Download Application of Operations Research to Personal Selling Strategy

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.