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I. Eliashberg and C.L. Lilien, Eds., Handbooks in OR d MS. Vol . 50 993 Elsevier Science Publishers B.V. All rights reserved.

Chapter 17Marketing Strategy Models*Yoram (Je rr y) WindThe Wharton School. University o/PennryIuonio. Philodrlyhio. PA 19104. USAGary L. LilienPennsyluanin Stare Uniriersity, Universiry Park, P A 16802, U S A

1. lntrcductionMany of the models and approaches outlined in other chapters of this bookadd ress single marketing issues (promotional spending, pricing, salesforce deploy-ment, etc.) within the context of any organization where othe r factors are assumedconstant. F or the most part, such approache s are 'bottom-up', an d closely akinto the operational philosophy of traditional ORIMS.Consider, in contrast, a large organization with several business divisions andseveral product lines within each division. Marketing plays a number of rolesthroughout that organization.At the organizational level, marketing can provide both perspectives andinformation to help management decide on what the mission of the corporationshould be, what the opportunities of the organization might be, what strategiesfor growth it might have, and how it might develop and manage its portfolio ofbusinesses. The resulting cor po rate policies provide guidelines forde velop me nt ofstrategy at each business division. And, at the lowest level, the managers of eachproduct and /or m arket within each division develop their own marke ting strategieswithin the context of the policies and constraints developed at divisional levels.We use the term strategic management process to describe the steps taken a t thecorpo rate and divisional level to develop market-driven strategies for organizationalsurvival and growth, while we use the term sfrate gic marketing process to refer tothe parallel steps taken at the product and/or market level to develop viablemarketing plans and programs. Thus, the strategic marketing process takes placewithin the larger strategic management process of the organization.Thus, in contrast with many of the approaches outlined in earlier chapters,marketing strategy models must reflect the overall corporate mission of the

'The authors would like to lhank Josh Eliashberg lor his extraordinary ellarts in making this paperh a p p e n and Adam Fein for his assistance.

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774 Y. Wind, G.L. Lilien

Fig. 17.1. A marketing-oriented approach to strategy formulation and evaluation (source: Wind &Robertson CL983,p. 161).

organization. While the domain of marketing strategy models is murky, there areclearly a number of strategic marketing problems that have yet to be adequatelyaddressed with existing models. One purpose of this chapter is to highlight thosegaps and to propose and illustrate some solutions.We take a broad view of the definition of strategy models in this chapter,including management-science models and less traditional process models whichapply to the generation, evaluation and selection of strategic options at (1) theproduct/market level; (2) the strategic-business-unit level (which can include anumber of product/market units); and (3) the corporate level (which can includea number of strategic business units).The models and processes that are often employed in the development ofmarketing strategy and marketing-driven business (and corporate) strategy can bedivided into three sets ofmod els. These are highlighted in Figure 17.1 and include:

(1) A traditional assessment of market opportunities and business strengths,including:(a) analysis of opportunities and threats;(b) analysis of business strengths an d weaknesses.(2) Marketing-strategy analysis including:(c) segmentation and positioning analysis which provide the foundation forthe selection of target segments an d product-positioning;


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Ch. 1 7 . Murketing Stra tegy Models 775(d) opportunity analysis linking the segments/positioning to market oppor-

tunities and business strengthsJweaknesses;(e) synergy analysis focusing on the positive and negative synergies inadvertising, distribution, manufacturing, and so on, among products.segments and marketing-mix components;(f) functional requirements analysis which include the specification of thekey success factors in each segment/positioning and the company'scompetencies and abilities to satisfy those requirements;(gj portfolio analysis, the an alytical core of the process pro viding anintegrated view of the p rodu ct, m arket segments and businesses.(3) Genera tion and eva luation o f objectives and strategies, including:(hj generation of objectives an d strategies;(i) evaluation of objectives and strategies;

(j) implementation, monitoring and control of the program.The range of analytical approaches and models that underlie these ten phaseshighlight the broad scope of m arketing strategy m odels. Lilien, Kotler & Moorthy[I9921 use Figure 17.1 as a framework to discuss seven types of models that aredesigned to overcome the seven key limitations of current marketing strategyefforts. The seven model categories they discuss and the limitations they a ttem ptto overcome a re highlighted in Tahie 17.1. The types of models that Lilien, Kotler

& Moorthy discuss include BRA NDA ID, ADVISOR, the PIM S ROI PAR Model,the Analytic Hierarchy Process, portfolio models, a nd others. Indeed, an informalsurvey we conducted o fa num ber of leading ma rketing scholars aimed at identifyingkey marketing strategy models elicited these models and other models such asASSESSOR [Silk & Urban , 1978; IRI, 19851, BASES [Burke M arketing Services,19841, NE W PR OD [Cooper, 19881, and POSSE [Green, Carroll & Goldberg,19811.Most of these models have been a rou nd fo r at least a decade. They include bothmodels that focus on a specific element of the marketing mix and models that

Table 17.1Seven limitations oitypical marketing strategy and the type olmarketing strategy models that can beused toaddres s theselimitations(source:Ada pted lrom Li lien. Kot ler& Moorthy [1992,pp. 508-5091.)Limitation of typical marketing strategy The modeling solution[Wind & Robertson. 198311. Improper analytic locus2. Functional isolation3. Ignoring synergy4 Shait-run analysis5 , Ignoring com petition6. lgnoi ing $nlr racl ions7. Lack of integrated view

Market definition and market structureIntegration, especially models 01 cost dyn amics(scale and experience effects)Marketing-mixiproduct-linemethodsDynamic models. especially product lice-cycle

analysis modelsCompetitive-analysis modelsProper market-definition modelsIntegrated models including shared-experiencemodels such as PIMS, product-poitiolio modelsand normative resource-allocation models


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776 Y Wind. G. L. Lilienaddress business-strategy issues. The m ost serious problem with these and similarmarketing strategy models is that most of them are not commonly used bymanag ement. O ur work indicates that th e reason for the lack of use includes thefollowing:- The models are not addressing the key strategic issues facing managem ent.-T he models tend to focus on brand strategy and are not aimed a t the higher- levelbusiness and corporate strategies of the firm.-T h e most challenging parts of strategy are problem definition and generation ofstrategic options. Yet, most of the models are of little help in this area.- Many of the models and especially those based on market data, may providesome useful input t o the decision, but d o not facilitate the process of makingthe strategic choice.- Most of the m odels are not 'user-friendly'.- Most of the models d o not a ddress the current key concerns of top m anagementsuch as the introd uct ion of quality, 'reengineering' key processes, becom ingcustomer-driven, time-based com petition, capitalizing on th e enorm ous advan cesin information technology, globalization of customer and resource markets, andthe shift from hierarchical to less hierarchical cross-functional team-empoweredorganizations.

These concerns have led to a growing gap between the supply of marketing-science-based strategy models and the demand for and use of these models.The ga p is especially striking given the advances in marketing science, as evidentin the papers in Marketing Science, Management Science and similar publications.(See Chapte r 1 for some empirical evidence.) In addition, the increasing receptivityand concern by management with the need to become more customer-orientedmakes this gap even m ore difficult to accept.The theme in this chapter is that there are many important OR /M S developmentsin marketing strategy models t ha t' are already available. Tho se developments,despite their low level of utilization, have the potential, once 'reengineered', toenhance the creativity, rigor and value of the marketing strategy process.The c hapte r is organized as follows:Following this section, we provide a taxonomy of strategy m odels an d reviewwhat is currently available in Section 2. That section focuses mainly on what werefer to as 'traditional' ORiMS models. We include several examples of thosetraditional approaches in Section 3. In Section 4, we develop some non-traditionalmodels, aimed at addressing some of the barriers to use, while Section 5 demon-strates the value of some of these non-traditional approaches. Section 6 providesa vision for strategy models in the 2Is t century and Section 7 draw s conclusions.

2. Strategy models: Progress to dateTable 17.2 presents a taxonomy of marketing strategy models structu red arou nd

six key attributes:


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Ch. 17 . M a r k e t i n g S t r a t e g y Models 77 7Table 17.2A taxonomy o l strategy models and assessment o l curren t oRerings

N o Som e effort Significant efforteffort - -imited Broad Limited Broad~ t i l i z a l i o n utilization utilization. utilizationI . Focus

1.1. Specific marketiq-mix componentsI. Segmentation2. Positioning3. Ncw products4. Product line5. Pricing6 . Promotion7 Disirihution8. Advertising9. Salesforce10. Public relations and public

affairs1.2. Integrated marketing program1.3. Business strategy

I . Overall business strategy2. Joint decision with othermanagem ent functionsa. Marketing-operationsb. Marketing-R & D-oper-ations-human resourcesc. Marketing-human resourced. Marketing-finances

1.4. Corp orate strategyI . Portiolio models2. Resource allocation models3. Simulations4. Screening models5. Process models

2. Genyrapliic and industry %rope2.1. Geographic

I . Globa l2. Regional3. Country4. Region within coun!ry

2.2. IndustryI. Coniuiriera. frequently purchased productsb, duisbles

2, Indusi i~al i i lduct i an d services


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778 Y. Wind, G.L. LilienTable 17.2. (cont'd)

.-- -.No Some effort Significant effoneKort

Limited Broad Limited Broadutilization utilization utilization utilization3. Objectives o/modelsA. Prob lem definitionB. Ge neratio n of strategy options

C. Evaluation of stratcgy optionsD. Optimai allocation o i resourcesE. Selection o f strateg yF. Help implement the straiesy

4. Inputs4.1. 'Hard data'I. Externala. Customersb. Competltoisc. Other stakeholdersd. Market performance type *data (PIM S, e tc)2. Intcrnala. Accauntlng, sales, profit

4.2. Incorporate o utcome of formalmarket analysisI. Conjoint analysis2. Brand-choice models3. Mulridimensianal scaling4. Diffusion models5 . Econom elric modeling6. Forecasting:a. Analogies

b. Concept testingc. Pre-test-market modelsd. Test-market modelse . Early sales models4.3. Integrate 'hard' data with manage.

ment subjective judgmentsi . T y p e "/model5.1. (1) Stand-alone vs.

