Decision Support Systems as the Bridge between Marketing Models and Marketing Practice

Transcription

1 Dcision Support Systms as th Bridg btwn Markting Modls and Markting Practic by Brnd Wirnga,. 1. Introduction Th fild of markting dcision modls mrgd about fifty yars ago. In th bginning, optimization tchniqus from th fild of Oprations Rsarch (OR) wr dominant, but soon, th modling of markting phnomna and markting problms bcam intrsting in itslf, irrspctiv of whthr thy could b solvd with a known OR tchniqu. Th fild of markting modls dvlopd its own idntity and bcam an important acadmic fild (Wirnga 2008b). Somwhat latr th trm "markting scinc" bcam in vogu, as a clos synonym to markting modls. In this currnt, first dcad of th nw Millnnium, th fild of markting scinc is in xcllnt shap with booming journals and xponntially growing numbrs of publications. Howvr, a lgitimat qustion can b askd: what is th impact of this growing body-of-knowldg on markting practic? Dos all this work lad to bttr markting managmnt dcisions? In othr words "Was macht di Wissnschaft fur di Untrnhmnspraxis?" (Simon 2008). On important flow of markting knowldg to markting practic gos through markting ducation and markting litratur.. Th nwst markting insights ar dissminatd through courss, txtbooks and othr communication channls. But thr is anothr important way of making th rsults from markting scinc usful for markting practic. Brnd Wirnga is Profssor of Markting, Rottrdam School of Managmnt, Erasmus Univrsity. His rsarch intrsts focus on markting modls, markting dcision making, and markting managmnt support systms. H has publishd widly on ths topics, in journals such as Intrnational Journal of Rsarch in Markting, Journal of Markting, Journal of Markting Rsarch, Managmnt Scinc, and Markting Scinc. H has writtn svral books, most rcny: "Markting Managmnt Support Systms: Principls, Tools, and Implmntation" (with Grrit van Bruggn). H is also th ditor of th forthcoming "Handbook of Markting Dcision Modls", which will appar in th Intrnational Sris in Oprational Rsarch and Managmnt Scinc of Springr Scinc + Businss Mdia. (2008).This papr is an abbrviatd vrsion of Chaptr 17 of this Handbook. For mor information about th Handbook of Markting Dcision Modls go to: 38 MARKETING JRM. 1/2008 pp This is through markting dcision support systms. Markting dcision support systms act as a bridg btwn markting scinc and markting practic. At th start of th markting modls movmnt, popl did not s th nd for such a bridg. Markting modls lad to bttr dcision, hncforth practitionrs would us thm. Unfortunay, it turnd out that this did not happn. Alrady in th arly 1970's, Lit obsrvd: "Th big problm with managmnt scinc modls is, that managrs practically nvr us thm" (Lit 1970). Mathmatical markting modls, howvr grat thir potntial, ar mosy not in a form that maks thm dircy suitabl for application to markting problms in practic. Thy hav to b intgratd in a dcision support systm in ordr to mak thm work. W can conciv of th modl (oftn combind with an optimization procdur) as th powrful ngin of a dcision support systm, but an ngin alon dos not tak you anywhr. It has to b installd on som platform, intgratd with th IT systm of th company; it has to b connctd to data sourcs from insid and outsid th company and th dcision makr should b abl to communicat with th modl through a usr-intrfac that is asy to us. Such a conslation is calld a markting dcision support systm, and w com back to this latr. Th xprinc with th application of markting modls in practic is mixd ("th glass is both half-full and halfmpty" - Lilin and Rangaswamy 2008), but thr ar ncouraging signs. In 2004 th INFORMS Socity on Markting Scinc Practic Priz Comptition was inauguratd. This comptition which is opn for implmntations of markting scinc concpts and mthods that hav a. "significant impact on th prformanc of th organization". Th list of winnrs and finalists so far (thr of thm ar from Grmany/Austria) contains fiftn imprssiv applications