82 - Marketing Engineering Imperative

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The Marketing Engineering Imperative:Introduction to the Special Issue

A we enter the new millennium, weare at the end of the era when firmscould gain and sustain competitive advan-tage merely by having market data. Todaylarge firms have access to more marketand customer data than they can use. Hav-ing too much data without the models andsystems for discovering what is importantand what can be discarded can be as bad(or worse) than having too little data. In-deed, at least one survey Wierenga andVan Bmggen 2000, p. 51 found that man-agers reported that they were rece~vingn-creasing quantities of information and halfof those interv~ewed eported that theywere unable to cope. Those managers re-ported ill health and worsening personalrelationships, along with other symptoms,such as paralysis of analytic capacity, in-creased anxiety, self doubt, and a ten-dency to blame others. Increasmgly mar-keting managers are being asked to clearthe same budget-justification hurdles im-posed on other types of investmen& firmsmake. It is not surprising, therefore, thatmore managers are seeking help in turn-ing their data and knowledge into im-proved decision making.

Over the years, the marketing field hasproduced a number of successful decisionmodels and decision-support tools that fa-cilitate sophisticated thidang about mar-ketlng problems. Several have been suc-cessful Edelman Prize entries, such asGensch, Aversa, and Moore's [I9901use ofchoice models at ABB Electric, Lodish

- ~~ ~~ ~~ ~et al.'s 119881 work at Syntex Labora-tories, and Wind et al.'s [I9891use of con-joint analysis to design the successfulCourtyard-by-Marriott chain. These appli-cations represent what we call marketingengineering: the systematic process of put-ting marketing data and knowledge topractical use through the planning, design,and construction of decision aids and mar-keting management support systems.

Until recently, the models and the ap-proaches that marketing engineering em-braces have been available only to thosefew managers in large organizations whohave had strong motivation to access themand a large budget to support them. Withthe wide availability of computers on'net-works and emerging user-friendly software,however, organizations increasingly applythese modeling approaches. We have dis-cussed elsewhere W e n and Rangaswamy1998; Lilien and Rangaswamy 2000; andLilien, Rangaswamy, Van B~ ggen ,ndWierenga forthcoming] a number oftrends that are enhancing the ability of or-ganizations to benefit from the marketingengineering approach. Those trends are allassociated with wider availability of hard-ware, software, and networking and thedecentralization of both corporate knowl-edge and corporate decision making. Yet itis largely the IT and systems builders whoare getting credit for leading that trend,oftentimes reinventing marketing engi-neering methods or failing to use readilyavailable and well-tested approaches.

Copyright 0 ZOUl INmRU7 MARKE1'1NGOWZ-ZIO~/OII~I~~ISWIISUS.LXI DECISIOXANALYSIS-SYSTEMS

INTERFACES 31: 3, Part 2 of 2, May-June 2001 (pp.S1-S7)


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INTRODUCTIONare delighted to have coaxed some of thebest people in our field to write for this is-sue, in spite of their hyper-busy schedules.Most authors have academic affiliations,but a l l are marketing-engineering practi-tioners. The articles that we have beenprivileged to put together appear in thefollowing order:

Prabha Sinha and Andy Zoltners (M)tell the compelling story of 25 years ofbuilding and implementing over 2,000sales-force models with several hundredclient organizations in over 50 countries.They provide rich and profound insights,particularly in the domain of implementa-tion; they point out how their own beliefsabout the role of models in support ofbusiness decisions have evolved from amodel-dominated perspective to a morebalanced view:"Our belief in the power of models is sti l lstrong, but our appreciation of the softer ele-ments of problem solving and change manage-ment is much stronger. Models help shapejudgment and judgment helps insure that mod-els are implemented successfully. The combina-tion of modeling and judgment can be verypowerful."We couldn't agree more.

