a customer are consdered (.e., margnal costng) as compared to full costs (wth overhead allocaton), the calculaton of CLV could be qute dfferent. Blattberg, Km, and Nesln (2008) argue for margnal costng snce full costng rases costs and can lead to the reecton of some customers (customers who would ncrease profts f they were targeted). Blattberg et al. (2009) also support the argument for margnal costng, but note that both full costng and margnal costng applcatons have been found n the lterature. Agan, these cost ssues relate prmarly to the predcton of CLV rather than to RFM analyss; but they do suggest that careful determnaton of costs s necessary. Future RFM research should tae these potental lmtatons nto account n order to contnually mprove the utlty and relablty of ths analytcal method. WORKS CITED Bhasar, T., Subramanan, G., Bal, D., Moorthy, A., Saha, A., & Raagopalan, S. (2009).An Optmzaton Model for Personalzed Promotons n Multplexes. Journal of Promoton Management, 15(1/2), Blattberg, R.C., & Deghton, J. (1996, July-August). Manage maretng by the customer equty test, Harvard Busness Revew, Blattberg, R.C., Km, B., & Nesln, S.A. (2008).Database Maretng: Analyzng and Managng Customers. New Yor: Sprnger

16 Asllan and Lar: USING GOAL PROGRAMMING TO INCREASE THE EFFICIENCY OF MARKETING CA Blattberg, R.C., Malthouse, E.C., & Nesln, S.A. (2009).Customer Lfetme Value: Emprcal Generalzatons and Some Conceptual Questons. Journal of Interactve Maretng, 23(2), Borle, Sharad, Sngh, Sddharth S., & Jan D. C. (2008, January). Customer lfetme value measurement, Management Scence, 54(1), Bose, I., & Chen X. (2009). Quanttatve models for drect maretng: a revew from systems perspectve. European Journal of Operatonal Research, 195, Breur, Tom. (2007). How to evaluate campagn response The relatve contrbuton of data mnng models and maretng executon. Journal of Targetng, Measurement and analyss for Maretng, 15, pp , do: /palgrave.t Dwyer, R. F. (1989).Customer Lfetme Valuaton to Support Maretng Decson Mang. Journal of Drect Maretng, 3(11), Enc, Y., Ulengn, F., Uray, N., & Ulengn, B. (2014). Analyss of customer lfetme value and maretng expendture decsons through a Marovan-based model, European Journal of Operatonal Research, Elsner, R., Krafft, M., & Huchzermeer, A. (2003). Optmzng Rhenana's Mal-Order Busness through Dynamc Multlevel Modelng. (DMLM). Interfaces, 33(1), Fader, P. S., Harde, B. G. S., & Lee, K. L. (2005).Countng Your Customers the Easy Way: An Alternatve to the Pareto/NBD Model. Maretng Scence, 24(Sprng), 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, 2010, from Forbes, T. (2007).Valung Customers. Journal of Database Maretng & Customer Strategy Management, 15(1), Haenlen, M., Kaplan, A., & Beeser, A. (2007). A model to determne customer lfetme value n a retal banng context. European Management Journal, 25(3), Hung, Y.H., Huang, T. L., Hseh, J.C., Tsue, H.J., Cheng, C.C., & Tzeng, G.H. (2012-November). Onlne reputaton management for mprovng maretng by usng a hybrd MCDM model. Knowledge-based Systems, 35, Jacson, T. W. (2007). Personalsaton and CRM. Journal of Database Maretng & Customer Strategy Management, 15(1), Kotler, P., & Armstrong, G. (1996). Prncples of Maretng, 7 th edton. Englewood clffs, N.J.: Prentce-Hall. Kumar, V., Raman, G., & Bohlng, T. (2004). Customer Lfetme Value Approaches and Best Practce Applcatons. Journal of Interactve Maretng, 18(3), Kwa, N.K., Schnederans, M.J., & Warentn, K.S. (1991). An applcaton of lnear goal programmng to the maretng dstrbuton decson. European Journal of Operatonal Research, 52(3), Publshed by FHSU Scholars Repostory,

17 Journal of Internatonal & Interdscplnary Busness Research, Vol. 2 [2015], Art. 6 Ln, Q. Y., Chen, Y.L., Chen, J.S., & Chen Y. C. (2003). Mnng nter-organzatonal retalng nowledge for an allance formed by compettve frms, Informaton and Management, 40(5), McCarty, J. A., & Hasta, M. (2007). Segmentaton Approaches n Data-mnng: A Comparson of RFM, CHAID, and Logstc Regresson. Journal of Busness Research, 60(6), Mnara, O. (2012, Wnter). The Mddle Ground. Maretng Research, 24(4), Pfefer, P. E., & Carraway, R.L. (2000). Modelng Customer Relatonshps as Marov Chans. Journal of Interactve Maretng, 14(2), Pratter, F. (n.a.). Clusterng for maret segmentaton, Retreved on February 14, 2014, from Renartz, W. J., & 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., & 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), Rust, R. T., Lemon, K. N., & Zethaml, V. A. (2004). Return on Maretng: Usng Customer Equty to Focus Maretng Strategy. Journal of Maretng, 68(January), Saglam, B., Salman, F.S., Sayn, S., & Turay, M. (2006). A mxed nteger programmng approach to the clusterng problem wth an applcaton n customer segmentaton. European Journal of Operatonal Research, 173, Scholz, S., Messner, M., & Decer, R. (2010, August). Measurng consumer preferences for complex products: a compostonal approach based on pared comparsons, Journal of Maretng Research, 47(4), Stahl, H. K., Matzler, K., & Hnterhuber, H. H. (2003).Lnng Customer Lfetme Value wth Shareholder Value. Industral Maretng Management, 32(4), Venatesan, R. (2007). Cluster analyss for segmentaton, (publcaton number UVA-M-0748). Retreved on February 14, 2014 from Venatesan, R., & Kumar, V. (2004).A Customer Lfetme Value Framewor for Customer Selecton and Resource Allocaton Strategy. Journal of Maretng, 68(October), Venatesan, R., Kumar, V., & Bohlng, T. (2007).Optmal Customer Relatonshp Management Usng Bayesan Decson Theory: An Applcaton for Customer Selecton. Journal of Maretng Research, 44(November), Vogel, V., Evanschtzy, H., & Ramaseshan, B. (2008).Customer Equty Drvers and Future Sales. Journal of Maretng,72 (November), Zethaml, V. A., Btner, M. J., & Gremler, D.D. (2009). Servces Maretng: Integratng Customer Focus across the Frm. New Yor: McGraw-Hll Publcatons. Zethaml, V. A., Rust, R. T., & Lemon, K., N. (2001).The Customer Pyramd: Creatng and Servng Proftable Customers. Calforna Management Revew, 43(Summer),

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