sprad ovr diffrnt aras of markting, including dirct markting, customr liftim valu, onlin markting, markting mix dcisions, forcasting, sals forc dcisions, sals promotions, product lin dcisions and advrtising (Lilin and Rangaswamy 2008). Considring that it taks a lot of ffort and tim to gt an implmntation rady for submission to th Practic Priz, it is saf to assum that ths succsss ar th top of a quickly growing icbrg of markting scinc implmntations in markting practic. Anothr indication of th incrasing adoption of markting scinc-basd applications in markting is th fact that SIMON, KUCHER & PARTNERS (SKP), a prominnt Stratgy and Markting Consultancy hadquartrd in Bonn, mntions that ovr rcnt yars thy hav dv sysi and wht and as' nc xa ofi sp (Si ar ant ing br Th th an m~ mr Ta su WI an 2. al In N pi d( m A ill m l CI ( P S h

2 vvirnq, uciston suopon :iysrms as m tsnaq orwn lviarkrmg tvtoots ana IVlarKnng r-rcuc dvlopd and implmntd ovr 300 dcision support systms, in a broad rang of diffrnt industris (Englk and Simon 2007). Hrmann Simon, th Chairman of SKP who has a lot of xprinc in both markting acadmia and markting practic classifis dcision support systms as "hits" undr th diffrnt approachs in markting scinc sinc 1960 (Simon 2008). Also othr companis, for xampl ZS Associats, hav ralizd imprssiv numbrs of implmntations of markting dcision support systms, spcially in th fild of sals managmnt dcisions (Sinha & Zoltnrs 2001; Zoltnrs and Sinha 2005). In th ara of consumr products, companis lik Nilsn, GfK, and IRI ar constany dvloping and improving markting information systms that hlp thir clints to gt th bst dcision-rlvant information out of thir data. Th rmaindr of this articl will start with discussing th concpt of markting managmnt support systms, and in particular dal with on important componnt of markting managmnt support systms, i.. markting modls. Nxt, w will discuss svral dvlopmnts (s Tabl 1) which crat nw opportunitis for dcision support systms in markting. W conclud th articl with a discussion of th gap btwn markting scinc and markting practic and how dcision support systms can hlp to bridg this gap. 2. Markting managmnt support systms and markting modls In 1966, Kor introducd th concpt of a "Markting Nrv Cntr", providing markting managrs with "computr programs which will nhanc thir powr to mak dcisions." Th first of ths systms wr ssntially markting information systms (Brin and Stafford 1968). At that tim, th rcny introducd computrs in companis producd lots of data, and a systmatic approach was ndd to mak thos data availabl in a way that managrs could us thm for dcision-making. Othrwis, thr could b a srious dangr of "ovrabundanc of irrlvant information" (Ackoff 1967). About tn yars latr, Lit (1979) introducd th concpt of markting dcision support systms. H dfind a markting dcision support systm (MDSS) as a "coordinatd collction of data, systms, tools and tchniqus with supporting softwar and hardwar by which an organization gathrs and intrprts rlvant information from businss and nvironmnt and turns it into an nvironmnt for markting action" (p. 11). Lit's concpt of an MDSS was much mor than a markting information systm. Important lmnts wr modls, statistics, and optimization, and th mphasis was on rspons analysis; for xampl, how sals rspond to promotions. In Lit's viw, MDSS wr suitabl for structurd and smi-structurd markting problms, had a quantitativ orintation and wr data-drivn. Almost two dcads latr, Wirnga and Van Bruggn (1997) prsntd a classification of markting dcision support tchnologis and tools, and usd th trm "mar- kting managmnt support systms" to rfr to th complt st of markting dcision aids. Thy dfin a markting managmnt support systm (MMSS) as "any dvic combining (1) information tchnology, (2) analytical capabilitis, (3) markting data, and (4) markting knowldg, mad availabl to on or mor markting dcision makrs, with th objctiv to improv th quality of markting managmnt" (p. 28). Markting managmnt support systms is a