Len Lodish (C) provides a catchy title:"Building marketing models that makemoney." And he backs that up with sub-stance. The application at Syntex Labs thathe described [Lodish et al. 19881won theEde ha n Prize and returned over $25 mil-lion to the company on a $30,000 invest-ment, an attractive ROI indeed. Len hashad extraordinary experiences in buildingsuccessful models, including some inwhich he relied primarily on judgment forparameterization (at Syntex and at UnitedAirlines, an earlier Edelman Prize finalist

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[Fudge and Lodish 19771) and some inwhich he relied primarily on empiricaldata. He summarizes the lessons as fol-lows: "Do the best with what you have."That philosophy means that marketing en-gineering, echoing Sinha and Zoltners,must blend art with science and recognizethat "good," while not as good as ' k t , "is much better than "fair" or "poor."

Paul Green, Abba Krieger, and JerryWind (M) tell the story of 30 years of con-joint analysis, one of the most popular andpowerful marketing engineering tools(and the featured methodology in Wind etal.'s 119891 contribution to the EdelmanPrize competition). They describe the basicideas behind conjoint analysis and theevolution of the conjoint platform of meth-ods. They also give brief summaries ofhow conjoint led to the development ofthe Courtyard by Marriott and the EZPass system, showing the interplay be-tween theory and practice. Most of~theni-tial developments in conjoint analysis oc-curred in academia, but the impetus for itscontinuing refinement and enhancementscame from the requirements of practice.Conjoint analysis will continue to be aleading weapon in the marketing-engineering arsenal for decades to come.

The next article is Ed Brody's (C) com-mentary. He reflects on the evolution ofmarketing applications within INFORMSand how the marketing science function atBBW developed three influential applica-tions. He firmly believes that the best ofmarketing engineering lies ahead.Frank Bass, Kent Gordon, TeresaFerguson, and Mary Lou Githens (A)describe the planning and launch ofDIRECTV. They discuss the use of the


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LILIEN, RANGASWAMYBass 119691model, a major and popufarmarketing-engineering tool, as the core oftheir forecasting tasks. In line withLodish's comments, no hard data can beused to calibrate that model before launchwhen the forecasts are needed. So they re-lied on a combination of managerial judg-ment and customer surveys to drive the"Do the best with what youhave."calibration. The documentation of the pro-cess that led to the model's successfulim-plementation shows how much can bedone with careful modeling when fewhard data are available but managers s u pport the marketing-engineering process.

Pete Fader and Bruce Hardie (A) intro-duce a new forecasting model in ane-commerce environment, studying repeatbuying at CDNOW. They model the un-derlying stochastic process to capture non-stationarity in repeat-buying. Remarkablythey show how this rather rich model canseparate the trial and repeat componentsof an aggregate sales series and yet caneasily be implemented in a standardspreadsheet environment. They show thevalue of models for generating excellentmedium-range sales forecasts and demon-strate the applicability of marketing-engineering concepts in emerging areas.

Josh Eliashberg, Sanjeev Swami, ChuckWeinberg, and Berend Wierenga (A) bringa hicontinental team to an application ofSilverSaeener, a marketing-management-support system (MMSS).They applied thesystem, designed to help theater-programming managers in their task ofoptimally choosing movies for their lim-

ited screen capacity, to help a movie dis-tributor in the Netherlands. The applica-tion of the model (in conjunction withmanagerial judgment) led to significantoperating improvements and to manage-rial satisfaction at the movie chain. Theauthors provide a number of useful com-ments on implementation success, all ofwhich complement the theme of the bal-ance between models and managerialjudgment that other contributors haveemphasized.

Berend Wierenga returns with GemtVan Bruggen (C) to describe the issues as-sociated with buildmg a successfulMMSS,BRANDFRAME, for a fast moving con-sumer goods company in the Netherlands.They emphasize the need for system cus-tornization, the need to ensure that systemusers remain in charge, and the recogni-tion that different types of MMSSs areneeded to support different decisionmakers.

