ADDRESSING NEW MEDIA WITH CONVENTIONAL MEDIA PLANNING
Hugh M. Cannon
Abstract: Accepted industry wisdom is that many new, and particularly Internet, media cannot be addressed through
conventional media planning procedures. This paper takes a contrary position. It not only argues that new media can be
addressed through conventional planning procedures, but it contends that they should be. Increasingly, all media compete for the
same budget. Furthermore, they play critical roles in the same integrated marketing communications programs. To suggest that
they should be treated differently argues against truly integrated media planning. While this paper does not suggest a
comprehensive integrated planning solution, it does outline the directions such a solution should take. Most important, it shows
how all media selections can be addressed through a common evaluative process.
One of the side effects of the media revolution has been a
shying away from traditional research in conventional
quantitative media planning. There are a host of reasons. One
is that media are prolipherating faster than the sources of data
needed to measure them. But research is addressing this
problem. In Internet media, measurement is relatively easy.
The question is more what should be measured (Novak and
Hoffman 1996; Pavlou and Stewart 2000). Leckenby and his
colleagues have made considerable progress in developing
measures of reach and frequency for evaluating Internet media
plans (Leckenby and Hong 1998; Kim and Leckenby 2000).
The greater problem is media integration. How does one
develop common standards for evaluating such varying media
types as the Internet and television or magazines? The issue of
intermedia comparisons ranked a close second to the effect of
advertising frequency in Schultz' (1979) study of media
practitioners' research priorities. While we have no data to
support this, we can infer that the problem has become more
pressing with the passage of time. In reality, media plans have
never focused on a single medium. But the problem is
exaggerated with the increase in the number of media
alternatives, and, in particular, the advent of Internet media.
The popularization of integrated marketing communications
(IMC) has further raised the stakes, placing more pressure on
planners to find methods of integrating diverse types of media
(Schultz and Kitchen 1997; Kitchen and Schultz 1999)
One approach is to retreat in the face of the increasing
complexity of the problem and rely on qualitative media
selection criteria. While we understand why people would take
this position, the fact is that modern campaigns often involve
millions of dollars in media expenditures. With this kind of
money at stake, the benefits of a more systematic, theoretically
rigorous, and ultimately, quantitative approach are enormous.
The purpose of this article will be to propose a basic system for
developing a common, quantitative planning system that can
be applied across multiple media classes.
Conventional Approches to Media Planning
While we speak of "conventional media planning," we
recognize that there is no single, universally accepted model.
But virtually all models address concepts such as gross
impressions, reach and frequency, effective reach and
requency, rating points, gross rating points, share, duplication,
audience
composition,
households/persons
using
television/radio, and cost per thousand (Novak and Hoffman
1996), if not by name, at least in concept. These grow out of a
relatively recent tradition, from the early 1960s, when media
planning was still struggling with the basic concepts of
quantitative media analysis. In 1961, Agostini published his
famous reach estimation formula, followed by a host of
competing formulations (Bower 1963; Caffyn and Sagovsky
1963; Kuhn 1963; Marc 1963; Hofmans 1966; and Claycamp
and McClelland 1968). These formulas enabled media
planners to estimate the reach, and by extension, the average
frequency of a schedule. Reach and average frequency were
superior to raw media weight in that they incorporated the
notion of duplicate exposure. Thus, they addressed two key
elements of media strategy -- how many people are exposed,
and how much advertising these people receive.
Reach and average frequency is a crude planning tool, even
though it is superior to the alternative of raw media weight it
replaced. When computers were introduced into media
planning, it became practical to begin working with frequency
distributions -- estimations of not only how many people were
reached, but also how many were reached with various levels
of exposure. In the early 1970s, Gensch (1973), and later
Chandon (1986) and Rust (1986), compiled extensive reviews
of the media schedule simulation literature. Many of the later
Journal of Interactive Advertising, Vol 1 No 2 (Spring 2001), pp. 28‐42. © 2010 American Academy of Advertising, All rights reserved ISSN 1525‐2019 29 Journal of Interactive Advertising Spring 2001
models
would
estimate
frequency
distributions
(Metheringham 1964; Green and Stock 1967; Beardon, Haden,
Klompmaker and Teel 1981; Rust and Leone 1984; Leckenby
and Boyd 1984; Leckenby and Kishi 1984; Leckenby and Rice
1985; Danaher 1988; 1989, 1991; 1992). However, it was not
until the advent of personal computers, and the corresponding
drop in the cost of computations, that these gained broad
acceptance in day-to-day media planning. As late as the early
1980s, the popular advertising literature was filled with
discussions of why frequency distributions should be used
instead of simple reach and average frequency analysis.