comprhnsiv trm which ncompasss th primarily quantitativ, data-drivn markting dcision support systms (for structurd and smistructurd problm aras), as wll as markting information systms, markting knowldg-basd systms and xprt systms, and also tchnologis that ar aimd at supporting markting dcision-making in wakly-structurd aras (for xampl: analogical rasoning-althuizn and Wirnga 2008). Closly rlatd to MMSS is th concpt of markting nginring (ME), dfind as: "a systmatic approach to harnss data and knowldg to driv markting dcision making and implmntation through a tchnologynabld and modl-supportd intractiv dcision procss" (Lillin, Rangaswamy, and D Bruyn 2007, p 2). ME focuss on th analytical componnt of MMSS, which still has to b implmntd in a (broadr) markting managmnt support systm in ordr to mak it accssibl for usrs (dcision makrs in companis). Markting modls From th bginning, markting modls hav bn a cor lmnt of markting managmnt support systms. Thy rprsnt th analytical capabilitis componnt of a MMSS (s th componnts of MMSS mntiond abov). A markting modl rlats markting dcision variabls to th outcoms in th markt plac (for xampl sals, markt shar, profit). A markting modl can b usd to find th bst dcision (optimizing) or to answr so-calld "what-if' qustions (for xampl: how will sals rspond, if w incras our advrtising budgt with x prcnt?). Initially, thr was a lot of optimism about markting modls. With markting modls, it smd, markting would almost bcom a scintific activity. Kor (1971) opns his classical book on markting modls, with th statmnt: "Markting oprations ar on of th last phass of businss managmnt to com undr scintific scrutiny" (p.1). It lookd as if markting dcision making would just bcom a mattr of formulating a markting problm as a mathmatical programming problm, and thn solv it with on of th known tchniqus of Oprations Rsarch. But th harsh rality was that th actual application of markting modls to ral-lif problms in companis rmaind far blow xpctations. This has causd a tradition of complaints in th markting litratur, lasting until today: "Mayb thr is som lvl of maturity in th tchnology, but I cannot s much vidnc in th application" (Robrts 2000). Lilin and Rangaswamy (2008) rfr to "th gap btwn ralizd and actual potntial for th application of markting modls". MARKETING JRM 1/

3 Wirnga, Dcision Support Systms as th Bridg btwn Markting Modls and Markting Practic In hindsight, for marktrs it should not hav com as a surpris that th supply of sophisticatd markting modls did not automatically gnrat dmand. Markting modls hav to b adoptd and usd by dcisionmakrs in organizations, and marktrs ar just lik othr popl with thir rsistanc to chang and to nw ways of doing things. Carlsson and Turban (2002) not that th ky issus with dcision support systms (DSS) ar "popl problms". "Popl (i) hav cognitiv constraints in adopting inlignt systms; (ii] do not undrstand th support thy gt and disrgard it in favor of past xprincs; (iii) cannot rally handl larg amounts of information and knowldg; (iv) ar frustratd by thoris thy do not rally undrstand; and (v) bliv thy gt mor support by talking to othr popl (p. 106). Of cours, it is not fair to blam only th markting dcision-makrs for not using markting modls. In many cass, th modls may just not hav bn good nough or thir advantags wr not sufficiny clar to th managr. Givn this stat of affairs, it bcam important to hav mor insight in th rol of ths "popl issus" and, in gnral, in th factors that can block and/or stimulat th adoption and us of markting managmnt support tools. This gav ris to systmatic rsarch (cross-sction studis, fild studis, lab xprimnts, fild xprimnts) on ths issus. Th knowldg acquird can b found in th markting managmnt support systms litratur. "Markting managmnt support systms" dos not just rfr to a collction of dcision support systms and tchnologis, but also to a substantiv fild with 'an mrging body-of-knowldg about th factors and conditions that affct th adoption, us, and impact of markting dcision support tools in organizations. W do not hav nough spac hr to rviw this ara. Th radr is rfrrd to books such as Wirnga and Van Bruggn (2000) and Lilin and Rangaswamy (2004), and to Spcial Issus of acadmic journals such as Markting Scinc (Vol. 18, No.3, 1999) and Intrfacs (Vol. 31, No. 3, 2001). Th most rcnt insights can b found in Wirnga, van Bruggn and Althuizn (2008). 