Murali Mantrala and Surya Rao (A) de-scribe an MMSS called MARK, designedto assist fashion-retail buyers to reviewand mark down individual items opti-mally during the season. The system pro-vides an integrated resolution of the twopreseason planning issues that a fashionbuyer typically faces, namely, how largeshould the order quantity be and what isthe anticipated markdown plan and bud-get. As the season progresses, MARK up-dates the initial demand forecasts anddetermines the optimal ttming and magni-tude of markdowns based on actual salesand inventory positions. Mantrala and Raoalso present a case study that comparesthe profit impact of the system to that ofseveral more conventional policies; more

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INTRODUCTIONimportant, they show how markdowns,widely viewed as mechanisms to correctmistakes, are exactly the type of mecha-nisms needed to cope with the unpredict-able world of fashion goods.

Dennis Gensch (A), Edelman Prize win-ner in 1989for his work with ABB Electric,closes this special issue by reflecting on hiswork on a new product-planning modelfor a manufacturer of heating and coolingsystems. Remarkably this model has re-mained the core of the firm's product-planning process for over 25 years. Genschascribes the success of the model over timeto the dose relationship between themodel builder and the managerial usersmdeveloping the model stntdure and ingenerating the necessary judgmentalinputs.Creating the Future: A CaU for MoreResearch on Marketing EngineeTing

So what is ahead for marketing engi-neering?A profound change is underwayin how knowledge is being generated.Ever since Edison established h s mdus-trial lab at Menlo Park in 1876, organizedresearch, whether at research labs, at aca-demic institutions, or at private thnk-"Let knowledge come from allquarters."--tanks, has played a central role in generat-ing new knowledge. ~ow eGer ,he emer-gence and growth of the Internet i~ decen-tralizing the generation and disseminationof knowledge. Consider the human ge-nome project, which involved scienhstsand practitioners in 16 instifutes worid-w d e in government, universihes, and theprtvate sector, all collectively contributmg

to the generation, synthesis, and dissemi-nation of knowledge. The Internet speededup data and information sharing amongmembers of this group and both contrib-uted to reducing the duration of the pro-ject by several years and speeded up ap-plications of the gene database madepossible by the project.

We make three projections about theknowledge-generation process:

(1) The time between the origination ofan idea and its application will continue toshorten,

(2) Knowledge advancement will accel-erate as researchers undertake problem-focused activities (for example, mappinghuman genes or searching for bandwidthimprovements on the Internet), and13) Previously independent entities in

academia, industry, and government willfind it increasingly more effective to worktogether than to work independently.

The development of new marketingknowledge will be driven by these trends.Newly published concepts and theorieswill get tried ever more rapidly by practi-tioners looking for an edge. And newtypes of data generated by industry willtrigger almost immediate in-depthsearches for new insights, theories, andmethods within academia. The boundariesbetween theory and practice blur as mar-keting academics increasingly engage inpractice through consulting work or in-dustry advisory boards, becomingscientist-consultants and consultant-scientists. The articles in this issue arefrom such authors.

The ancient Indian text Rig Veda [verse1.89.11proclaimed: "Let knowledge comefrom all quarters." NEWmarketing knowl-

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LILIEN, RANGASWMYedge is generated not only through con-templation, experimental manipulation,and empirical analysis, but a lso throughsystematic attacks on real business prob-lems. Increasingly, the process of applyingknowledge itself triggers the generation ofnew knowledge.

We expect and hope to see more re-search at the interface between marketingtheory and marketing practice, that is,more work on marketing engineering.Both scientists and praceitioner-consultantslike challenging and important problems.But consultants have neither the time northe economic incentive to create new solu-tions, while scientists do. Marketing saen-tists will increasinglybe aided by practi-tioners in selecting problems and testingsolutions. Promising academic solutionswill be ever more quickly tested in theyears to come.

We will all be better off when we blendour skills. With apologies to HerbertSimon, "We have to humanize the saentistand simonize the humanist." In doing so,we can expect to see more effectivemarketing-engineering applications andmore exciting marketing-academic break-throughs than could be produced by ei-ther group alone.Acknowledgments

We editors appreciate the invitation thatEditor-in-Chief Teny Harrison extendedto us; we hope that the results do not dis-appoint. We thank the referees below fortheir work in improving the papers: theauthors were generally astonished at re-ceiving constructive and supportive re-views. And to press the constructive pointa bit, the authors marveled at the energyand persistence of Mary Haight, scourge

of jargonsand the passive voice. Thoughsome small pockets of resistance remain,the papers are, as is the norm for Mary'swork, accessible. Mary Wyckoff keptthings running on schedule by pesteringboth authors and referees and allowing usto meet deadlines. Thanks to all!