(2) par1 of a iarger system5.2, ( I ) Descriptive v i .12) predictive vs. (3) prescriptive5.3. ( 1 ) Static vs . (2) dynamic (1 )5.4. [I ) Deterministic vs. (2) stochastic (1 )5.5. ( I ) Stand-alone models vs. (I !12) part of D SS5.6. Facilitate sensitivrty analysis5.7. Process model


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Ch. 17. ~MarkeringStrategy Mudeieis 77 9Table 17.2. (cont'd)

N o Some effort Significant efforteffort -- --Limited Broad Limited Broadutilization utilization utilization utilization

6. Th e ourpui-'benefits'6.1 . 'Quality' of the selected strategy

I. Short-term2. Long-term e

6.2 . Speeding up decision-making6.3. Higher likelihood ol successlul *implementation

6.4.Enhance the uniticreate value

-t he focus of the model,-t h e geographic and industry scope of the model,-the objective of the model,t h e n pu t to th e m od el,-the type of model ,-the output of the model .The table also includes our subjective assessment of the current strategy modelson two d imens ions -- the numbe r of models in each category (none, little, or many )an d the degree of utilization of these models (limited or broad).The key mode ls in each o f the categories are identified below a nd discussed inthe context of some general observations about the table.2 . 1 . The ocus o f marketing strategy models2.l.i. Spec& marketing-mix componentsMarker segmentation. The selection of target market segments is (together withthe positioning decision) the found ation for most mark eting pro gram s. Yet thereare few models for the selection of market segments. The segmentation decisionis one of the major meeting grounds between marketing research and modeling,since models used for the selection of target segments requir e consid erab leinformation on the size of segments, their key characteristics, expected competitiveactivities. and expected market response of given segments to the offering of thefirm and its competitors. Amon g the segmentation models used a re norma tivemodels which try to offer prescriptive guidelines [Moorthy, 19841. Also, modelssuch as POSSE [Green, Carrol l & Goldberg, 19811 and the Analyt ic HierarchyProcess ( 4 H P )have been used effectively. POSSE is a decision s up po rt system formaking product design decisions. The approach uses conjoint analysis to identifythe relation between the attr ibut es possessed by a product a nd the desirability ofthat product. for each of a set of potential customers. In a second step, the level


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780 Y Wind. G.L. Lilienof market demand for any poten tial pro duct is estim ated by aggregating theindividual preference models across customers. An optimization routine thenreveals the most desirable product (or products) in terms of some specificmanag ement objective (e.g. maximizing incremental market-share). This objectivemay take into account the presence of specific other p rod uct s in the market an d/o rany cannibalization effect of the new product on specific existing products. Themarket segment most attracted to this optimal new product is identified [Green,Carroll & Goldberg, 19811. Fo r furth er discussion, see Gree n & Krieger 119891.An AHP analysis is especially appropriate when one considers the portfolio ofsegments that product management, the SBU, or the firm wishes to reach. Anexam ple of the use of A H P to select a po rtfolio of segm ents is included in Sect ion 5.Positioning. Given the imp ortan ce of positioning as th e found ation of marketingstrategy [Wind, 19901, it is no t surprising tha t m uch a tten tion has been given tothe development of positioning models. M ultidimensional scaling, clustering andconjoint analysis have been used primarily for positioning analysis [e.g. Wind,19821. Operational models for positioning strategy include multidimensionalscaling and op timization m odels such as POSS E, which h elp to select a product'sbest position and then find the best market segment; or, alternatively, selects atarget segment and then find the product's optimal position. Analytical modelsprescribing target positioning under various scenarios have also been developed[Eliashberg & Manrai, 19921. A H P analysis has also been used t o find the bestpositioning to reach selected target segments. A good review article on this topicis Green & Krieger C19891.New product and product-line decisions. Marketing-science models have beenapplied to the entire range of product decisions from the generation of new p roductideas to the evaluation of ideas. concepts and products, to new-product iaunch,to the management of the pro duct life-cycle, an d finally to prod uct d eletion [U rban& Hauser, 1980; Wind, 19823. These m odels have encom passed all of the m ajormodeling and research developments in marketing. They have been subject tosome of the more creative modeling efforts which inc lude simulated test marketsan d innovative models for new-product design optim ization , product-line decisionsand new-product forecasting models. For review articles of many of the modelssee Shocker & Hall 119861, Wilson & Sm ith [1989], G ree n & Krieger [I9851 andMahajan & Wind 119861.Pricing. Most applied pricing models are aimed at assessing the price-sensitivityof the market. They include experimentation, econometric modeling, conjointanalysis, and a variety of consum er surveys focusing on cu stom er attitudes towardprice, price perceptions and expectations. Most conjoint-analysis models includeprice as a factor, leading to the determination of price-elasticity. More specializedmodels, such as the Mahajan, Green & G ol db erg [1982]. Elasticon models, offerinsights into the cross-elasticity ofdem and and theex pected impact of price changeson brand shares. There is also increasing interest in bidding models, game-theoretic


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Ck. 17 . ~ClarketingStrategy hlodris 781models for competitive pricing [Eliashberg & Jeuland, 19861, quantity discounts[M onah an, 19841, an d identifying the best pricing strategy-not just the price itselfbut a number of associated 'services' such as terms of payment, premiums andlife-cycle costing (Chapter I I) .Promotion. The proliferation of scanner da ta has resulted in a Rood of models tomeasure the effects of sales promotional programs. The PROMOTER model byAbraham & Lod ish 119871, for instance, uses artificial-intelligence technology. Itoffers on-line computer access to evaluate sales promotion programs usingmeasures such a s incremental sales an d p rofit, consumer pull-through, a ndcomparisons with other company sales promotions and those of competitors.Distribution. Channels of distribution have also received attention by marketingscientists, focusing mainly on identifying the best distribution outlets [Rangan,19871. The tre men dous g rowth of direct-marketing activitirc has led to significantmodel ing and research act iv it ies. This modeling is often l i ~ ~d to experimentationand is aimed a t establishing th e m ost effective direct-marketing program.Advertising. Advertising m odels encom pass copy-testing, media selection, adv erti-sing pulsing, cam paig n scheduling, an d advertising budgeting [Burke, Ranga swam y,Wind & Eliashberg, 1990; Ho rsky & Sim on, 19831. Advertising is included in mo stmarket response models where it is used to assess the relative contribution ofadvertising to pr oduc t sales, mar ket share, or diffusion patterns [Eastlack & Rao,19861. Much of the recent development is associated with new research methodsand the design of test m arkets where split-cable technology links with consum er-panels da ta collection and experimen tally assesses t he effect of different advertisingstrategies.Salesforce. Significant modeling h as been do ne in the salesforce area, focusing o nallocations of salespeople to terri tories, territory realignment, frequency o f salescalls, and sched uling ofsales calls [Zoltners & Sinh a, 19831. Salesforce expend ituresare often included a s part of market response models. Analytrcal models have alsoexamined the related issue of salesforce compensation [Basu, Lal, Srinivasan &Staelin, 19851.Public relations and public affairs. Pub lic relations focuses on comm unication withthe desired target segments and oth er external stakeholders. Although this functio nis typically outside the responsibilities of marketing, public-relatrons and public-affairs programs should be consistent with the overall marketing strategy o f thefirm. Modeling activities from the advertising and communication areas could beapplied here.2.1.2. The integrated murketing programAn important modeling area which has had limited usage in practice is themodeling of the entire marketing program. Such models tend to focus on theinteraction am ong the various marketing-m ix variables. BRANDA ID [Little 19751