3. Opportunitis for Dcision Support Systms in Markting In this sction w will discuss dvlopmnts that ar favorabl for th dvlopmnt, adoption, and us of markting managmnt support systms in companis. An ovrviw is givn in Tabl 1. Highr quality markting managmnt support systms Mor favorabl usr nvironmnts Th third markting ra Customr rlationship managmnt (CRM) Tabl 1: Opportunitis for Dcision Support Systms in Markting Highr quality markting managmnt support systms At th tim of th arly work in markting modls (Bass, Buzzll, and Grn 1961; Buzzll 1964; Frank, Kuhn, and Massy 1962; Montgomry and Urban 1969; Kor 1971), th knowldg about markting procsss was limitd. This somtims ld to th dvlopmnt of ovrly simplistic modls that wr not vry usabl for markting practic I. SO, mor work was ndd hr. W alrady obsrvd that th us of markting modls in practic rmaind bhind th initial xpctations. Intrstingly, a comply diffrnt situation has dvlopd in acadmic rsarch. Hr, markting modl building or "markting scinc" has bcom on of th dominant aras of rsarch in markting. It looks as though th fild of markting modls "rtractd" from th batfild of actual markting dcision-making to th acadmic halls of scinc. Th focus of acadmic markting modls is mor on dvloping fundamntal insight into markting phnomna (just lik physical modls ar usd to obtain insight in th working of natur) than on immdiat dcision support. It is not always asy to prdict th valu of a particular approach in markting scinc for markting practic. For xampl, Simon (2008) may b right that th arly work on conomtric modls had limitd impact on markting practic. Howvr with today's abundanc of markting data th hlp of skilld conomtricians, statisticians and computr scinc popl is vry much ndd in ordr to turn this data into knowldg for markting action. For xampl, in dirct and onlin markting whr th offrs to individual customrs ar optimizd on th basis of thir purchas historis, conomtric skills ar indispnsabl (Bucklin 2008). To giv anothr xampl, causal modling may not oftn b usd to solv a markting problm for on particular company at on particular point in tim, but it dos hlp to produc gnral markting insights from larg datasts, for xampl about th ffctivnss of customr rlationship managmnt (Rinartz, Krafft and Hoyr 2004). Th modling approach has producd a walth of knowldg about markting procsss and th ky variabls that play a rol in ths procsss. Furthrmor, vry sophisticatd mthods and tools for th masurmnt and analysis of markting phnomna hav bn dvlopd. Ths advancs hav bn documntd in a sris of books that appard with intrvals of about 10 yars: Kor (1971), Lilin and Kor (1983), Lilin, Kor and Moorthy (1992) and Eliashbrg and Lilin (1993). Th ditd volum: Handbook of Markting Dcision Modls (Wirnga 2008a) apparing this spring prsnts th currnt stat-of-th-art. Ovr tim, markting modls hav bcom "dpr", in th sns that mor rlvant variabls ar includd. This 1 For xampl, linar advrtising modls that wr fit for optimization through linar programming, rathr than for dscribing how advrtising rally works (Engl and Warshaw 1964), h, S. o jl ti P SI g' H al A b S' si fr VI l CI if c. t S( aj T T pi I al C( T OJ it 01 d: VI K C( ar kl pi 40 MARKETING JRM ' 1/2008

