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Application of Operations Research to Personal Selling Strategy DAVID B. MONTGOMERY and FREDERICK E. WEBSTER, JR. Effective a p p l i c a f i o n of operations research to selling strategy offers significant opportunities for understanding how the market responds to sales effort and for better strategy decisions. Lack of progress to date may have created premature disillusionment. New information technology will create new needs and opportunities for applying increasingly realistic OR techniques to selling strategy. Joumcl of Marketing. Vol. 32 (January, 1968), pp. 50-57. ESPITE the fact that personal selling is the largest item in most fii-ms' marketing budgets, it continues to be one of the D most elusive and poorly understood elements of the marketing program. Significant analytical insights into personal selling strategy have come very slowly compared with other elements of marketing strategy such as advertising, pricing, and product development. New understanding of personal selling and more effective selling strategies may result from combining the manager's intuition and experience with the analytical rigor of the behavioral scientist and operations researcher. Operations research, in particular, seems to have unrealized potential for developing and appraising sales strategies. The purposes of this paper are to: (1) analyze the reasons for slow progress in applying operations research (OR) to personal selling strategy. (2) structure the selling strategy problem in a way which suggests the potential for OR applications, and to review briefiy some previous applications in this context. (3) outline current developments in marketing information systems and in operations research technology that will both require and facilitate the use of the operations researcher's skills by the sales manager. Management Uses of Operations Research The term "operations research" describes a problem-solving approach characterized by the use of mathematical, statistical, and economic descriptions (models) and analytical techniques. The objective of most OR studies is to develop "decision rules" for evaluating the relative profitability of alternative courses of action in decision problems characterized by complexity, conflict, and uncertainty. OR also involves the analysis of relationships which are likely to influence the future effects of management decisions.^ The field covered by OR is characterized more by its techniques than by the problems to which it has been applied. Among the most frequently applied techniques in the OR tool kit are: linear, integer, and dynamic programming; simulation; critical path analysis; game theory, input-output analysis; queueing theory; Markov chain analysis; and statistical (Bayesian) decision theory.- The application of these techniques to decision problems often requires that ' Harvey M. Wagrner, "Practical Slants on Operations Research," Harvard Business Review, Vol. 41 (May-June, 1963), pp. 61-71. 2 For an introductory treatment of operations research techniques, see Frederick S. Hillier and Gerald J. Lieberman, Introduction to Operations Research (San Francisco: Holden-Day, Inc., 1967). 50 Application of Operations Research to Personal Selling Strategy the problems be structured in particular ways. If this restructuring of the problem preserves the essential features of the problem as it exists in the real world environment, OR techniques can yield optimal or nearly optimal decisions consistent with the decision maker's stated objectives. At the heart of the OR approach to management problems is the use of models—abstract representations of the relationships among the important variables in the decision environment. To understand the opportunity for applying operations research to selling strategy problems, it will be helpful to review briefiy the major uses of models in management decision problems. Models serve several functions in management decision problems: (1) Developing an understanding of the decision environment (2) Providing a basis for measurement of important variables and relationships (3) Transformation of data into understandable and usable form (4) Prediction of changes in the environment and of the consequences of alternative courses of action (5) Control of the activities of the firm (6) Decision making and the formulation of policy The development of models requires management to analyze and structure its decision problems systematically. This structuring alone is often a prime benefit of the OR approach because of the enhanced understanding of system relationships which the manager may gain from this formal approach. Assumptions and decision premises are brought out into the open where they may be subject to review and empirical verification. Models may also provide a framework for measurement. They can assist the decision maker in identifying the information he needs and in the analysis and interpretation of data. Models can be especially helpful for transforming and summarizing into more usable form the large amounts of raw data generated by computer-based information systems. Models can provide the basis for prediction, most often "conditional" prediction. In conditional prediction, a model is used to predict the likely outcome of a