Media Models for the Industrial Goods
Advertiser—A Do-it-yourself Opportunity
HARPER W. BOYD, JR.
HENRY J. CLAYCAMP
and
CHARLES w . MCCLELLAND
Can low-cost media models
be built for industrial advertisers? These advertisers typically have small media budgets
and are faced with specialized
audience groups about which
little is known. What problems are encountered in building a low-cost media model?
What results can be obtained
through the use of such a
model? The authors attempt
to answer these and related
questions through the use of
a case history.
n P H E development of models to facilitate the selection and evalua-•- tion of advertising media insertion .schedules has been one of the
most important advances made by advertising researchers in recent
years. Almost all of the work to date has been done by large advertising agencies because of the substantial costs associated not
only with constructing the model per se, but also because of the
problems inherent in obtaining the almost endless array of needed
input data. The thrust has been to develop models dealing with
consumer or household media. This is a direct result of (1) the
billing importance of these schedules; (2) the complexity of the
number of media types; and (3) the number of individual media
vehicles within each type.
While much still remains to be done to develop more efficient
media decision models relating to consumer media, the building of
media models for use by advertisers of industrial goods appears to
be well developed. This would seem logical considering the task of
building a media model for industrial goods advertisers should be
less difficult than for consumer goods. The latter type of models
requires coping with a great variety of households that are widely
dispersed through space and exposed to literally countless media
vehicles in various combinations over time. In contrast, the typical
industrial goods advertiser is interested in a more limited number
of buyers who are exposed essentially to print media.
Media models can and should be built for industrial advertisers.
This becomes evident when one considers that media decision needs
of many industrial advertisers are often both rationalized and
compromised on the basis of their relatively small budgets. In
addition, they are frequently faced with small and specialized
audience groups about which very little is known.
The purpose of this article is to discuss how such models can be
built at a cost commensurate with their payouts. The authors
have labeled this a "do-it-yourself" type of model since it can be
constructed with limited model-building expertise and used advantageously by almost any industrial advertiser who regularly
uses industrial print media.
The authors have recently completed an assignment which required the development of such a model. This article is based to
a considerable extent on the results of this research. The presentation covers: (1) how the model was developed; (2) the results obtained; and (3) what improvements could have been made
in the model.
Objectives of the Model
Journal of Marketing. Vol. 34 (April.
1970). pp. 23-27.
The ideal goal of any media model is to provide the optimum
schedule in terms of prospects to be reached, frequency of exposure
23
24
desired among various prospect groups, and other
relevant decision rules. Based on discussions with
the client the decision was reached to proceed with
the development of a model which would evaluate
altemative schedules as submitted and costed by the
advertising manager. It was thought that such a
model would have high utility and would yield substantial benefits at a relatively low construction and
maintenance cost. Thus, it was concluded that the
development of reasonably viable media schedules
could be obtained considering the experience of the
firm's advertising manager and the account executives at the firm's advertising agency, and considering the limited number of media choices involved.
More specifically, the model (called MISER) bad
as its objective among predetermined audience segments: 1) the production of a weighted readership
score and 2) a readership distribution. The former
yields a score which is based essentially on reach
and frequency of exposure within time periods. The
latter consists of two distributions. The first distribution reports the percentage of readers exposed
exactly once, exactly twice, and so on. The second
distribution is cumulative; that is, it reports the
number of readers exposed once or more, twice or
more, and so on. By comparing alternative media
schedules designed to apply to a specific audience
the user can decide which schedule is better given
his objectives for the particular advertising program in question and the costs involved.
Case Study
The first problem encountered in building the
model was the identification of members of the
relevant audience groups for the company's line of
products. This was accomplished by using the
company's mailing list which included the name,
title, and company affiliation of individuals recommended periodically by the salesforce to receive
company literature. It also included all those who
had requested technical literature, visited the company, attended a company workshop, visited the
company displays at trade shows, or sent in an
enquiry. This list was assumed to contain the
names of individuals associated with companies
which accounted for a very substantial part of the
potential for the products involved. If the media
habits of those individuals were known it was hypothesized that they would be reasonably representative of all buying infJnentials within all prospective
companies.