4 V VIC:;I C:;I ''::::ICI., '-'''''-'1...<''''''''-'' I...,I...A,...,,...,...,.L -J , _.;::J _ _ r s s j " D r p ir r s y d I. s: :r ). n :s n is a- w has mad markting modls mor ralistic and bttr adaptd to actual markting problms in practic. W can dmonstrat ths dvlopmnts by looking at modls for sals promotions. In Kor's (1971) book th discussion of sals promotions is limitd to two pags (47-48), with just on formal modl for finding th bst sals promotion. In th mantim, rsarchrs hav ralizd that sals promotions is a multi-factd phnomnon, with aspcts such as acclration, dclration, cannibalization, catgory switching, stor switching, and many othrs (Van Hrd and Nslin 2008). Similar progrss has occurrd in th modling of othr phnomna in markting, such as advrtising, sals managmnt, and comptition. Also, th procdurs for paramtr stimation hav bcom much mor sophisticatd (from last squars to maximum liklihood to Baysian stimation mthods). So th analytical capabilitis componnt of markting managmnt support systms, i.. markting modls, has significany improvd in quality. This is also th cas for th information tchnology componnt. Using stat-ofth-art IT possibilitis, most MMSS now hav usrfrindly intrfacs, ar asy to us, and ar plasant to work with. As w will s, thy ar oftn comply mbddd in th IT nvironmnt in which a markting managr works. Th situation with rspct to anothr critical componnt of MMSS, markting data, has also improvd dramatically ovr tim. First, scannr data causd a "markting information rvolution" (Blattbrg t al. 1994), and mor rcny this was followd by a scond information rvolution in th form of normous amounts of CRM data, clickstram data, and all kinds of intractiv markting data. Th conclusion is that bcaus of bttr modls, mor sophisticatd information tchnology, and bttr data, th quality of markting managmnt support systms has trmndously improvd. This is a favorabl factor for thir us and impact. MMSS-favorabl usr nvironmnts Thirty yars ago, Lit (1979, p. 23) obsrvd that computrs "ar impossibl to work with" and h forsaw th nd for "markting scinc intrmdiaris", profssionals with good tchnical skills who would ntrtain th connction btwn th computr and th managr. Through th spctacular dvlopmnts in IT, th rality of today is comply diffrnt. Th computr is now th most intimat businss partnr of th managr. Whthr it is in th form of a PC, a laptop, a trminal in a ntwork or a PDA, th computr is comply intgratd in th daily work. A rcnt study among Grman managrs rportd that managrs spnd on avrag 10.3 hours pr wk using information tchnology (Vlahos, Frrat, and Knopfl 2004), i.., about 25% of thir work tim. Th comparabl figur for th U.S. is 11.1 hours pr wk and for Grc 9.3 hours (Frrat and Vlahos 1998). Markting and sals managrs spnd on avrag 8.6 hours pr wk using th computr (a bit lowr than th 10.3 hours ovrall), which maks it clar that for marktrs th computr is now a ky lmnt of th job. Today, a marktr typically has svral databass and spradsht programs availabl that ar usd to monitor sals, markt shars, distribution, markting activitis, actions of comptitors and othr rlvant itms. Such systms ar ithr mad in-hous, i.., by th firm's own IT dpartmnt, or mad availabl by third partis. Providrs of syndicatd data such as Nilsn or IRI, typically mak softwar availabl for going through databass, and for spcific analyss. For th adoption and us of MMSS it is an important advantag that markting managrs ar fully connctd to an IT systm. Whn a nw MMSS is to b introducd, th "distribution channl" to th markting managr (i.., th platform) is alrady thr. In this way, using th MMSS bcoms a natural part of th (daily) intraction with th computr. On stp furthr, markting dcision support tools ar not sparat programs anymor, but hav bcom comply mbddd in othr IT systms that managrs us (s Lilin and Rangaswamy, 2008). In som cass, with vry complx dcision support systms, th dcision makr may nd th assistanc of th systm dvlopr in ordr to obtain valid rsults form th systm (Englk and Simon 2007, sction 5.1). Howvr in gnral this is not advisabl, bcaus it mans a big impdimnt for th us of a dcision support systm. Th ral powr of dcision support systms is that thy ar dircy accssibl to th dcision makr in an intractiv way and ar oprational at th vry momnt that a dcision issu mrgs. For th succss of MMSS, th rlationship btwn th markting dpartmnt and th ITS dpartmnt in a company is critical. Thr