particular course of action conditional upon a particular set of assumptions about such factors as competitive activity and how customers respond to marketing effort. Models may also play a role in managerial control. For example, models may be used to monitor performance and to identify the occurrence of an "exception" in an exception reporting scheme. If a control variable, such as sales as a percentage of quota, falls below a set standard, the model may indicate this fact and prescribe appropriate corrective action. Finally, models may be used to help make deci- 51 sions and formulate policy. They often provide a framework within which policy may be set so as to optimally fulfill some goaKs) of the firms. The optimality of such policies is, of course, subject to the validity of the model itself. Models can be particularly useful in developing decision rules of the form "if event X occurs, then take action Y" for alternative values of X. In addition to the specification of the relationships between variables, the use of models also requires data inputs. There are four basic sources of these inputs: (1) historical data, (2) experimentation, (3) field measurements, and (4) management judgment. Of these, experimentation will generally yield the most accurate results.^ In view of the cost of experimentation, the dynamic nature of many marketing situations, and the frequent need for rapid decision, one of the other methods may prove to be superior in any given application. OR analysis can indicate the value of additional information and the degree of precision required in the data by testing the sensitivity of decisions to particular parameter values and input assumptions. An early example of the benefits to be derived from applying models and OR to selling is given in the Brown, Hulswit, and Kettelle report of a study for the Penstock Press (a disguised firm) in which the OR group derived some decision rules to be used •' Richard E. Quandt, "Estimating Advertising Effectiveness: Some Pitfalls in Econometric Methods," Jourjial of Marketing Research, Vol. 1 (May, 1964), pp. 51-60. • ABOUT THE AUTHORS. David B. Montgomery is Assistant Professor of Management at the Sloan School of Management, Massachusetts Institute of Technology, and is currently Vice Chairman of the Marketing College within the Institute of Management Sciences. Dr, Montgomery Is the author of papers on stochastic models of consumer behavior, research methods, and computer applications in INDUSTRIAL MANAGEMENT REVIEW, JOURNAL OF MARKETING RESEARCH, and MANAGEMENT SCIENCE. Frederick E. Webster, Jr. is Assistant Professor of Business Administration at the Amos Tuck School of Business Administration, Dartmouth College. He received his M.B.A, at Dartmouth and his Ph.D. at Stanford. He was previously on the faculty at Columbia University. Dr. Webster Is coauthor of SALES FORCE MANAGEMENT: TEXT AND CASES and READINGS IN SALES FORCE MANAGEMENT (Ronald Press) and has had articles on buyer behavior and sales force management recently In BUSINESS HORIZONS, JOURNAL OF MARKETING RESEARCH. JOURNAL OF PURCHASING, and WASHINGTON BUSINESS REVIEW. The investigation for this article was supported by the Tucic School Associates Program. 52 by management for allocating salesmen's time to new and old accounts. In summarizing their results, the authors stated: "Operations research introduced to the Penstock Press the concept of measurement and logical analysis in sales problems which had been previously considered amenable only to intuitive treatment. Most of the general results were not unexpected by the officers of the company—their background had given them a rational basis for arriving at the same major conclusions. It was in deciding on the particulars needed to give the results operational meaning that their intuitive background failed them. Of course, the sales manager already knew that it was important to concentrate on large customers. However, his intuition could not tell him whether this should include the 500 largest, the 1,000 largest, or the 5,000 largest . . . . The salesman already knew that it was harder to get a customer than to hold one. Their intuition failed to tell them how much harder . . ." -^ Reasons for Lack of Progress Despite the potential benefits of applying OR to personal selling strategy, little work has been done. Attempts to review thoroughly previous applications of OR to personal selling strategy' decisions have uncovered only a handful of published studies. Conversations with managers in those companies and consulting firms who seemed most likely to be applying OR to selling strategy have revealed only two unpublished and reasonably complete studies. While it is safe to assume that many other companies have explored the area and that several have studies under way but are reluctant to discuss them because of their confidential nature, the evidence suggests strongly that there has been only minimal interest in applying OR to selling strategy. Some possible explanations for this lack of interest and progress can be offered by examining the unique perspective of the manager of salesmen, the nature of personal selling strategy decisions, and the approach of the operations researcher. The Viewpoint of the Manager of Salesmen Sales managers tend to have short planning