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782 Y Wind, .L. Lilienis one of the few models that focuses on the entire marketing-mix program. Thismodel, discussed in greater detail in Section 3.1. is a decision sup po rt system w ithmodular components that are developed individually and then put together toform a customized marketing-mix program. Despite its early promise, BR AN DA IDis not commonly used by management.Promising developments in the marketing-mix area include studies and modelsof synergy among the various marketing-program elements and approaches thatallow for the development of an integrated program. These developm ents includethe simultaneous selection of a target m arket segment, desired product-positioning,and the identification of a creative strategic thrust that links these with the restof the marketing program. Th e AH P [Saaty, 1980; Wind & Saaty , 1980; Dyer &Fo rm an , 19911 has been useful for this purpose.21.3. Business strategy modelsOverall business strategy models. Models th at focus o n business strat egy ca n greatlybenefit from a marketing-science perspective. Most notable in this regard are thePIMS-based models - PAR ROI a n d L OO K AL I KE ANAL YSI S - a n d theportfolio models. They are discussed in more detail in the next section.Portfolio by products, market segment and disrribution outlets. O n e of the keydecisions facing any business manager is the determination of the desired portfolioof products by market segment by distribution-outlet-type. This decision involves(1 ) an analysis of the current product, market and distribution portfolio and(2) the selection of the desired portfolio of products, marke t s egm ents and distri-bution outlets. The analysis of the current product, mark et a nd distributionportfolio follows two m ajor a pproaches: (1 ) factor listing and (2) determination oftarget portfolio.Factor listing considers the factors used in making decisions on the width anddepth of the portfolio. Product-portfolio models offer a more structured set ofdimensions on which the current portfolio models can be analyzed. Thesedimensions include market share (as a measure of the business's strength ) andmark et growth (as a m easure of the business's attrac tion), as well as profitability,expected return, and risk. Most models focus on two dimensions-company(pro duc t) capabilities and m arket-attractiveness. Y et, the specific dime nsion s varyfrom one portfolio model to another. They include models with a normative setof dimensions (such as shar e and grow th or risk a nd return) and the m ore flexiblecustomized portfolio models which ideniify dimensions tha t m anage men t considersrelevant.Following an assessment of the existing (and any potential new) products of thefirm on the chosen dimensions, the major managerial task is to decide on thedesired target portfolio. The target portfolio should not be limited only to products.Ideally, it would also include target market segments and distribution outlets.Such a portfolio reflects management's objectives, desired directi on of growth , an dthe interactions (synergy) am ong products. m arket segments and distributionoutlets [Wind, 19821.


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Ch. 17, hlarkering Strategy Models 783Join t decisions with other managem ent functions. A relatively new ar ea of investi-gation involves the development of joint-optimization-typ e models. M ost no tableamo ng theseefforts are som e initial development ofjo int marketing-and-operationsoptimization models, focusing on new-product design [Cohen, Eliashberg & Ho,19921 and pricing decisions [Eliashberg & Steinberg, 19871.

As the acceptance of marketing orientation increases, one would expect theother business functions to include marketing considerations in their functionalplans, and, to the extent possible, utilize appropriate marketing-science researchand models. Yet little progress in the direction has been seen to date.2.1.4. Corpo rate strategy modelsCo rpora te strategy models include portfolio models, resource-allocation models,simulations, so me (venture) screening models an d strategy process m odels.Portjolio models. These include the standardized portfolio models introduced byconsult ing firms such as the BCG growth-share matr ix and the GE JMcK inseymarket-attractiveness-business-strength matrix. Given the limitations of thesemodels (as discussed in Wind, M ahaja n & Swire [1983]), a numb er of custo mize dportfolio models have been developed and employed. These include both modifi-cation of the customized portfolio models as well as specially designed conjoint-analysis-based an d A nalytic Hierarchy Process (AHP)-based portfolio m odels. Keycharacteristics of the custom ized models are their focus on management's criteriafor evaluating strategic optio ns and the focus on th e allocation of resources amo ngthe portfolio elements while offering diagnostic guidance to corporate strategyinvolving the portfolio elements. (We develop some po rtfolio models in Section 3.)Resource-allocation models. Give n the imp ort anc e of prioritizatio n of objectives,strategies and businesses, management uses a variety of resource-allocation models.These range from the use of simple heuristics (such as matching a successfulcomp etitor), throu gh m odels that help quantify manage ment subjective judgm entssuch as the AH P, to optim ization-type models. The more powerful of these modelstend to be based on market-response elasticities. The problem, however, is thatthe closer the model is associated with market response da ta, the less comp rehensiveit is in terms of the other key strategy determinants (such as likely competitoractivities, technology. etc.). There are a number of elegant resource-allocationmodels such as STRATPORT [Larreche & Srinivasan, 1981, 19821. Yet, theirusage is quite limited. (We discuss ST RA TP OR T in Section 3.2.)Sirnu1ation.s. Busine ss simu lation s are quite com mo n. On e of the first ma jor busin esssimulations was designed by A mstutz in the early 1960s [Amstutz, 19671. Yet ithas not been employed widely due to its complexity and unrealistic data require-ments. Forrester [I9611 represents another attempt to employ dynamic simulationmodels to aid strategic decision-making. Today, most simulations are designedand used for education purposes as business games. A significant number of firmsdo use business simulations as part of their business and /or co rpora te strategy. Inrecent years, some simulation s have been developed as games addin g an en tertain-


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784 Y Wind. G.1.. Liiirnment component to the business strategy and educational goals [Reality Techno-logies, 19901.Screening models. Th e increased reliance on extern al sources of business exp ans ion(i.e. icensing, for min g strategic alliances, merging o r acquiring produc ts, businessesor even entire firms) has led to th e development of screening models. Am ong themore pop ula r of these models ar e discriminant analysis on the key discriminatingcharacteristics of 'successful' vs. 'unsuccessful' entities.Most of these mode ls have been developed by firms that were able to put togeth era datab ase on successful vs. unsuccessful pr oduc ts o r businesses. At the produ ct andSBU level, there have also been significant e ffo rts to develop cross-in dustrydatabases . Them ost popular ones are the N EW PR OD model for product screening[Cooper, 19881, and the PIMS database for business screening and evaluation[Buzzell & Gale, 19871.A comparison o f the NE W PR O D cross-industry model with a customizedindustry-specific model developed by a pharmaceutical firm suggests that anindustry-specific approach leads to better predictions. Yet, given the speed andcost at which one can get an answer from one of the cross-industry databases,both types of screening models have their role.Process models. These are the most popular of the models used in the corporatestrategy area. Most strategy hooks [Lorange, 1980; Day, 1986; Aaker, 19921,propose a process-flow model for strategy developm ent. These are often used asblueprints for the design of strategy gene ration and evaluation processes.2.2. Geographic and industry scope2.2.1. Geography

Most of the marketing-science-based strategy m odels are domestic in nature. Anumber of the models have focused on segments and several have been appliedto regions. T he regional focus has received increased attention as a num ber ofmanufacturers of frequently purchased consumer goods, such as Campbell Soup,have restructured their operations along regional lines.The few global models have focused on country selection [Wind, Douglas &LeM aire, 19721, global portfolio of coun tries and global portfolio of countrie s bysegment by mode of entry [Wind & Douglas, 19811.Despite the growing interest in regional blocks (i.e. European Community,NAF TA, etc.). non e of the marketing-science mode ls have focused on the devefop-ment o r evaluatio n of regional strategies.2.2.2. Industry

Most of the brand-specific models have been developed for frequently purchasedproducts, while a few (such as diffusion models Cha pter 6) focus primarily onconsume r durables. W ith the exception ofco~ijoint-ana lysis-based trategy models,which have been applied primarily to industrial and quasi-industrial produ cts such


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Ch. 17 . Marketing Strategy iLIodels 785as pharm aceuticals, little attention h as been given to ind ustrial goo ds an d services.Services, including growth areas such as entertainment and leisure activities, havereceived less attention and, to date, have benefited very little from marketing-science-based strategy models.2.3. Objectices of models

Curre nt marketing strategy models focus on ev aluation o f strategic options an don o ptim al allocation of resources. Little attention has been given to m odels thathelp management define and formulate the problem, that help generate creativeoptio ns, that help select a strategy, or that help implement the strategy, The lattercatego ry has been almost completely ignored in the m arketing-strategy literature.This lopsided focus o n strategy evaluation o verlooks the potential th at marketing-science methods have in helping management in the process of:-Problem def ini t ion: Scenario planning [e.g. Shoemaker, 19911, stakeholderanalysis, SW OT analysis (strength--weakness, opp ortun ities an d threats), market-ing au dit, benchmarking, positioning analysis, an d sim ilar analyses can all help indefining the prob lems facing the firm.- Generation of strategic options: The various approaches marketing scientistshav e been using for the generation of new-product ide as can all be used for thegene ration of strategic options. F or a discussion of these appro ache s, and theirapplic ation to the gen eration of strategic option s, see Wind 11982, 19901. Th emo st powerful of these approach es are morpho logical analysis and stakeholderanalysis.-Selec t ion of a s t rategy: The Analytic Hierarchy Process has been effective inhelping management structure a problem hierarchically, evaluate the various

op tion s on a set of criteria, and make a choice following app ropriate sensitivityanalysis. For a review of AHP-based applications to marketing-strategy problems,see Wind & Saaty [198O],.Dunn & Wind 119871 an d Saaty [1990].- Help in implementing the selected strategy: On e of the major adva ntages of anAHP-like approach is that the participants in the process tend to 'buy in' and

sup po rt the group decision, an im portan t benefit considering the typical difficultyin implementation.2.4. Inputs2.4.1. 'ffcird data'O ne of the unique con tributio ns of marketing science to business strategy is thenature of the inputs it provides to strategy models. Given the 'boundary' role ofmarketing, and its traditional focus on understanding consumer behavior, i t is notsurpris ing that marketing-science-based strategy models emphasize informationabout the consumers. More recently, the scope of marketing has been expandedto include all stakeholders. Figure 17.2 presents the '6C' model which emphasizesthe need for expanding the scope of the inp uts to the marketing strategy models,