ar indications that th powr balanc btwn markting and th firm's ovrall information dpartmnt is changing in favor of markting. In a study among managrs of markt rsarch in Fortun 500 companis, Li, McLod and Rogrs (2001) concludd that markting has an incrasing influnc on th company plan for stratgic information rsourcs and that markting now occupis a "position of powr in th organization in trms of computr us with markting gnrally calling th shots" (p. 319). This is a big chang from th arly days of computrs in companis, whn markting occupid on of th last placs in th IT priority quu, aftr accounting, financ, production, and oprations. Th third markting ra Markting bcam an acadmic disciplin around th bginning of th 20 th cntury. Broadly spaking, w can distinguish thr diffrnt "ras" in markting, in which th typ of data usd for markting dcisions volvd significany. (1) Markting as distribution ( ) In th bginning th focus in markting was on distribution. Rsarchrs wr intrstd in how products go MARKETING JRM. 1/

5 Wirnga, Dcision Support Systms as th Bridg btwn Markting Modls and Markting Practic through th distribution channl from th original producr (.g. farmrs) to th ultimat consumr. Products wr sn as commoditis, anonymous products wnt to anonymous consumrs. Markting was studid at th macro/industry lvl rathr than as a managrial activity of individual companis. Th variabls of intrst and th data that wr usd wr also dfind at high lvls of aggrgation, for xampl: total production of a particular product, total sals, consumption pr capita, avrag consumr pric, tc. (2) Markting as brand managmnt ( ) In th fiftis of th last cntury, aftr th "invntion" of th markting mix, markting changd comply. From a fild that studid intrsting phnomna in distribution channls, it bcam a managrial fild with as its main qustion of how to dtrmin th lmnts of th markting mix in such a way that th total profit (or som othr organizational goal) of th company is maximizd. In this markting ra, markting modls wr primarily markting mix modls, focusing on th rlationship btwn markting instrumnts and sals or markt shar (Wirnga 2008b). Englk and Simon (2007) giv a classification of applications of markting dcision support systms according to th markting mix instrumnts involvd. In thir st of applications pric and product dcisions ar vry important, followd by product lin dcisions, sals forc and distribution dcisions. In th scond ra practically all th information in th markting managmnt support systms of th tim was organizd around brands. In this priod th so-calld scannr data rvolution took plac: obtaining information about brand sals from th scanning of product barcods at chck-out countrs. (3) Markting as customr orintation (customr-cntric markting) (2000-) Markting as customr orintation (customr-cntric markting) mrgd toward th nd of th twntith cntury. Information tchnology mad it incrasingly asy to collct and rtain information about individual customrs. This was not only dmographic information (.g., family stag, ag, and ducation for consumr markting; company siz and industry for B-to-B markting), but also information about thir purchas history, and thir rsponss to markting campaigns. This mans that individual customrs wr no longr anonymous but obtaind thir own idntity. With such information a company knows prcisly with whom it is daling, and can figur out th bst way of intracting with a particular customr. This is a major shift from th prvious ra. Th individual customr has bcom cntral. This dos not man that brands hav bcom obsolt. W can say that aftr th product had lost its anonymity (and bcam rcognizabl as a brand) in th scond markting ra, th third markting ra has also givn th individual customr an idntity. Customr-cntric markting rquirs nw markting mtrics, such as, customr shar, customr satisfaction, and customr liftim valu (CLV). Customr-cntric markting also causs a shift in th 42 MARKETING JRM focus of markting managmnt support systms, whr data ar incrasingly organizd