horizons. They are almost always under real pressure to generate the budgeted level of revenue for the period. While they may be more or less sympathetic to the values of analysis, experimentation, and longrange planning, the new insights that might be obtained from these exercises are luxuries that can rarely be traded for sales this year. The sales manager is l-esults-oriented because his superiors demand Arthur A. Brown, Frank T. Hulswit, and John D. Kettelle, "A Study of Sales Operations," Operations Research, Vol. IV (June, 1956), pp. 296-308, at pp. 307-8. Journal of Marketing, January, 1968 it and because his previous selling experience has oriented him to the psychic satisfactions of accomplishing a revenue objective. As one sales manager succinctly expressed it, "The name of the game is 'Go Make Quota.'" Furthermore, sales managers are not noted for their interest in, and ability for, rigorous analysis of hard data. Most companies still follow the questionable policy of staffing the sales management structure with those men whose selling performance has either merited the "recognition" of a promotion to management or demanded the potentially higher earnings of a management position. While managements are increasingly recognizing that the best salesmen do not necessarily make the best sales managers, and that selling and managing require different skills and abilities, most sales organizations are staffed with managers who have not had the benefits of formal training for management and analytical responsibilities.^ These managers are acutely aware of the importance of the personal "chemistry" between the salesman and his customers, the sensitivities of the individual salesman, and the unique aspects of each competitive selling situation. They are suspicious of the validity of patterns and generalizations identified as the result of the analysis of historical performance or the experiences of other companies. While some of the sales manager's distrust of quantitative analytical approaches is justified, it often results in the erection of strong barriers to careful reexamination of how the sales force conducts its business. Obviously, these observations do not apply to all sales managers, nor do they completely describe their important characteristics. However, these tendencies help to explain the slow progress in appljing OR to sales strategy. The Nature of Personal Selling Strategy Decisions Another source of difficulty in applying operations research to selling strategy is the problem of implementing change. Personal selling is unique among the elements of the marketing program in that the resources involved—the "controllable variables"which the manager manipulates—are people. These people, salesmen and their supervisors, interact with other people, customers, who respond to selling eflfort in a complex and variable manner. This makes it especially difl[icult to run controlled experiments, to shift resource allocations, or to change the characteristics of the inputs, except on a slow, evolutionary basis. As a result, it is both risky and expensive to tinker with a sales organization on the chance that the changes may, or may not, result in a more effective strategy. Selling strategy decisions, therefore, tend to be made within the constraints of •'^Andrall E. Pearson, "Sales Power Through Planned Careers," Harvard Business Review, Vol. 44 ary-February, 1966), pp. 105-116. Application of Operations Research to Personal Selling Strategy present organizational arrangements, with changes in organizational resources requiring long periods of time for implementation. Changes in the basic organizational resources must occur slowly for several reasons. First, customers will object to disruptions of the personal relationships they have developed with particular salesmen, especially if these disruptions occur on a frequent basis. Second, in some types of selling, these personal relationships are so important that the selling company risks the loss of important customers if the salesman is taken away from these accounts. Third, the long time required for careful recruiting, selection, and training programs to increase the level of organizational resources makes change both slow and expensive. Finally, salesmen have wives, mothers-in-law, children in scbool, and community responsibilities which have a tendency to decrease their willingness to move. Thus, it is difficult to shift the allocation of salesmen to customers and market areas, and these kinds of allocations are the basic stuff of selling strategy decisions. Another problem in developing new selling strategy relates to the ways in which companies collect information relating to the sales organization. The basic data available for controlling and evaluating sales performance are usually coded and organized on the basis of particular territories, account and product identification procedures, and other unique methods of organizing information which tend to evolve without careful planning. Basic changes in selling strategy which result in changes in the "control units" of the organization—sales territories, product groupings, and customer categories—can seriously disrupt the control processes of the organization. Like all marketing strategy decisions, personal selling strategy