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Y.Wind. F.L. 1,ilien

CULTURE AN D OTHER ENVIRONMENTAL FORCESAN D STAKEHOLDERS (including Suppliers.Government,etc)4I 4I t I

COMPANY COMPETITORS / IL-i* IICANDiDATES FO R COOPERATION(Cornpetitots supplters OtQet) k "

Fig 17.2 The 6C model: an expanded view oi the iocus of marketing strategy models.as well as the models themselves from the traditional 2C o r 3C models - he'company -custom er' o r the 'company-customer-com petition' - o all the relevantstakeholders.Many marketing-science models use data on co nsum er behavior generated fromsurveys, experiments, o r secondary d ata services. These include scanner da ta andassociated single-source da ta for frequently purchased consumer products, variousforms of prescription data for the pharmaceutical industry, etc. Whereas most ofthe secondary da ta services include information on com petitors as well, such datais typically at the product level and not the SBU or corporate level.When surveys are used, they often collect data about perception preferencesand reported behavior. Few syndicated d ata services are available to mon itor thebehavior of other stakeholders.At the SB U level, important data services are the P IM S and Federal TradeCommission databases, as well as the databases of D un n and Bradstreet and o therinformation providew. Company-internal data are often important inputs tostrategy models. These data often have significant problems concerning accuracyof profit figures, approp riateness ofan alyt ic unit (i.e. segments, distribution outlets),etc. Internal data should generally be supplemented with external dat a o n relativecompetitive performa nce such as market-share. positioning and customer-satis-faction data .2.4.2. Incorporate outcome i , f , formaimarker nnaiysis

A major advantage of marketing-science-based strategy models is that they canincorporate the outputs of formal market analyses including:


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C h. 17 . Marketing Strategy Models-th e results of conjoint-analysis studies (see Cha pter lo),- brand-choice models (see Chapter 2),- multidimensional scaling (see Ch apte r 5),-diffusion models (see Ch apte r 8),-econometric modeling (see Chapter 9).2.4.3. M anagement subjective judgmentAn important component of all strategy models is management subjectivejudgment. Strategy models vary with respect to the degree of formalism andqualification of management subjective judgment. AHP -based models, for instance,are hased on management subjective judgm ent and incorporate 'hard' ma rket da tathrough management's assessment of the results of available studies. Most otherstrategy models do not explicitly incorporate management subjective judgmentand thus leave management the task of deciding what to do.2 .5 . Ty pe o fmadel

Marketing strategy models include a variety of models that can he classified onseven dimensions:2.5.1. Stan d-alone us. part of a larger syste mMo st current models ar e developed o n a sta nd-alon e basis. Since most decisionsrequire more th an th e out put of a specific model, this may be one of the reasonsfor the relatively poor utilization of marketing strategy models. Consider, forexample, the need t o decide on a pricing strategy for a new product. Models forestimating the consumer price-elasticity, for example, are useful input to thedecision, but must consider issues such as the likely trade reaction, likelycompetitive an d governm ent reaction, an d the implication of the initial pricing o nthe firm's ability to change prices in the future.2.5.2. Descriptiee us. predictive us. normativeModels are often classified hased on their primary objective. Most consumer-hased marketing models have tended to be descriptive in nature. The interest inpredictive models is evident from th e m any forecasting models in use. And MSiORhas always encouraged the development of normative models. The best strategymodels shou ld encompass all three objectives.2.5.3. Static us . dynamic modelsMo st m odels tend to be static in nature. Given the dynamic na ture of business,there is a great interest in dynamic models, which consider factors such as com-petitors' reactions to the firm's strategy; entry of new competitors; changesin government regulations and technology: and changes in consumer demo-graphics, needs and behavior. 'These and other dynamic factors are often dealtwith via simulations. sensitivity analysis. and occasionally by complex analyticalmodels.


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788 Y. Wind. G.L. Lilien2.5.4. Deterministic cs. stochasric modelsMajor developments in stochastic brand-choice models include the inco rporationof marketing-mix variables and the use of these models as a basis for laws ofmarket behavior. The Hendry system [Kalwani & Mo rrison, 19771, for example,partitions an d defines a market in terms of current market-shares a n d a switchingconstant. Based on this, it calculates a par sha re for a new -brand en try an d suggestsimplications for the new brand and its competitors.Despite stochastic model developments and the obvious stochastic nature ofmarketing phenomena, most marketing models, especially those used a t the SBUand corp orate levels, are deterministic in nature. Even m os t new -product diffusionmodels have been mostly deterministic.2.5.5. Stand-alone models us. pa rt of decision suppo rt systemsMany of the early marketing models had a single focus. A number of thesemod els were linked to m arketing decision sup port systems (MDSS) - a coordinatedcollection of data, models, analytical toots and comp uting pow er that help managersmak e better decisions. MD SSs generally replaced mark eting information systems,which often failed because of lack of user-orientation. Use r-orien tation an d friendlymarketing decision sup po rt systems are emerging, but are still qu ite limited intheir diffusion. MD SSs utilize com pute r technology (including personal com puters);artificial-intelligence approaches; management judgments; inputs on market,competit ive and environmental condit ions; and models of the mark et plan. Encour-aging developments in this area include expert systems and their incorporationas part of a decision support system (Chapter 16).2.5.6. Sensitiuiry an alysisGiven the u ncertainty surro und ing m ost marketing strateg y decisions, it is oftenbeneficial to condu ct sensitivity analy sis to assess the sensitivity of the results todifferent assumptions. Simulation -based models and th e A H P are especially condu -cive to sensitivity analysis.2.5.7. Process modelsMo dels of processes such as new-produ ct developm ent or a new-produ ct launch,are com mon. They differ f rom tradit ional strategy models in their focus on theset of activities that should be considered to yield a set of actions. The mostadvanced strategy process models are those involving the various steps in thedevelopment of new products [Crawford, 19911. M or e recently, with th e increasedattention to cross-functional processes, there has been an invigorated search forthe design of processes for speeding up decisions and activities, incorporatingcusto merim arket inpu t in the firm's decision, enhancin g quality, etc. [Kleindorfer& Wind. 19921.2.6. The outpur-bmejrs ojrhe tnodels

Aside from the obvious ou tpu t of any model - has i t answered the question thati t has designed io answer? little attention has been given to the four critical


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.Price-Trade Promotion- SalespersonsPackag e Assortme

.price

.Trade Promotion-Salespersons- Packag e Assortme

SamplingPackageGraphics and Function. ssortmenlFig. 17.3. The BRANDAID view oi the marketing system t o be modeled (source: Little C19751).

where g, ( t ) is the contribution o f brand i (in dollars per sales unit).Fo r a given brand (droppin g the subscript i) , the brand sales rate s ( t ) s expressedas a reference value modified by the effect of marketing activities and other salesinfluence. The structure of the model is:

whereS, = reierence-brand sales rate, dollars per customer per year,

e,(t)= eRect index in brand sales of the sales influence, i = I , . . I(I= number of sales indices).Th e specific submodels a re described next, in turn. In each case, we dr op thesubscript i in e j(r ) for i the particular promotiona! activity because i t will be clear

from the context.


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Ch . 17. Murkrting Strategy ,\$odeis 791Advertising submodel. T he advertising suhm odel st arts with th e brand's sales at areference value and assumes that there exist som e advertising rate that will ma intainsales at tha t level. This rate is called the mai nten anc e or reference advertisin g rate.W hen advertising is ahove reference, sales are assum ed t o increase; below reference,they decrease.Th e dynamics of the process are captu red in the following equatio n:

wheree ( r ) = advertising-effect index at time t,r(a)= long-run sales response t o adve rtising (index),

2 =carry over effect of advertising per period,u(r)= advertising rate at time t in dollars.Ope rationa lly, the advertising rate is the rate o f messages delivered t o individuals

by exp osure in media paid fo r in dollars . Thus ,

whereX(r)= advertising s pendin g rate.h(r)= media efficiency at I.k(t)= copy-effectiveness of I.X,,ir,,k, = reference values of the ahove quantities.

The model can also incorporate a memory effect:

whereri(t) =effective adve rtising a t r ,/!= memory cons tant f or advertising (fraction per period).

Price siihrtiolirl. The price-index suhmodel has the form:

wheree(t) =ef fect of brand price on sh are at ia( t )= \-(r), ' .~, relative price.X(r)= manufiicturer's brand price,r(u)= response function.

Y(r)= price-ending effect.


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792 Y. Wind. G.L. LilienSalesforce submudel. The salesforce submodel is also structured in the form of aresponse function. Salesperson effort is defined as

whereX(t) = salesperson-effort rate, dollars per customer per year,h(t)= coverage efficiency, calls per dollar,k( t )= effectiveness in store, effectiveness per call,a(t)= index of normalized salesperson effort rate.

To account for memory and carryover effects, he following equation is employed:

whered ( t ) = effective effort at 1,B = carryover constant for memory effect (fraction per period).