around individual consumrs. In th third markting ra a lot of ffort is put in th dvlopmnt of customr data bass, which ar th starting points for any intraction with individual customrs. According to Glazr (1999) th customr information fil (CIF) is th ky asst of a corporation. From th prspctiv of MMSS, th transition to th third markting ra is a trmndous stp forward. Individual customrlvl data ar an nrichmnt of our information about what is going on in th marktplac. Th nw data has also stimulatd th dvlopmnt of all kinds of nw typs of markting modls, which can b usd to optimiz markting fforts at th lvl of th individual customr (Gupta and Lhmann 2008; Rinartz and Vnkatsan 2008; Bucklin 2008). Th third markting ra has significany incrasd th opportunitis for markting dcision support systms. Customr Rlationship Managmnt (CRM) Customr rlationship managmnt (CRM) has bn calld th "nw mantra of markting" (Winr 2001). Customr rlationship managmnt is an ntrpris approach aiming at undrstanding customrs and communicating with thm in a way that improvs customr acquisition, customr rtntion, customr loyalty and customr profitability (Swift 2001). Th basis for doing this is th CRM systm, a computr systm with a data bas with data about customrs, about company-customr contacts, and data about th customrs' purchas history. Rcny, companis hav bn installing CRM systms at a high rat and a larg numbr of companis now hav functioning CRM systms in plac. Of cours, th larg scal adoption of CRM systms by companis is dircy rlatd to th transition to th third markting ra, dscribd abov. CRM systms ar basically usd for two purposs: (1) To support and optimiz day-to-day intractions with customrs. This is calld oprational CRM; (2) To nabl firms to lvrag on data and find nw markting opportunitis, for xampl, th nd for spcific products/srvics among crtain customr groups, opportunitis for cross-slling, opportunitis for vnt-drivn markting, tc. This is calld analytical CRM. Sinc th vry purpos of a CRM systm is to offr dcision support for th intraction with customrs (oprational as wll as analytical), vry CRM systm is a markting managmnt support systm. Hnc, th advnt of CRM systms implis a quantum lap in th numbr of MMSs in companis. Intrstingly, th companis that ar at th forfront of implmnting CRM systms ar not th sam companis that wr dominant in th dvlopmnt of MMSs for brand managmnt in th scond markting ra. Th CRM movmnt is particularly strong in industris lik financial srvics (.g., banks and insuranc companis), communications, utilitis,

6 VVIt::It::::llya, LJrJL-'.:::)/U VUjJfJU/1. VY..:JLVlliU U<J ",...,...",... :::1..., '..r..., :::1... _ I.,- :r d g a ;- A :s ~, :s.g d IS w )r r 1- :d :la- ir- of of at r :1- ld Jy ks ~S, rcration and travl, whras in th scond markting ra th consumr packagd goods companis wr dominant. Thr ar normous opportunitis for th analysis and optimization of markting dcisions with th data in CRM systms. An xampl of a frquny mployd mthodology is data mining. With data mining a prdiction modl (.g., a nural nt, Hruschka 2008) is traind to larn th association btwn customr charactristics (for xampl, dmographical information and purchas history) and intrsting dpndnt variabls (for xampl, whthr or not th customr has accptd a spcific offr). Onc th modl has bn traind, it can b usd to prdict whthr othr customrs (with known charactristics) will accpt th offr. This tchnology is typically usd in markting campaigns to slct thos customrs from a databas that hav a high probability of accpting a particular offr. Data mining can caus larg savings, bcaus of a bttr allocation of xpnsiv markting rsourcs. Many qustions can b answrd with th inlignt us of th data in CRM systms, such as: which customrs should w acquir, which customrs should w rtain, and which customrs should w grow (Rinartz and Vnkatsan 2008). Today, th intraction btwn companis and thir customrs is incrasingly taking plac ovr th Intrnt. This has cratd anothr sourc of valuabl information: i.., clickstram