must be implemented and evaluated in a complex, dynamic environment. Only rarely is it possible to isolate the effects of a change in selling strategy per se. Economic conditions, buyers' expectations, and competitors' strategies influence sales performance in the current period. And there may be significant lags in the relationships between selling strategy changes and sales results. Thus, even when the personal selling variables can be rather carefully controlled, evaluation of the results attributable to changes in these variables is a difficult task. All of these factors contribute to a tendency for selling strategies to change only slowly and to the manager's reluctance to systematically manipulate selling strategy variables. Likewise, it can be very difficult to appraise the effectiveness of changes in selling strategy. The lack of convincing evidence of the value of change, added to the expense involved in implementing change, can lead to a strong preference for the status quo in personal selling strategy. 53 The Approach of the Operations Researcher In several respects, the approach and point of view of the operations researcher is almost directly opposite to that of the manager of salesmen and the nature of selling strategy decisions. The operations researcher thinks analytically. He tries to structure problems in a way which identifies the important relationships among critical variables and cuts through the complexity and idiosyncratic nature of the situation to produce new analytical insights. To obtain structure, he frequently resorts to simplifying assumptions and to generalizations which capture the basic nature of the process which management wishes to control. These relationships are generally stated in terms of symbols and mathematical functions forming a model. Certain requirements for structuring the problem under investigation may result from the nature of the analytical techniques which the operations researcher plans to use. For example, the use of linear programming requires that the relationships be stated in terms of linear functions. Model building results in a simplification of the problem to get at the essential relationships and, in the process, destroys some of the complexity and uniqueness of the problem. For the operations researcher this is a desirable feature of model building; from the sales manager's point of view, it may make the results of the analysis difficult to accept. The sales manager's point of view has been characterized as concentrating on action and results, the complexity and uniqueness of each decision problem, and not oriented to analysis and generalization. In contrast, the operations researcher is oriented to rigorous analysis, the identification of consistencies and patterns, and the development of a basic understanding of the underlying processes which characterize decision problems. It is not surprising that the two points of view do not mix well unless specific steps are taken to bring them together. Let us assume, for example, that a sales manager is particularly concerned about the complex problems of allocating his salesmen's efforts between potential new accounts and old established customers. The operations researcher will almost certainly have to simplify and structure the problem in order to come to grips with the essential differences in how these two classes of customers respond to selling effort and the factors upon which these different responses depend. The operations researcher might begin to structure the problem with the statement: "Assuming all salesmen are of equal ability and have equal workloads, and assuming no competitive retaliation to a change in our strategy. . . ." At this point, the typical sales manager has probably lost interest. (One might just as well ask General Cuater to assume that Indians march single file!) 54 Journal of Marketing, January, 1968 SET OBJECTIVES (Define role of personal selling in the marketing mix) \ Estimates of total market potential and sales forecast Determine Sales Force Size Set Sales Budget Available Resources and Company Constraints / Organize Sales Effort Define Control Units (Territories. Districts, etc.) Measures of Potential and Forecasts, and Workload, by Control Unit Allocate Effort to Control Units Measures of Account Potential and Sales History Develop Call Strategies for Specific Accounts Evaluate and Control Sales Force Performance Data on Current Results FIGURE Feedback Data on Sates Performance 1. The selling strategy decision process Potential for OR in Personal Selling Strategy In spite of slovi' progress to date, OR does have real potential for aiding the analysis of selling strategy and assisting the sales manager in making strategy decisions. The potential can be seen by examining the basic steps in the selling strategy decision process and by examples of studies which apply at each step. Figure 1 suggests the steps involved in selling strategy decisions. {In addition to strategy, sales management is also responsible for administering the sales organization. Policies relating to sales force recruiting, selection, training, motivation, compensation, and supervision are distinct from selling strategy and are not considered in this framework.) The development of a selling strategy begins with a definition of