Finally, the salesperson-effect index includes a carryover (loyalty) constant a, aswell as a response function:

Orher influences. Other influences, such as seasonality, trends, package changes,and the like, can be handled by direct indices. For example, trend can be treatedas a growth rate. In this case a trend would be modeled as

where r(i) is growth rate in period i.Cornperition. In BRANDAID, competition is handled in the same way as directsales effects; each effect (competitive advertising, competitive pricing, etc.) goes intothe model either as an index or as an additional submodel, depending on the levelof detail available.Application. The implementation of BRANDAID can be viewed as the developmentof a decision support system for aiding brand-management decisions. Little recom-mends a team approach to implementation; the ideal team involves an internalsponsor, a marketing manager, a models person on location, and a top-managementumbrella.

Calibration of the model involves two types of data: state data (reference values


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Ch . 17 . Marketing Strategy Models 793of sales, share, product-class sales, etc.) and response information. The former areeasy to ob tain; the latter require a creative blending ofjudgment, historical analysis,tracking (running the model on past data and getting managers to review theresults and, if necessary, refine parameters), field experimentation, and adaptivecontrol (the formal processes of using marketing activities to refine parameterestimates through an ongoing measurement process).Little describes a case, called GR OO VY, for a well-established brand of packagedgoods sold throu gh grocery stores. The m o k l tracked sales well over a five-yearperiod and has proven useful for advertising, pricing and promotional planning.For example, by tracking m onths 72 to 78, analysis made it clear that year-to-datesales were good. However, since most of the year's advertising was spent, most ofthe promotio nal activity was over, and price had been increased, the prospects forthe rest of year were bleak. The brand manager used this analysis to support arequest for additional promotional funds, a proposal accepted by management.This action 'almost certainly would not have been taken without the tracking andforecasting of the model'.In spite of this illustration the rich BRANDAID structure is apparently toocomplex for most managers to use and its operational impact has been slight.3.2. S TRATPORT

As theory and understanding abou t the fac tors underlying effective strategies areemerging, normative product-portfolio models that incorporate those ideas arealso emerging. Th e STR ATP ORT model of Larreche & Srinivasan [1981, 19821is an example of an integrative, normative approach.STRATPORT focuses on the allocation of marketing resources across businessunits; it is not concerned with the allocation of resources within business units.The business units are assumed to he independent of one another - hey share noexperience-curve synergies or m arketing synergies. The model is structured aro undtwo time-frames: the planning period and the post-planning period, common toall business units. Changes in market shares are assumed to be accomplishedduring the p lanning period, while the post-planning period captures the long-termprofit impacts of the strategy implemented during the planning period, and themarket shares are treated as if they had remained constant during this time.Marketing expenditures and changes in working capital follow the evolution ofsales. In the model. the following notation is used. Time is at the end of period t .Flow variables (cost, revenue, production) have a start time and end time. Thus,, ,C , , is the cost from t , to t , and C , s thecos t from 0 o t. Also, T is the length of theplanning period, and S - T is the length of the post-planning period.The driving force behind the model is the set of business-unit market sharesjm,,], i = I , . .. ,N. The problem is then to find m, ,...,m,, to

Amaximize n = 1 , (mTi)i = /

(12)subject to Z , , < I ~ , ~ < Z , ~ ,= i ,..,, N , (13)


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794 Y Wind.G.L . Lilien

wheren = ong-term profit,

Z,,,Z,! =limits imposed by management,F = cash flow need during planning period,A = net cash-flow limit.

Equation (12) represents to tal profit during the planning horizons (in con stantdollars), Equation (13) represents the upper and low er l imits on marke t share, andEquation (14) represents t he cash-flow constraint. In effect, Equ ation (14) is notfixed since the value ofA ca n be affected by borrowing. The constrained optimizationproblem (12)-(14) ca n be solved using the generalized La gran ge multiplier method[Everett, 19681.

We now consider the c om pon en ts of the mod el for a single business unit,dropping the i subscript (business-unit notation). T he effect of ma rketin g investmentduring the planning period is modeled by the market response function:

whereL, U = lower and upper l imits on m, (0 < L < U < l) ,a, B = parameters to be est imated,E = marke ting expenditures.

The evolut ion of market share from m, at 0 to m, at T is modeled as

where

Thus, values of P greater than L Lead to a slow app roa ch to nt,, while values of Pnear 0 lead to a rapid app roac h to ul timate shares.The model assumes tha t industry dem ands are exogenous, given by {&.I,).henthe total production for the firm is given by

where the market share during a period is approximated by its average values.


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Ch. 17. Marke t ing Strafegy Models 795Combining Equations (16), (17) a n d ( 1 8 ) yields:

where k, and k, are constan ts tha t can be evaluated numerically follow ing somealgebra [Larrech&& Srinivasan, 19821.Total costs are driven by the experience curve an d are modeled as:

whereC, = total cost of units sold,

2. = learning or experience constant,,,P, = cumulative production from time of product introduction to end

of planning horizon.A similar expression is derived fo r costs du ring the p ost-planning period.Industry unit price is assumed to fall with industry cumulative experience as:

wherep, =average industry unit pr ice,I = industry cumulative value (in units),p =constant ,= industry learning constant. which potentially changes over time

0).Now following the reaso ning in Equ ation (20), we get

where ,,Q,, is indu stry revenue from s tart time for industry ( t , ) to present (r,). T h eprice set by the firm may be higher o r lower than the industry price, so the firm'srevenue during time period t is modeled as:

where m is ratio of firm's price to industry average price. Revenue during thepost-planning period is modeled similarly.A market share of 171, a t T requires production capacity of


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796 Y. Wind,G.L. LilienIf the current plant capacity is X , and X , < X , capacity-expansion expenditureswill be incurred during the planning period; if X .c X,, then liquidation of excesscapacity can generate a cash inflow. The capacity expenditures corresponding toX are modeled as

whereY = capacity-expansion expenditures,q = cash value of divesting entire current capacity,a,b,y,J=positive constants, with O < y - 6 < 1 .

Expenditures above what is spent (through C, and ,C, in the form of depreciation)during the planning period are expressed as a fraction (8,) of Y: Z = 8 , Y.We also need to adjust C, by an amount A, which represents the depreciationover the period 0 to T of assets acquired prior to t = 0.In general, a change in market share cails for a change in working capital,modeled as a function of revenue in period t:

The change in working capital corresponding to the change in market share isgiven by g, - o.To avoid double-counting the working capital expenses includedin C , , we only take a fraction 8 of g,- go:where G is the additional required working capital.Let V denote the proportion of the firm's revenue spent to maintain marketshare at m,; V is modeled as

where d and e are constants to be determined. The cost of maintaining share fromt t o t + f ( r B T ) i s

and from Equations (28) and (29) , we get

The value of profit from the business unit can now be calculated asn = R , + rRs ) - Cr+ ,C,) - E + ,H,)


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C k . 17. Markrting Strategy Models 797where expressions for terms on the right-hand side of Eq uation (31) are developedabove. Similarly, the cash flow need for the business unit durin g the planningperiod is

where, again, the expressions are given above. an d discounted dollars are used inall expressions. T o account for taxes, we must multiply E quatio n (31) by ( I - taxrate), as we must also do f or E, C and R, in Equation (32).Risk can be handled by d iscou nting business units a t differen t discount rates,reflecting their different risk profiles.Application. Given a specific portfolio strategy, the model described above canevaluate its profit implications and cash-flow needs. In addition, STRATPORThas an optimization module to determine the best allocation of resources amongbusiness units with the maxim um net p resent value over the time horizon, subjectto mark et-share and cash-flow constraints. Th e cash-flow con straint can beev alua-ted over ranges of borrowing activity, if desired. One can utilize STRATPORTto update, via its optimization routine, { M , ) which can be obtained initially bystandard forecasting techniques. For more details of the solution algorithm andan illustrative run of the model, see Larrecht & Srinivasan [198I, 19821.However, as with BRA ND AID , the model's richness and comprehensivenesshas severely limited its use.3.3. Financial/product portfolio models

STRATPORT incorporates risk in an implicit manner. Financial-portfolio-based models deal with risk explicitly. The financial app ro ac h to the portfolio-selection problem assumes that the profits from portfolio items (such as productlines, stocks, bonds, etc.) are random variables, and estimates concerning theirdistribution (subjective or objective) are known . Fu rtherm ore, the rates of profitfor different items m ay be correlated an d hence the need to examine the portfolioitems collectively. Th e expected rate of return on a portfolio is simply the weightedaverage ofthe expected rate sof retu rn ofth eitem s contained in that portfolio, i.e.

where wj is the portion of funds invested in item i, Ri is the expected value ofreturn for item i, m is the total number of items in the portfolio, and g , is theexpected rate of return for the portfolio. If variance is used as the measure of riskassociated with a portfolio, it may be obtained by


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798 Y Wind, G.L. Liiienwhere V# is the po rtfolio v ariance, w j and w, are the po rtions of funds invested initems ia nd j, respectively, and cij s thecovariance between return sofi tem s i and j .The systematic steps that characterize the portfolio selection decision may bestated:

( I ) Determine all possible items to be considered in the portfolio an d ge nera te allfeasible portfolios. The major objective of this step is to specify a finite number (m)of items and gene rate a set of feasible portfolios. Th e nu mbe r of feasible portfolioscan be determined by generating combinational solutions to the equa tion xy=w j= 1within the constraints imposed on the values of wi.(2) Gene rate the admissible (efficient or undominared) portfolios. Th e ob jectivehere is to reduce the large nu mber of feasible portfolios to a smaller nu mb er usingcertain 'efficient' rules. These rules are derived by m akin g certain sta ted a ssum ptionson the nature of the investor's underlying utility function. The reduced numberof portfolios ar e termed efficient, admissible, o r und om inated portfolios. A lthougha number of efficient rules have been proposed in the financial literature, weconcentrate on mean-variance (EV) an d stochastic dom inanc e (SD) rules forgenerating efficient prod uct portfolios.(3) Determine the optima l portfolio from the admissible portfolios. Th e efficientrules provide a m echanism t o divide the feasible portfolios into tw o groups: thosedominated by others and those not dominated by others . The undominated oradmissible portfolios provide a smaller set of alternatives from which the optim alchoice can be ma de by obta ining further information o n the investor's utilityfunction (risk ireturn trade-off).The most widely used efficiency criterion for portfolio sefection is the mean-variance (EV) rule suggested by Ma rkow itz [1959]. Since the decisions abo utinvestment may be viewed as choices among alternative probability distributions

of returns, the EV rule suggests that, for risk-averse individuals, the admissible setmay be obtained by discarding those investments with a lower mean and a highervariance. Th at is, in a choice between the two investments, designated by retu rndistributions F an d G, respectively, a risk-averse investor is presumed to prefer Fto G, or to he indifferent between the two if the m ean of F is as large as the m eanof G and the variance of F (reflecting the associated risk) is no t gre ater tha n th evariance of i.e., if /I, > p, an d a 6 c i . Furthermore, if at least one of theseinequalities is strict, then some investors prefer F to G in the strict sense, and Fis said to dominate G in the sense of EV. In this case, G can he eliminated fromthe admissible set. If only one of the inequalities holds, the selection depends onthe individual 's personal mean-variance trade-off, and neither F nor G can beeliminated under the EV dominance rule. The rule can be applied easily to theportfolio-selection problem by ordering all portfolios by increasing means andexcluding any portfolio i such that the variance of portfolio i is greater than orequal to the variance of portfolio j where i < j.

In spite of its popularity, the mean-v ariance app roa ch has been subject tocriticism as i t requires specific inform ation abou t th e firm's utility func tion andignores information about the complete distribution of the firm's returns.


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Ch. 1 7 . Marke ting Strategy Models 799T o address these concerns, Hillier El9691 has proposed, for instance, the

following approach. Let X j be the ran dom variable that takes on th e value of thenet cash-flow durin g the time period j , where j = 0, 1, 2, ... n. Let i, he the rate ofinterest, commonly referred to as the cost of capital, which properly reflects theinvestor's time-value and time-preference of money du ring the period j . Th e presentvalue, P, of this investment or set of investments can then be defined as

Con sider a set of m proposed investments. Define the decision variable 6, asI, if the kth propo sed investm ent is approved,0, ifthe kth prop osed investm ent is rejected (36)

f o r k = 1 ,2 ..., m.L e t 6= ( S , ,6 , , ...,8,). Assume that the investments can generate incoming(positive) or outgoing (negative) cash flows immediately and during some or allof the next n time periods, hut not thereafter. Let the random variable X,(6) bethe ne t cash-flow during time period j ( j = 1,2, ...,n). Let U(p) be the utility if p isthe realized present value of the approved set of investments. Let S he the set offeasible solutions, i.e. the subset of [616, = O or I ; k = 1,2, ..., m ] whose elementsare feasible decision vectors.The problem tha t can he formula ted to de te rmine 6 6 s so as tomaximize E[U(P(6))],

where

This problem can be reformulated in a chance-constrained programming format.Hillier 119691 provides oth er solu tions o r approximate solutions un der variouscondit ions.Another app roach to the problem is the stochastic-dominance approac h [H adar

& Russell, 19711. Stochastic dominance is a relationship between pairs of proh-ability d istributions; in particular, it involves com parison of the relative positionsof the cumulative distribution functions. Three types of stochastic-dominance ruleshave generally been presented lor decision-making under uncertainty: first-orderstochastic dominance (FS D), second-order stochastic dominance (SSD), and third-ord er stochastic domina nce (TSD ). These rules have been derived by considering


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800 Y. Win d. G .L. Lilien

certain stated assum ptions o n the form of the utility function U. If U', U" an d U"'stand for the first, second and third derivatives of the utility function, the FSDrule assumes that U' 2 0; the SSD rule assumes that U' > 0 and U" 0, and theTSD rule assumes that U'2 0, U" $ 0 an d U" 2 0. Th at is, the F SD rule requiresonly that the first derivative of the utility function be everywhere non-negative.These assumption s are clearly more reasonab le than the assump tions of a quad rati cutility function with increasing absolute risk-aversion implied by the EV rule.These stochastic-dominance rules result in the following:

(1) the FSD rule provides the efficient set of portfolios for all decision-makerswith utility functions increasing in wealth;(2) the SS D rule provides the efficient set of portfolios for the subset ofdecision-makers having increasing utility functions an d risk-aversion;(3) the T SD rule provides th e efficient set of portfolios for the subset o f risk-aversedecision-mak ers with decreasing absolute risk.

The optimal portfolio for the investor can then be determined, based on his!herrisk-re turn trade-off. from am on g the relevant smaller set of admissible choices.Applications. Cardozo & Wind [1985] have suggested a mod ification of therisk-return portf olio mo del which overcomes som e of the difficulty involved inapplying the conventional model of the product-portfolio decisions. They reportan application in on e company whose disguised name isT he M onitro l Company.Discussion with executives revealed that Monitrot's performance was affectedby three distinct sets of factors, corresponding to three distinct markets. On thatbasis, the company's product-market investments were divided into three separatebusiness units which shared some support services, but were independent withrespect to demand.The business unit whose experience is described here contained four productlines, each of which had a different application in a technical market. Theseapplication markets were not related, and could be considered distinct andindependent markets. The four lines shared some production and engineeringfacilities with each oth er and with th e other two business units. Mo nitrol executivesbelieved that resources relinquished by any one product line could he readilyemployed by other lines or units.Using the portfolio app roa ch required 10 managers within the business unit to:(a) forecast earnings for each prod uct line; (b) identify the principal factor s affectingthose earnings, cons truct scenarios aro un d thesefactors, and estim ate the likelihoodof occurrence of each of several scenarios or descriptions of future environments;(c)quantify these estimates and array them in tab leand chart form, then constructan efficient frontier, an d (d) assess the trade-off between risk an d return. Manag ersbegan by forecasting retu rns fo r the four product lines in the business unit d uringthe conling three years (Monitrol's planning period), hased on projections fromhistorical experience and forecast changes in the environment. Managers' initialresponses encompassed a wide range of estimates. They were then ask ed t o specifythe conditions under which the lowest and highest estimates would likely occur.


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Ch. 1 7 . Marketing Strategy Models 801This procedure helped identify several factors on which one set of values would

lead to high returns; another, to tow returns. Managers next selected the majorfactors or 'driving forces' that were most critical in influencing returns. Theyidentified two factors that were likely to account for most of the variation in futureearnings: (1) whether a dominant competitor would attempt to limit the extent towhich Monitrol and other small manufacturers could supply products compatiblewith a new product line it was introducing to replace existing products, and (2) therate at which users accepted new technology pioneered by the dominant competitorand the new products associated with that technology.After managers had described these factors, they were asked to specify rangesof values on each factor that would produce noticeable differences in returns. Theydivided the first factor into three ranges, 'favorable', 'neutral' and 'unfavorable';the second into four, ranging from almost no adoption to prompt conversion ofthe entire industry. On that basis the authors constructed 12 scenarios (three valueson the first factor times four values on the second).Managers were also asked to estimate the likelihood that each of the 12 scenarioswould in fact accurately describe the environment during the coming three years.Managers dismissed five scenarios as having less than one chance in 20 of occurring.After comparing and contrasting the remaining seven scenarios, managers decidedthat the ranges of values they had originally specified on each factor wasunnecessarily detailed, and that two values on both the first factor ('favorable' or'unfavorable' atti tude) and the second ('rapid' or slow' adopt ion) would adequatelydescribe what might happen. This redefinition allowed the authors to reduce thenumber of scenarios to four.Finally, managers were asked to estimate the likelihood that each scenario wouldoccur. Although individual managers' estimates differed somewhat, the group

EfficientFrontier

RiskFig. 17.4. Monitioi 's current portfolio (source: Caidoro & Wind 119851)


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802 Y Wind.G.L. Lilienagreed that there was little reason to consider on e scenario m ore o r less likely tooccur than any other, Managers also concluded that together the four scenariosrepresented a full range of foreseeable outcomes. (Subjective estimates like thesecould - and t o the extent possible sho uld - be supplemented with m arket researchda ta in the fo rm of conditional forecasts.)This information was converted into forecast returns for a three-year planningperiod for each of four investments under four environmental scenarios and thencon verted int o the return-risk cha rt in Figure 17.4, showin g the efficient frontierfor the business unit's current investments.Monitrol managers recognized that investment 3 (control systems) appeared tooffer lower returns than investments I , 2 a n d 4 a n d higher var iance than investmentsI and 4. This information prompted Monitrol executives to examine ways toreduce resources allocated to the control-systems line.Mahajan & Wind 119851 describe the lim itations of the finan cial-portfolioapproach to product-portfol io problems and modif icat ions needed to make theapproach more easily applicable.3.4 . The shared-experience approach: P I M S