RobertJ. Dolan, Harvard University;Peter S. Fader, University of Pennsylvania;Sunil Gupta, Columbia University; JamesD. Hess, University of Jllinois; Sidney W.Hess, Chadds Ford, Pehnsylvania;Aradhna Mrishna, University of Michigan;Lakshman Krishnamurthi, NorthwesternUniversity; James M. Lattin, Stanford Uni-versity; Leonard M. Lodish, University ofPennsylvania; Vijay Mahajan, Universityof Texas, Austin; John M. McCann, DukeUniversity; Donald G. Morrison, Univer-sity of California, Los Angeles; FrankMulhem,Northwestern University;Narakesari (Das) Narayandas, HawardUniversity; Stephen M. Shugan, Universityof Florida; Joel H. Steckel, New York Uni-versity; Gerrit Van Bmggen, Erasmus Uni-versity, Rotterdam; Charles 0. Weinberg,Univenity of British Columbia; BerendWierenga, Erasmus University, Rotterdam;Dick R. Wittink, Yale University; and FredS. Zufryden, University of SouthernCalifornia.ReferencesBass, Frank 1969, "A new product growth

model for consumer durables," ManagementScience, Vol. 15, No. 5 (January),pp. 215-227.

Fudge, Wiam K. and Lodiih, Leonard M.1977, "Evaluation of the effectiveness of amodel-based salesman's planning system byfield experimentation," Inferfaces,Vol. 8,No. 1, Part 2 (November),pp -97-106.Gmsch, Dennis H.; Aversa, Nicola; and Moore,Steven P.1990, "A choice modeling market-ing information system that enabled ABB

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INTRODUCTIONEleckic to expand its market share." Inter-faces, Vol. 20, No. 1 (January-February),pp. 6-25.Leeflang, Peter S. H. and Wittink, Dick 2WO,"Models for marketing decisions: Postscrip-tum," nternational fournal of Research in Mar-keting, Vol. 17,Nos.2-3, pp. 237-253.Lien , Gary L. and Rangaswamy, Arvind 1998,Marketing Engineering: Computer AssistedAnalysis and Planning, Prentice Hall, Engle-wood Cliffs, New Jersey.LiIien, Gary L. and Rangaswarny, h i n d 2000,"Modeled to hits. Decision models for thedigital, networked economy," Comment on"Building models for marketing decisions:Past, present and future" by Peter S. H. Lee-flang and IFick R. Wittink, International lour-nal of Research on Marketing, Vol 17, Nos. 2-3,pp. 227-235.

Lilien, Gary L.; Rangaswamy, Arvind; VanBruggen, Gerrit; and Wierenga, Berend forth-Coming, "Bridging the marketing theory-practice gap with marketing engineering,"Journal of Business Research.Lodish, Leonard M.; Curtis, Ellen; Ness,Michael; and Simpson, Kerry M. 1988, "Salesforce sizing and deployment using a decisioncalculus model at Syntex Laboratories," Inter-

faces, Vol. 18, No. 1 (January-February),pp. 5-20.Wierenga, Berend and Van Bruggen, Gerrit2000, Marketing Management Supporf Systons;Principles, Tools and Implem entation,Kluwer,Boston, Massachusetts.Wind, Jerry; Green, Paul E.; Shiiflet, Douglas;and Scarhrough, Marsha 1989, "Courtyard byMarriott: Designing a hotel facility withconsumer-based marketing models," Inter-faces, Vol. 19,No. 1 (January-February), pp.25-47.

Gary L. LilienA m in d R a n ~ a s wa m y

Smeal College of BusinessPennsylvania State University

Un ive rsi ty Park, Pennsylzlanin [email protected]

[email protected]

May-June 2001

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82 - Marketing Engineering Imperative