the role of personal selling in the marketing mix. Objectives for personal selling are established in the form of descriptions of the salesman's responsibility for communication (promotion), customer service (including technical assistance, delivery, etc.), and market information. This analysis leads to a preliminary estimate of the number of salesmen required, given the opportunity in the market, that is, market potential. The statement of sales objectives leads to an estimate of the sales budget required, whicb must also consider the availability of financial resources and other company constraints. The simultaneous evaluation of market opportunities and company constraints leads to decisions about sales force size and sales budget. These decisions specify the total level of selling effort available. Optimization techniques are important tools in the operations researcher's kit, and several OR studies have dealt with the problem of determining the optimum level of selling effort, Waid, Clark, and Ackoff analyzed historical and experimental sales results for the Lamp Division of the Genei-al Electric Company and recommended a reduction in the Application of Operations Research to Personal Selling Strategy level of selling effort to existing accounts. This recommendation was based on several critical assumptions about the shape of the response function describing the relationship between number of calls and sales results and about the workload of the salesman.^ Buzzell reports an OR study where one objective was to determine the optimum size of the sales force. This study analyzed historical sales and cost data, formulated a model of market response, and used calculus to determine the most profitable sales force size. An attempt was made to analyze the sensitivity of the results obtained to several key assumptions upon which the model was based.^ While both studies produced important insights into the nature of the selling process, the recommendations made by the OR group in each case depended on some critical (and questionable) assumptions about how the market responds to sales effort. The assumed response function was a central part of the model built by each group, and the validity of the results of these analyses depends on these assumptions. Moving on through the decision process, the sales manager must next develop the basic control units upon which he plans to organize and control sales effort. Decisions are required for the kind of units (product, customer, geographic) as well as the hierarchical structure (span of control) to be used. An example of an application of OR to this stage of the decision process is Stern's model for determining the optimal number of sales offices.* This model minimizes total .selling costs for a given level of sales. The value of the model lies in the fact that it forces management to think in terms of how various costs vary with the level of sales, that it provides a measure of the adequacy of the present sales organization, and that it yields planning information in terms of the optimal number of branch ofiices for various anticipated future sales levels. Using some estimates of market potential and expected revenues (contingent on alternative levels of selling effort), the sales manager must allocate sales effort to control units—territories, branches, districts, regions—or among customer types, etc. Also to be considered in this process is the "workload" as defined by territory geography, customer concentration and dispersion, and the work a salesman is expected to do on an "average" call. This analysis leads to a decision about the number of salesmen to be assigned to a control unit or, alternately, the number of accounts to be assigned to a salesman. 6 Clark Waid, Donald F. Clark, and Russell L. Ackoff, "Allocation of Sales Effort in the Lamp Division of the General Electric Company," Operations Research, Vol. IV (December, 1956), pp. 629-47. 7 Robert D, Buzzell, Mathematical Models and Marketing Management (Boston: Division of Research, Graduate School of Business Administration, Harvard University, 1964), pp. 136-56. 8 Mark E. Stern. Marketing Planning: A Systems Approach (New York: McGraw-Hill, 1966), pp. 65-69. 55 The study by Brown, Hulswit, and Kettelle, cited earlier, dealt with this problem. Allocation problems are familiar fare to the operations researcher. Because of the multiplicity of factors, the complexity of relationships among them, and the existence of resource constraints, sales effort allocation problems often exceed the manager's abilities to compute and evaluate the consequences of all feasible alternative allocations. Operations research techniques are necessary for the thorough examinanation of possible allocation strategies. Previous applications of OR to the allocation of sales effort have tended, however, to "assume away" some of the most important factors involved in the selling strategy decision—differences in competition in sales territories, differences in the abilities of individual salesmen, and differences among accounts in how they respond to sales effort. Here the sales manager's characteristic concentration on the idiosyncratic nature of each situation and the operations researcher's desire for generalization clash head on. One possible compromise is for both