To use the company's list effectively the following
steps were taken:
1. A systematic sample (with a random start)
was selected.
2. All individuals associated with nonprospect
companies were eliminated from the sample.
Where any doubt existed, the decision was
referred to the sales manager.
Journal oj Marketing, April, 1970
3. All duplicate names were dropped.
4. All individuals who were considered "unimportant" were removed on the basis of occupation (e.g., librarians).
In consultation with the company's advertising
agency a list of 28 trade magazines and professional
journals were identified as representing a census
of the print media vehicles from which the company would draiv over the next 12 to 24 months.
The covers of the most recent issues were photographed and reduced in size to fit on a double-fold
8^/2 X 11 questionnaire. At the bottom of each
photo the respondent was asked to indicate (for
the current year) whether he read all the issues,
three quarters of all issues, one half of all issues,
one quarter of all issues, less than one quarter, or
none.
After consultation with the marketing manager
and his product manager it was decided that three
different sets of classification data should be obtained on each individual included in the model in
addition to his exposure to a number of different
vehicles. The first classification indicated whether
his basic activity consisted of representing an original equipment manufacturer, a university, or the
government. The second classification indicated
whether his fundamental job activity was in general
• ABOUT THE AUTHORS. Harper W.
Boyd. Jr. is Sebastian S. Kresge prolessor oi marketing and director.
International Center ior the Advancement oi Management Education. Graduate School oi Business. Stcmiord
University.
Proiessor Boyd received
his PhD at Northwestern University
where he taught ior a numbei oi
years. He is the author and coauthor
oi books and journal articles dealing
with a variety oi marketing subjects.
Henry J. Claycamp is associate proiessor oi marketing in the Graduate
School oi Business, Staniord University. Proiessor Claycamp received his
PhD from the University oi Illinois and
has been on the iaculties oi Washburn
University and the Massachusetts Institute oi Technology. He is an active
researcher and consultant in the applications oi quantitative methods in
marketing.
Charles W. McClelland is vice president. Lex Computer Systems. Inc. He
received his BS, MA. and PhD irom
Staniord University.
Dr. McClelland
has been a visiting lecturer at the
Staniord Graduate School oi Business
and iormerly was head oi operations
research at Varian Associates, Inc. He
has had extensive experience in the
application oi operations
research
techniques in marketing and iinancial
planning.
Media Models jor the Industrial Goods Advertiser—A Do-it-yourselj Opportunity
management, applied research, manufacturing, or
engineering. The third classification specified his
areas of technical interest—e.g., radar, test measurement, scientific and medical, and navigation. These
classification data could be used in any combination
desired to specify the target audiences to which one
or more schedules would be submitted for evaluation.
Data Inputs
The survey data obtained were collapsed into
essentially the following question, "What is the
probability of prospect X being exposed to the
average issue of each of a variety of print media
vehicles?" Although exposure to a media vehicle
is a variable (e.g., how much of it was read?) no
attempt was made to measure the extent or degree
of exposure. Thus, exposure was defined operationally as whether a respondent reported reading
something within a particular vehicle.
The probability statement comes into the model
because of the problem of time. Assume a quarterly
journal. If one knew that a prospect was exposed
on the average to three out of four issues then the
probability of exposure to the average issue would
be .75; if the exposure was two out of four the
probability would be .50; and so on. If the prospect was exposed to all four issues then it would be
certain (probability of 1) that he read something
in the average issue.
The problem of how to treat additional exposures
(either within a specific vehicle through time or
among media vehicles) is not easily solved. The
problem is complex because of the need to ascertain
the effect of each additional exposure given certain
time intervals between exposures. In the case at
hand the first exposure was rated at .9, the second
at 1.0, the third at .9, within a two-month interval
on the assumption that the advertising centered on
products which were relatively new and complex.
The reader, therefore, would need exposure to two
ads in order to obtain jidl injormation. The
weightings were recognized as being subjective and
provision was made for them to be changed as
desired.
Individual media vehicle costs (including the
effect of discounts) were not included in the model
although it would not have been difficult to do so.
It was thought that since the model operated by
evaluating alternative schedules prepared by the
advertising manager, it would be simpler to have
the agency cost out the schedules to be tested.