When cheap computational power arrived, media planners
were faced with a catch-up game. They recognized that using
reach and average frequency analysis was naïve, and that it
should be replaced by an analysis of frequency distributions.
The question was, "How?" Some visionary organizations, such
as Foote, Cone and Belding Communications, developed more
sophisticated planning systems (Wray 1985). But the industry
settled on the simpler notion of effective reach and frequency,
or simply effective frequency planning (EFP), as articulated in
Naples' (1979) classic book on the subject. Estimates vary of
how widely spread the use of EFP became, but it was clearly
the dominant media planning paradigm in the 1980s and 90s
(Kreshel, Lancaster and Toomey 1985; Leckenby and Kim
1994).
While EFP is still widely practiced in the advertising industry,
the concept has been shown to have major conceptual
problems - namely, that it still involves crude rules of thumb
that did not fully utilize available media data and that it
presumed an "s-shaped" advertising response curve that rarely
exists in real advertising situations (Cannon and Goldring
1986; Cannon and Riordan 1994). We will build the model
discussed in this paper around the concept of frequency value
planning, or FVP (Cannon 1996; Cannon, Leckenby and
Abernethy 1996, 2001). FVP is an emerging model that
addresses the major shortcomings of EFP.
A Media Planning Model
Hoffman and Novak (2000) suggest that, while quantatitive
media planning is essential for Internet media, conventional
approaches do not apply:
"As with advertising programs in the real world, online
managers want to know whether advertising is achieving the
company's business objectives. Traditionally, however,
advertising has not been able to provide meaningful
measurements of results. At best, conventional broadcast and
print advertising has only allowed measurement of the mass
amount of advertising delivered - as opposed to actually
consumed - usually expressed in terms of exposures or
impressions and at best broken down by demographic or
psychographic segments.
Thus, for managers seeking measurable results from online
advertising, traditional media models offer little guidance, and
may even hinder the process of capturing the value in
consumer response in virtual environments. Further, the Web
possesses unique characteristics compared to conventional
media. These differences warrant new strategies. Borrowing
advertising practices and metaphors from traditional media
like exposure-based advertising pricing models and billboardlike "banner ads" is likely to be appropriate only for limited
advertising objectives such as awareness in the context of
branding goals."
This paper takes a contrary view. Exhibit 1 portrays a basic
media vehicle planning model that is based on conventional
approaches, including those criticized by Hoffman and Novak.
It begins with the well-accepted premise that media planning
ultimately stems from marketing plans (box A), as suggested
by the classic DAGMAR paradigm (Colley 1961). In support
of this is an integrated marketing communications (IMC)
program (box B), also prominent in the literature. While IMC
is relatively new as a formal concept and has no clear,
universally accepted definition (Schultz and Kitchen 2000), a
growing body of literature and practitioner experience
suggests that it grows out of a reaction by advertising agencies
to such factors as the development and usage of databases,
client desires for interaction/synergy across media, and the
need for coordination on both a global and a regional basis
(Kitchen and Schultz 1999). Following the DAGMAR
paradigm, marketing strategy provides sales-oriented
objectives for the IMC analysis. The IMC analysis itself
consists of an effort to determine the kind of promotional
support needed and divides it up into logical tasks that can be
assigned to specific, synergystic media programs.
30 Journal of Interactive Advertising Spring 2001
Exhibit 1. The Media Planning Process
creative strategy and execution. Exhibit 2 provides a
framework for reconciling executional factors to media
environment.
The Exhibit 2 framework defines media environment in terms
of consumers' typical psychological environment during
advertising exposure. The environment is defined by
consumers' need for information and the capacity available to
process it. That is, how much is the typical audience member
likely to be actively looking for, or at least receptive to,
advertising information? And, how much actual processing of
advertising information is the typical audience member able to
do, given both his/her level of involvement and given the
number of competing mental tasks competing for her/her
attention.
Exhibit 2. Allocating Tasks by Media Class
It is at this point that the process begins to address actual
media decisions. As Exhibit 1 suggests, the first step is to
allocate promotional tasks to various media classes (box C).
Then, within each media class, the process seeks to identify the
specific media vehicles that are most cost-effective in
performing each task (box D). Finally, the process draws on
the most efficient media vehicles to develop media schedules,
again in support of each task. The process seeks to adjust these
schedules to maximize their frequency value -- their impact,
given the possibility of duplicate exposures (box E).
We will now turn to the task of discussing the three media
stages of the planning process that support what we have
termed "media strategy." These are portrayed by boxes C, D,
and E in Exhibit 1.