The PI M S (profit impact of marketing strategy) project began in 1960 at G eneralElectric as an intra-firm analysis of the relative profitability of its businesses. I t isbased on the concept that the pooled experiences from a diversity of successfuland unsuccessful businesses will provide useful insights and guidance about thedeterm inants of business profitability. Th e term 'business' refers to a strategicbusiness unit, which is an operating unit selling a distinct set of products to anidentifiable grou p of custom ers in com petitio n with a welldefined set of comp etitors.By the m id-1980s. the data base of ab ou t 100 data items per business includeda bou t 3000 businesses from ab ou t 450 participating firms.Perh aps the m ost publicized use of the P IM S data is in the form of the PARregression model, which relates return on investment (ROI, i.e. pretax income:'average investment over four years of dat a) to a set of independent variables[Buzzell & Gale, 19871. Table 17.3 presents that model for the entire PI M Sdatabase.The most widely cited (and frequently challenged) results of the PIMS studiesare associated with market selection and strategic characteristics associated withprofitability: Table 17.4 summ arizes some o f those findings.Firms participating in the P IM S program receive PAR reports for their business,which provides a comparison of the actual return on investment (R OI and RO S)of their businesses and the ROI and ROS (=pretax incomeiaverage sales overfou r years of data ) that PI M S predicts for the business (based on its market an dstrategic characteristics). This type of analysis, showin g the dev iation of actual

RO1 from PAR RO I, yields insights into how well and why the business has m etits strategic potential. Because PIMS bas been the mostly widely publicized andwidely supp orted source of cross-sectional information ab ou t business strategy.the results emcrging from the program have undergone considerable scrutiny.


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Ch. 17 . M urk ei in g S t r u t e ~yModeisTable 17.3'The PIMS profitability equation: multiple regressioneqiiation fo r ROf and ROS (en t i re P INS da tabase )(source: Buve l l 81 Gale [1987. p 2741)

Impact on :Profit iniluences- ROI ROSReal market-growth rate 0.18 0.04Rate o i price inflation 0.22 0.08Purchase concentration 002** N.S.Llnionization. ?; -0.07 -0.03Low-purchase amount:Low importance 6.06 1.63High importance 5.42 2.10High-purchase amount:Low importance -6.96 -2.58High importance -3.84 - l . l l '*Exports-imports, % 0.06' 0.05Customized products -2.44 - 1.77Market share 0.34 0.14Relative quality 0.1 I 0.05New products, 0/ -0.12 -0.05Marke t ing % of sales -0.52 - .32R & D, o i sa les -0.36 -0.22Inventory, Y.:, of sales -0.49 - .09Fined capital intensity -0.55 -2.10Plant newness 0.07 0.05Capacity utilization. 7 ; 0.31 0.10Employee productivity 0.13 0.06Vertical integration 0.26 0.18FIF O inventory valuation 1.30' 0.62R2 0.39 0.3 1F 58.3 45.1Number of cases 2314 2314Note:All coeflicients. erceot those starred, ar e signiiicant

( p


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Ch . 17 . Marketing Strategy Models 805The 'non-traditional' models we discuss are of three types:(1) Models for genefating creative strategic options:- approaches borrowed from the new-product idea-generation area,- meta-analysis;(2 ) Models addressing specific strategy needs such as:- creating a corporate and business vision,- scenario planning,- benchmarking,- portfolio analysis and strategy;(3 ) Models that facilitate integration of 'hard' market data with management

subjective judgments.4.1. M ode ls for generating creatiue strateg ic option s

One of the most ignored areas of management-science models in general, andmarketing strategy models in particular, is the generation ofcreative options. Yet,option generation may have the greatest strategic impact. Sophisticated evaluationmodels are not very useful if applied to a conventional set of 'me too' strategicoptions. Thus, greater attention should be given to the generation of innovativeoptions.4.1.1. Approaches borrowed from the new-product idea-generation area

in deciding how to generate creative strategic options, one can benefit from theapproaches to the generation of new-product ideas.

Most of the approaches for generating new-product ideas can also be used togenerate strategic options. Thus, the approaches listed in Table 17.5 (and discussedin Wind j1982, Chapter 91 should be considered.Table 17.5Approaches t o the generation of new-product ideas (source: Wind [1982])Source Research approach

Unstructured StructuredCons umer s Motiva tion research Need;benefit segme ntation

Focused group interviews Problem detection studiesConsumption system analysis Market structure analysislgapConsumer complaints analysisProduct deficiency analysis

Brainslormhg 'Problerniopportunity' analysis'Synectics' Morphological analysis'Suggestion box' Growth opportunity analysisIndependent invento rs Environmental trends analys is

Analysis of competitive productsSearch of patents and other sources

of new ideas- he R & D process---'Experts'


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806 Y. Wind, G.L. LilienTable 17.6Morphological approach es for generating strategic optionsA. Th e tradilional use of morphological approaches for acnerating new-product ideas l s oum: Adams-[1972. p. 831)Example: Improved ball-point PenA t t r i b ~ t ~

Cylindrical Plarlic Separa te ca p Steel cartridgeAlternstivesFaceled Aiiached CAP N o cartridgeSquare No cap PermanentRetracts Paper cartridgeSculptured Paper Cleaning cap Cartridg e made oi ink

B. Use of t h e m o r ~ h o l o ~ i c a lpproach for aeneratinr! strategic oDtionsMarket Producl- Product and Distributionsegment position ing service offering;,

T o p 20% Price A Outlet 1PerformanceCus tom ers with potential Guaran teedfor to p 20";P ~ a s p e c t swith potential \\ for top 20"; 1 ServicePrevious customers PrestigeCandidates lor deletionOther customers

The lessons from using these approaches in generating new-product ideas are:- generation of new ideas requires both structured and unstructured approaches;-approaches to idea generation should include both internal (decision-makers)and external (consumer, competition, suppliers, etc.) sources;- the more approaches one uses for generating new ideas, the higher the likelihood

of success; andi d e a generation should be conducted o n an ongoing basis.One of the most valuable approaches for generation of creative new ideas ismorphological analysis. Table 17.6 illustrates the morphological approach for the

generation of new-product ideas and shows how the same approach can be usedto generate strategic options.tn both of these cases, the key is (a) the structuring of the problem, (b) theidentification of all possible options for each component, and (c) the evaluationof all possible combinations of options.


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Ch. 17. Marketinq Strn teyy Models 80 7In Table 17.68, marketing strategy consists of the following components:

segments, positioning, product and service offering, distribution outlet, and others.For each of these components, a list of options is generated. For example, thepositioning strategy includes the possibility of price, performance, guaranteedperformance, convenience, service and prestige. Having identified the option undereach of the strategy components, strategic options are identified consisting of apattern of options from each of the components (one from each column).4.1.2. Meta-analysisA second approach to the generation of creative strategic options is reliance onhypotheses one can draw from existing theories, concepts and findings. In thiscontext, any theory, concept or study that suggests a specific relationship betweensome strategic variables and performance can be used as a source of hypothesisfor a similar strategic situation.

N d Slgnllcsnt+ ~ i r m irs

lndwtry DiveialCallon~ r m t ~ u u n e s sdatre Pnca~ n i ~ u o n e r ra h s t c n g xpenseConsumar va lndusfnal Sabs.lndur!ry Capital Invssfmen, I+) RlnYsuslnssr nventory*irmurtry size (*I CNInsr vs Managementfindustry Mvsmsing I+)

indortryimwnr ( tindunry Minimum EBuentScab(+)industry eng graphic D q s r r i a n I+)

*cap,,a, ,"vestment ( I+ f i rm AdVericSng I*)+ ~ a i h e thare I+)r Rersaich a Devalopmenl 1-1

Diueiiv#~caiion1WaUV ol Product & SeMcs (+I"Bnrai int8gia$,on *)coiwiate soaaiRespo"r,bli,w I*) P..no""s(EB

Fig. 17.5. Summary of th e Capon. Farley & Hoenig mela-analysis of the determinanls of financial


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808 Y. Wi nd, G .L. LilienA powerful source of strategic ideas is empirical generalization. These can bedeveloped by either com paring a nd con trasting the findings of available empirical

studies or by conducting a meta-analysis.A recent meta-analysis of the dete rmin ation of the financial pe rformance of firms[Capon, Farley & Hoenig, 19901resulted in the findings highlighted in F igure 17.5,and the following observations:

- High-growth situations are desirable; growth is consistently related to profitsunder a w ide variety o f circumstances.--H avi ng high m arket-share is helpful. Unfortun ately, we do not have a clearpicture of whether trying to gain m arket-sh are is a good idea, othe r things beingequal.-Bigness per se does not confer profitability.- Dollars spent on R & D have an especially strong relationship to increasedprofitability. Investmen t in adv ertising is also worthwhile, especially in prod ucergoods industries.- High-qu ality produ cts an d services enhance performance; excessive debt c an h urtperformance; capital-investment decisions should be made with caution.- We can learn from history - he lack o fm ajo r changes in strength of relationshipsover time in dicates that financial-performance history repeats itself.- N o simple prescription involving just on e factor is likely to be effective. Theresults indicate that the determinants of financial performance involve manydifferent factors. Furthermore, results hint at the presence of strong interactiveeffects am on g variables.These and similar conclusions can serve as useful hypotheses in the generation ofstrategic options.To date, a numb er of meta-analyses have been conducted on published research.The concept and advantages of meta-analysis should not be restricted to suchstudies; much leverage can be gained by applying the idea to the firm's ownexperiences.4.2. ,Models for addressing specific strat egy n eeds

Some specific strategy needs have dra wn attentio n recently, involving such issuesas the creation of a corporate and business vision and benchmarking, as well assome of the m ore established a reas of strategy such as scenario planning andportfolio analysis and strategy.4.2.1. Creating a corporute cisionWith the increased recognition that a vision is a key to the establishment ofcorporate mission and objectives, the demand has increased for an appropriateapproach to the determination of a vision. Figure 17.6 outlines such an ap proac h.This approach is based on three phases:

(I ) Analysis of external environm ent, identifying the expected business en piron-ment a nd the changing n ature of the firm's stakeholders. Given this analysis.