data that provid information about how customrs bhav on wbsits and about thir information acquisition procsss. In onlin markting sttings, companis can produc tailor-mad offrs to individual customrs, advrtismnt xposur can b individualizd through sarch-ngin markting, and companis can offr Intractiv Consumr Dcision Aids (Murray and Haubl 2008) to hlp customrs with thir choics. To support onlin markting, nw markting modls ar ndd. For xampl, modls for th attraction of visitors to a sit, modls for th rspons to bannr ads, modls for paid sarch advrtising, and modls for sit usag and purchas convrsion (Bucklin, 2008). Th most important advancs in markting modls and MMSS in th coming yars will occur in th domains of CRM and intractiv markting. 4. Dcision Support Systms as th Bridg btwn Markting Scinc and Markting Practic W hav sn that th conditions for succssful markting dcision support systms hav significany improvd ovr th last fw yars, and w xpct thm to bcom vn bttr. This is important sinc dcision support systms fulfill an important rol. W hav larnd ovr th last fiv dcads that it rquirs painstaking work, high quality data, sophisticatd modls, advancd computational capabilitis and qualifid rsarchrs to gt a thorough undrstanding of markting phnomna, which is ndd for markting dcision making. Th markting modling community has shown imprssiv achivmnts in this rspct, as xmplifid by th books mntiond arlir and by th articls in th modl-orintd acadmic journals. Still, compard to th many cnturis of rsarch in physics, w ar only at th bginning of our undrstanding of markting phnomna. W hav also larnd that vn if th knowldg and th modls ar availabl, companis do not automatically us thm for th improvmnt of markting dcisions. Thr ar major barrirs, rlatd to individual dcision makrs and to th organizations in which thy oprat, that work against th implmntation of markting dcision modls in practic. Thr is a big gap btwn th scintists who dvlop th analytical markting tools and th practitionrs who ar xpctd to implmnt and us thm. W all know th hug diffrnc btwn th world of acadmics who lik to analyz and solv problms in a thorough and solid way and th world of managrs whos activitis can b charactrizd by brvity, varity, and discontinuity (Mintzbrg 1973). It is asy to blam th modl buildr for bing mor intrstd in th modl than in its application, and th practitionr for not immdiay mbracing thos wondrful modls. Howvr it is mor ralistic to admit that oftn th implmntation is byond th xprtis, incntiv systms and availabl tim of ithr of ths two partis. An intrfac in th form of a dcision support systm is ndd as th missing link btwn scinc and practic. Th dvlopmnt of succssful dcision support systms rquirs a sparat typ of xprts: popl who undrstand markting dcision problms good nough to s what th managr nds, and at th sam tim hav sufficint tchnical skills to turn modls into working dcision support systms. W might call thm "markting nginrs". Th succss of a markting managmnt support systm is dpndnt on a larg numbr of variabls (Wirnga, Van Bruggn and Stalin 1999), for xampl th typ of organization and its dcision making cultur, th dynamics of its markts, th availability of data, th dcision support tchnology applid (.g. modls, xprt systms or nural nts), dsign charactristics of th systm (usr intrfac, accssibility, flxibility), and how th systm is implmntd (usr involvmnt, top managmnt support, training). It taks thoughtful and dlibrat considration and advancd markting nginring capabilitis to dsign and implmnt a markting managmnt support systm that succssfully bridgs th gap btwn modl and dcision makr in a particular situation. Th xampls of succssful markting dcision support systms mntiond in th introduction of this articl ar vry ncouraging. Basd on th dvlopmnts discussd in this papr, w xpct a furthr growth of ths support systms, in thir availability, thir capabilitis, and thir contribution to th quality of markting dcisions. 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