How the Model Works
As noted earlier, the model works by evaluating
alternative schedules submitted by the advertising
manager. It does so by producing the weighted
readership score and the two readership distributions. The major steps required to generate such
data are discussed below.
1. The model user specifies the target audience
2.
3.
4.
5.
25
by indicating its desired characteristics; for
example, original equipment manufacturers
and government, research and development,
or the primary technical areas of work such
as radar, navigation, and test measurement.
The computer pulls out all individuals with
such characteristics.
The media schedule to be evaluated is inserted.
Each vehicle and its insertion (s) by month
are listed. At this time the user can insert
the present schedule in order to in(x;ulate the
sample; i.e., past exposures for the individuals
involved are taken into account in the
weighting of future exposures. Thus, if
prospect X was exposed once via the current
schedule and once in the proposed (future)
schedule his rating would be 1.0 (the weighting for a second exposure) versus .9 (the
weighting for a first exposure). Further, the
user can input different weights for successive exposures other than the .9 (first), 1.0
(the second), and .9 (the third), already in
the model.
The time units to be used are specified. Since
the model determines the exposure of each
individual by time unit, the typical period is
one month. It could be weekly if the media
vehicle were issued with such a frequency.
The heginning month and the closing month
are also stipulated at this time.
This step specifies the number of vehicles to
be used during time period one, two, and so
on. This instructs the computer when to
move from one time period to the next.
The computer selects an individual from the
target segment and, in effect, he is asked
whether he was exposed to each of the specific media vehicles in which insertions are
made. A special procedure is used with those
individuals who have neither a zero nor a 1.0
probability of being exposed to the media
vehicle in question. Since it is not known
which particular issues are read by an individual who doesn't read all issues, a random
process is used to determine which issues he
does read. For each magazine or journal a
random number is generated. If the individual's magazine readership probability for
that particular vehicle is, for example, 0.75,
then, if the random number is less than 0.75,
the program considers that he read that time
period's issue. If the random number is
greater than 0.75, the program considers that
he did not read that particular issue and the
next media vehicle is analyzed in similar
fashion with respect to the same individual.
6. Assuming that an issue was read, this jact is
recorded as part of the score and the individual's record is then updated so that any
26
second or third exposure during the same
or following month can be appropriately
weighted. The score consists simply of exposure multiplied by the value assigned to
that particular exposure.
All further steps are part of the repeat instructions contained in the model. Essentially the same
process as described in steps 4-6 are repeated until
all individuals are scanned with respect to their
exposure by time period for the media vehicles included in the schedule. The output for both the
target audience and the residual group consi.sts of a
score, a target audience exposure distribution, and
a residual audience exposure distribution. These
can be better understood by considering the following example:
• Two altemative twelve-month advertising
schedules for promoting one of the company's
product lines were submitted to the model for
evaluation. The product line involved was sold
primarily to manufacturers of electronic communications equipment and firms in the broadcast industry. Electrical engineers with an interest in communications and broadcast equipment were specified as the target audience for
the campaign.
• The first of the two media schedules had
been used by the company in the preceding year.
It consisted of 24 insertions in four horizontal
type electronics publications (i.e., vehicles serving the electronics industry in general) and 12
insertions in two specialized vehicles serving the
broadcast industry. The total space cost of
schedule I was $19,000. The second schedule
involved 24 insertions in four different horizontal
electronics magazines at a total cost of $10,000.
• Less than $25 of computer time was required to evaluate both schedules using the MISER
program.
• The output from the first evaluation run
showed that schedule I could be expected to
reach 92.5"^ of the relevant target audience and
produced a total exposure score of 3081. Although schedule II cost $9,000 less than schedule
I, the results indicated that it could be expected to
reach 90.3"^^ of the same target audience. It
generated a total exposure score of 2881. Thus,
the 70"^r higher cost of schedule I over schedule
II produced only a 2.4% increase in the predicted
reach and a 6.5% increase in the weighted score.
• On the basis of the above results the company implemented the second schedule. Comparison of the number of returned inquiry cards,
letterhead requests for information and results of
advertising awareness studies at the end of the
year versus similar data for the preceding year
indicated, at least as measured by these criteria,
that there was no appreciable difference in the
effectiveness of the two schedules.