the manager and the researcher to agree to certain groupings of salesmen, territories, and accounts which recognize these important differences while at the same time identifying characteristics and patterns which describe important similarities. For example, it might be desirable to classify salesmen into categories based on their years of experience and previous sales, and to group customers into several categories on the basis of estimated potential and frequency of orders. An example of a method for grouping customers is provided by Magee, who arrayed dealer customers according to average order size. This provided the basis for allocation of sales effort to customers,-* Having allocated sales effort to control units, the sales manager must then consider the call strategies to be used with specific customers or types of customers. Call strategy involves time allocation to new versus current accounts, call norms (for example, the number of calls to make per period on each customer), and call scheduling. The studies by Brown and Waid and their respective associates, which were cited earlier, provide examples of OR approaches to allocating effort between potential and current accounts. Recently, some researchers have proposed a Markov chain approach to this allocation decision.'" Cloonan has presented a heuristic pro»John F. Magee, "The Effect of Promotional Effort on Sales," Journal of the Operations Research Society of America, Vol. 1 (February, 1953), pp. 64-74. If* Abraham Shuchman, "The Planning and Control of Personal Selling Effort Directed at New Account Acquisition; A Markovian Analysis," in New Research in Marketing (Berkeley: The Institute of Business and Economic Research, University of California, 1966), pp. 45-56, and William W. Thompson and James U. McNeal, "Sales Planning and Control Using Absorbing Markov Chains," Journal of Marketing Research, Vol, IV (February, 1967), pp. 62-66, 56 cedure which will generate "good" solutions to the very complex combined problems of setting call norms and call scheduling.!^ (A "heuristic" is a rule of thumb which hopefully assures "good" decisions but which cannot guarantee an "optimal" decision.) The final step in the sales strategy decision process is the evaluation and control of sales performance. Data on current performance are compared to standards, variations between what is desired and actual results are observed, and elements of selling strategy are adjusted accordingly. There do not appear to be any published examples of the use of OR to design systems for monitoring sales force performance and using the results to modify strategy. We know of one sales manager, however, who was using such a system in a manner he described as very successful. It seems highly likely that such use will become more common as the advent of marketing information systems creates the need for analyzing large amounts of data as an aid to more effective day-byday decision making. The attempts by Hughes to measure the impact of sales presentations on buyers' awareness and attitudes also show promise at this stage in the decision process.^^ The Need for OR in Sales Management The foregoing review has suggested four decision areas in the determination of sales strategy where OR has potential usefulness: (1) determining sales force size and selling budget, (2) allocating sales effort to control units, (3) developing call strategies for specific account categories, and (4) designing systems for monitoring and adjusting sales strategy. If OR is to realize its potential, however, operations researchers will increasingly have to recognize the need to tailor their approach to the realities of the specific situation. Too often in the past, OR applications to marketing problems could be characterized as the "have model, will travel" approach in which problems are inappropriately defined in order to fit the requirements of an optimization model. Recent and prospective technical developments in OR should enable the operations researcher to formulate and solve increasingly realistic problems. There is likely to be a growing need for OR in sales strategy decisions. If the sales manager is to take full advantage of the advent of computer-based marketing information systems, he will almost certainly need to turn to the operations researcher for assistance. At present, the typical firm has much of the data required for the application of OR to per11 James B. Cloonan, "A Heuristic Approach to Some Sales Territory Problems," in J, D, C. Little (ed,). Proceedings of the IFORS Conference (September, 1966). 