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Ch. 17 . Marketing Stralegy Models

me pcted 8=inessEnvironment and theChanging Nature 01 heFirm'sSlakehaldsnVa l i l a , Pupirationr, Desired0bjeci~v.snd Chic(bmpclendes

DesiredVision

Th c Selected Vision and Misrbn IFig. 17.6. A framework lor selecting a vision and global business concept.

the focus is on the question, 'What type of firm could be successful underthese expected conditions? This wou ld lead to the identification of charac-teristics of the 'ideal' firm.(2) Inter nal analysis of the firm - ts values, aspirations, desired objectives andcore competencies. Based on this analysis, it is possible to identify a n initialvision for the firm.(3) A comparison and contrast of the 'ideal' firm resulting from the externalanalysis and the initial vision resulting from th e internal analysis. The resultof this comparison is a vision which satisfies the requirements of both theexternal and internal analyses.A useful methodology for this problem, described in detail at the end of thissection is the Analytic Hierarchy Process. As in most of the strategy applications

of the AHP , this approach requires input from a group of executives in a structuredbrainstorming session. in this case, the group is typically the CEO and his o r herexecutive committee. This procedure stimulates a broad discussion and evaluationof alternative visions. The evaluation should include both fit with internalcompetencies and aspirations, and appropriateness under the expected businessenvironment.Another major advantage. which is common to most AHP applications, is thebuilding of consensus and 'buy-in' for the selected vision by the participants.The weaknesses of this and most AHP applications is that the quality of theprocess and outpu t depends on the composition of the group an d the willingnessof the top executives to engage in an open discussion. The analytical weakness ofthis approach is that the evaluations are typically made informally and at besteither as a matrix of options by criteria or as an A HP hierarchy [Saaty, 19801.4.2.2. Scenurio planningScenario planning is a commonly used process in strategic planning, and it isincreasingly employed in marketing planning as well.


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.so!reuass Llay!f i a q ~ o 41 jo q x a lo j sueld Loua8u!luo3 jo sa! las r! pue o!leuassLfayy lsom aql l ap un uef d 3!8ateils e s e palu asaid L[[es!dL] s! s!sL[r!ur aql 'ase3iamloj aql uj .rnoso Lem so!~euassa q ~o Lun 18qi Bu!mnsse ~ s a q q plnonn ieq lL S a ~ e i ~ sq l l o .so!i eu ass a q ~o qaea io j L8ale il s lsaq a q l ia q i ~ ao luamdo[anap aq]ie pa~oar!pa q u r ! ~sajold 8u!uue[d aql jo isal a q l ' ( 2 . ~ 1 ina!!j aas) siaplo qaye lsiuenalai [ [e uo s!seqduia ale! idoidde ai ow o] siamnsuo s uo snsoj morleu e uo r j~ da su os u !layrew aq i jo uofsua lxa ue s! s ! q ~ spaau i!aql pue ssau!snq aql I3 a pues ley] s ia p lo qay e~s q i uo snoo j aq] s! ssaso id s!q~ o a8e iuenpe lo iem a q l

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=!~r!m!ssad =! ~s !u ~! id o onh smploqa3eir La? 01 palela, se~sal*: I S O N s n l s l ~ s ~ a i ~ e jn a ~ ~ n nax

so!nuaas aaq i lapun siapjoqaqelsaqljo io!neqy/sanjl~aiqolspaau ay a q l

13npaxda1~3-qljeaqe lo, uojlsnllsuo3 o!lcua2s uaA!lp-8u!iaqlem E loj qlofialueq \iL L I


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Ch. 17 . .Markuting Stra tegy Models 81 1The difficulty in implementing this approach is its dependency on informationabout the stakeholders' needs/ohjectives and their likely evolution under apessimistic or optimistic scenario. Stakeholder surveys are helpful here and canbe effectively supplemented by available secondary information and insights ofvarious members of the firm who have regular conta ct with the various stakeholders.Fo r further discussion and illustration of this approa ch, see Shoemaker [19911.

4.2.3. BenchmarkingTh e increased interest in qu ality programs an d the widely publicized success ofthe Xerox benchmarking initiatives [Kam p, 19891 have drawn attention tobenchmarking as a strategic tool.Figure 17.7 outlines design guidelines for a benchmarking system. The basicbenchm arking process involves:(1) T he selection of the facto rs for benchm arkin g. Th ese shou ld reflect the firm's

key success factors.(2 ) Th e selection of a benchmarking target. W ho d o we want to compare our-selves to - best in industry vs. best in any industry and best in our countryvs. best in the world?(3 ) The development of a measurement process and collection of data on thebenchmark target and the firm's own operations and position.Once the gap, if any, between the benchmark target and the firm has beenestablished, there are three additional steps that have to be undertaken:(4) Analysis of the results and the development of astrategy that could lead tomoving the firm's positio n closer to the po sition of the best in the class.(5) A link of the results to the rewa rd an d co mp ensatio n system of the firm.( 6 ) A link of the process to t he ongoing data-co llection a nd m onitoring activities

of the firm and incorporation as part of the DSS of the firm.Many of the benchmarking applications undertake the first three steps without

any rigorou s process fo r the determ ination of the key success factors, the selectionor target benchm ark, and the development of the measurement instrument.Marketing-science models and processes could be used in all six phases.The following marketing-science approaches could be used for each of the sixsteps:S tep I . Selection of benc hm arking factors. Key success factors can be identifiedusing discriminant, regression, or logit analysis on the characteristics of successfulvs. unsuccessful firms. This c an be don e using av ailabl e cross-sectional databa sessuch as PlMS or any other available data.S t e p 2 . Selection of benchmarking target. The firm should sample a broad

universe of successful firms or develop models to identify firms similar to a targetmodel ('ideal' firm).S tep 3 . Development of a measurement process and data collection. Multi-dimensional scaling, clustering an d related a pproa ches used in positioning analysiscan be employed in a benchmarking study.


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81 2 Y Wind , G.L. Liiirn

What is the external bustnesrcnvuonment and what are thecharacttnstlcr of o g m n o n swhich vnll be able to succeed inth s environment7

What a ou r corporate msrianand objef tiv s and what unpli-canons d o 6 11have to the p cof organlzanonwc have to

I- A. CriticalSucccs Fanont18,Can thecritical succsr faston begrouped and can *prioritize them?I ask: Who do we uant to cornpan I

positioning of you and your referrneepoint(s) on each of the criticat ~uc cessfacton.

4A. Anaiyrc the d t s nd asesr: 6. Link the pmc s s to the DSS ofWhat d a s he bcrt in clmr the firm to as- continuousdo thatm don't?What can we do to becomethe be t in class?

48. Link conclusion to the Strategic

Fig. 17.7. Design guidelines for a benchmarking system

Srep 4. Generation and evaluation of strategies to close the gap between thefirm and the benchmarking target . The approaches used for generating optionsand evaluating options can be employed in the benchmarking area as well.Step 5. Link to the reward and compensation. Marketing-science models ofcom pens ation (see Ch apt er 13) can be extended to include activities leading to the

accomplishment of the benchmarking targets.Srep 6. Link to the monitoring and data-collection activities and the DSS ofthe firm. This area can benef,: from advances in marketing research, modeling a ndDSS development in general.


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Ch . 17. itfarkrting Strategy Models 813To date, only partial application of these models and approaches to thebenchm arking area have been condu cted. Resistance to employing the appro aches

has stemmed from the added cost an d time these approaches require. Yet, the few(and unpublished) partial applications suggest that the benefit of a more rigorousprocess may be worth the added cost and effort.4.2.4. Portfolio analysis and strategyAs illustrated in Figure 17.8, product portfolio models can be divided into foursets -stand ardize d models, customized models, financial models and hybrid ap -proaches. (For a detailed discussion of the models, see Wind, Mahajan & Swire[I9831 and for a review see Lilien, Kotler & Moorthy [1992].)Standardized models. These are useful way

62 - marketing strategy models

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