Journal of Marketing, April, 1970
The above example proved to be particularly interesting since the savings from using schedule
II exceeded the total cost of building the model, including those associated with the data collection,
by more than $1,000.
Model Critique
A first criticism of the model is the way it deals
with prospective buyers. Ideally it should take
into account the importance of the individual within
the firm (for the purchase of a particular piece of
equipment) as well as the importance of the firm
or organization to which the individual is attached.
The latter is, in actuality, nothing more than an
account potential per some stated time period and
can be determined in a number of ways (e.g., salesmen evaluation of likelihood of future purchases).
Since the company envolved sells a variety of products, an account potential for each product group
is necessary.
To determine the relative importance of individuals within prospective accounts (customer firms)
is a more difficult problem ; however, there is usually
a hierarchy of buying infiuentials within each prospective account. These individuals should be identified by the salesforce and their relative importance
ascertained. This can be accomplished by treating
the buying decision as 100<T^ and then assigning a
share to each of the individuals participating in the
decision; for example, a .3 weight to the manager
of the R & D department.
A second major criticism is that the model as it
now stands is incomplete; i.e., it does not take into
account contacts (and their cost) made by the salesforce, by direct mail, the service organization, company workshops, and trade shows. Such a total
communications model could be built, but a major
problem would be the assigning of a value to each
of the different media involved. Subjective values
could be assigned in a manner similar to the way
the model handles repetition. Unfortunately, very
little is known about the relative effectiveness of
different media types and to obtain reasonably precise estimates is probably beyond the resources of
most firms. The same can be said about obtaining
estimates of the worth of additional exposures
(repetition).
A further problem has to do with the failure of
the model to measure the extent or degree of media
exposure. Research should have been undertaken to
determine whether readership scores could be used
for this purpose since such data were available to
the company for a majority of the media vehicles
incorporated into the study.
The above limitations could be largely overcome
given the required resources. The additional inputs would add substantially to the usefulness of
the model since alternative media schedules could be
tested in terms of both market potential and the
relative importance of the individuals comprising
Media Models for the Industrial Goods Advertiser—A Do-it-yourself Opportunity
the audience vis-^-vis the buying decision. Both
types of data could, however, be incorporated into
the present MISER model without too much
difficulty.
Summary and Conclusions
In the preceding sections the authors have attempted to show how industrial firms can develop
their own specialized media models at a very low
cost.
Information on actual and potential buyers collected during the company's normal marketing activities and data from specially designed readership
studies were used to create an artificial population
similar in important characteristics to the firm's
relevant real-world population. Use of the artificial
population in a Monte Carlo simulation makes it
possible to avoid problems of estimating media
2,1
overlap which usually plague attempts to use
mathematical models to evaluate insertion schedules.
Considerable improvement could be effected in the
MISER model by obtaining data on the relative
potential of firms included in the firm's universe of
prospects, on the relative influence exercised by individuals within such firms on the buying decision,
and on the relative worth of alternative media
types. Although MISER was designed to evaluate
alternative schedules as proposed by the media
planner, it can, with a relatively minor investment,
be converted into an optimization model using an
algorithm such as that developed by Aaker.^
David Aaker, "A Probabilistic Approach to Indus-
trial Media Selection," Journal of Advertising Research, Vol. 8 (September, 1968), pp. 46-54.
Alpha Kappa Psi Foundation Award
for 1969
Philip Kotler and Sidney J. Levy were voted the
1969 Alpha Kappa Psi Foundation Award for their
article BROADENING THE CONCEPT OF xMARKETING, which appeared in the January, 1969 issue
of the JOURNAL OF MARKETING. Professors Kotler and
Levy will receive a certificate of award from Alpha
Kappa Psi and a cash prize of $200 in recognition
of their achievement.
The winners are both professors of marketing at
Northwestern University, Evanston, Illinois. Professor Kotler earned his PhD at M.I.T. He is advisory editor of the Holt, Rinehart and Winston
Marketing Series and is past chairman of the
College on Marketing of The Institute of Management Sciences. Professor Levy's PhD is from the
University of Chicago. He is vice president of Social Research, Inc. Professor Levy has authored
several books and many articles.