12 G. David Hughes, "A New Tool for Sales Managers," Journal of Marketing Research, Vol. 1 (May, 1964), pp. 32-38. Journal of Marketing, January, 1968 sonal selling. Salesmen's call reports, records of orders and invoices, and shipment records describe the basic elements of selling strategy. These data, however, are often difficult to retrieve and summarize for purposes of analyzing the effectiveness of selling strategy, except with major expenditures of time and effort. Computer-based marketing information systems will allow the sales manager to retrieve such information much more quickly and economically. Programs can be developed for monitoring sales strategy and reporting potential problems as soon as they begin to develop, on an "exception reporting" basis. The evidence available suggests that the most effective marketing information systems will be those in which line managers participate actively in their design and in which the sophistication of the system is balanced with that of the managers who use it,'-^ This suggests a real need for the sales manager and the operations researcher to get to know each other better if useful analytical results are to be achieved. As mentioned previously, many of the early applications of OR to personal selling were highly artificial examples where the problem was molded to fit the solution and modeling techniques available. There is a danger that marketing managers and educators, because of the artificiality of these early attempts, may have become prematurely discouraged with OR. The point is that OR as a body of knowledge is constantly growing and expanding its capacity to solve problems. For example, developments in nonlinear programming free the user from the need to assume linear objective and constraint functions. Recent developments in numeric optimization procedures enable the user to optimize very complex expressions which even tbe non-linear programming algorithms cannot handle.^* The methodologies of heuristic programming and simulation are constantly improving as more and more experience is gained with these techniques, enabling the analyst to formulate more realistic models. Perhaps the most exciting possibility is the development of "interactive" models that permit the manager, using a remote computer teletype station, to examine systematically the consequences of alternative selling strategies. By allowing the manager to work in a simulated environment, interactive models can provide important insights about "What would happen if. . ," without the difficulties inherent in experimenting with a real-world sales force. In13 Donald F. Cox and Robert E. Good, "How to Build a Marketing Information System." Harvard Business Review, Vol, 45 (May-June, 1967), pp, 145-54. 1* For applications of numeric optimization procedures to marketing problems, see Glen L. Urban, "SPRINTER: A Tool for New Product Decision Makers," Industrial Management Review, Vol. 8 (Spring, 1967), pp, 43-54, and William F. Massy, David B. Montgomery, and Donald G, Morrison, Stochastic Models of Consumer Behavior (Cambridge, Mass,: M I T Press, forthcoming in 1968). application of Operations Research to Personal Selling teractive models do not make decisions for the manager but provide a basis for analysis and decision. At present, there are several examples of interactive models in other areas of marketing,^'"' and the future seems certain to bring the development of interactive models for exploring selling strategies. Concluding Remarks A critical look at previous attempts to apply operations research to selling strategy decisions suggests that these attempts have been relatively ineffective for three reasons. First, sales managers take a dim view of attempts to simplify and generalize about the complex problems involved. Second, initial attempts have been rather artificial due to the "have See, for example, Theodore E. Hlavac, Jr., and John D. C. Little, "A Geographic Model of an Urban Automobile Market," Working Paper 180-66, Sloan School of Management, M.I.T., 1966. Strategy 57 model, will travel" approach of fitting problems to the requirement of OR techniques of limited applicability. Third, the conflicting viewpoints of the sales manager and the operations researcher have resulted in the creation of some strong barriers. The result has been a certain disenchantment—probably premature—with the opportunities for applying OR to selling strategy problems. While initial applications have tended to be rather naive and oversimplified, they repi-esent an important first step. As OR techniques become more sophisticated, they make possible more realistic solutions to more complex problems. As marketing information systems generate the management need for new analytical tools and provide the data base for such analyses, considerable progress can be made. The results will be a better understanding of the process by which sales are generated in response to selling effort and more effective and efficient selling strategies. MARKETING MEMO The Incompatibility of Science and the Free Market System . . . What is certain is . . . the profound incompatibility between the new idea of the active use of science within society and the idea of capitalism as a social system. The conflict does not lie on the surface, in any clash between the immediate needs of science and those of capitalism. It lies in the ideas that ultimately inform both worlds. The world of science, as it is applied by society, is committed to the idea of man as a being who shapes his collective destiny; the world of capitalism to an idea of man as one who permits his common social destination to take care of itself. The essential idea of a society built on scientific engineering is to impose human will on the social universe; that of capitalism to allow the social universe to unfold as if it were beyond human interference. —Robert L. Heilbroner, The Limits of American Capitalism, Copyright © 1965, 1966 by Robert L. Heilbroner (New York; Harper & Row Publishers, 1966), p. 132.