Allocating Tasks by Media Class
As Exhibit 1 suggests, the first media stage of the planning
process seeks to allocate the various IMC tasks to specific
classes of media. This is consistent with traditional planning,
but it also lends itself very well to new media. Indeed, new
media (particularly Internet media) tend to play more highly
differentiated roles than conventional media. In this sense, the
traditional model shown in Exhibit 1 actually works better for
campaigns that include both new and old media than for
traditional campaigns. The only requirement is a model to
guide the integration.
Exhibit 2 provides such a model. It assumes that IMC tasks
will consist of advertising-based marketing support strategies
that must ultimately be expressed through some kind of
Note that the framework is closely aligned to the creative
strategy models growing out the Foote, Cone & Belding (FCB
Grid) traditon (Vaughn 1980, 1986; Rossiter, Percy and
Donovan 1991; Rossiter and Percy 1997). As with our
framework, the creative strategy models are built around two
dimensions, although they are labelled differently - (1)
thinking/feeling" in the case of the FCB Grid, and Wells' now
classic "informational/transformational" distinction in the case
of the Rossiter/Percy Grid, and (2) "high involvement/low
involvement."
The Need for Information Dimension
The best way to understand the "need for information"
dimension of media environment is perhaps to review the
logic behind the "thinking/feeling" or "informational /
transformational" dimension of the creative strategy models.
31 Journal of Interactive Advertising Spring 2001
Ultimately, these describe the orientation of the promotional
messages that must be delivered in an appropriate media
environment. Understanding them, then, should provide a key
to media classification.
The scale items used to operationalize the "thinking/feeling"
dimension of the FCB Grid (Ratchford 1987) are suggestive of
the distinction between what Shimp (1997) calls the consumer
information processing model (CIP) and the hedonic,
experiential model (HEM), respectively. The CIP model
parallels the functional attitude theoretical notion of
"utilitarian" attitudes (Katz 1960) - the idea that people
evaluate alternative behaviors by weighing the consequences,
in our case the benefits derived from product attributes
(Fishbein and Ajzen 1975). In the FCB Grid scales, this
dimension is operationalized by items such as "decision is
mainly logical or objective" and "decision is based on
functional facts" (Ratchford 1987). The HEM tends to involve
holistic evaluations that are "felt" and "experienced" rather
than reasoned out (Hirschman and Holbrook 1982; Holbrook
and Hirschman 1982). These seem to correspond with the
FCB "feeling" scale items such as "decision expresses one's
personality" and "decision is based on a lot of feeling"
(Ratchford 1987).
Rossiter and Percy (Rossiter, Percy and Donovan 1991;
Rossiter and Percy 1997) argue that the FCB "thinking"
dimension relates roughly to "informational" advertising, but
that it suffers in its simplicity. In the Rossiter/Percy approach,
"informational" advertising draws on five different "negative"
motivations. Rossiter and Percy argue that the "feeling"
dimension corresponds relatively closely to "transformational"
advertising, which, in turn, implies positive consumer
motivations.
In our view, the general distinction between the two modes of
information processing is "logical" versus "associative." The
"need for information" dimension in Exhibit 2 is based on the
premise that both "thinking" and "informational" messages are
processed logically, consciously using information to make
inferences regarding how to achieve consumer objectives, as
posited by the CIP model. "Feeling" and "transformational"
messages are processed associatively - experienced -- with only
a casual regard for logic. This is important because consumers
do not consciously look for associative information. They do
not seek information or cues that will transform the way they
feel about a product. Contrary to Rossiter and Percy's
contention, we maintain that this applies to some negative
motivations, such as feelings of inadequacy that are triggered
through an ego-defensive appeal, as suggested by Cannon and
Boglarsky (1991). Consumers do not address threats to their
egos by logically choosing to abandon their feelings of
inadequacy any more that they consciously evaluate attributes
and choose to fall in love. Both are experiential events, the key
to which lies in unconscious associations. This explains why
ego-defensive messages do not tend to work in informationseeking media environments, such as magazines, and certainly,
the Internet.
To summarize, then, IMC messages that are highly associative
in their persuasive approach -- that address HEM motivations
-- are best delivered in media environments such as television,
where people tend to process cues by association. In these
media, consumers tend to "experience" rather than consciously
"evaluate" input. Conversely, IMC messages that rely on
conscious CIP evaluation are best delivered in media
environments such as magazines or the Internet, where
consumers tend to process decision-related information
through logical inference.
The framework goes beyond the obvious distinction among
major media classes. Within classes, we may also distinguish
among media that call for logical versus associative processing
of advertising cues. One interpretation of Aaker and Brown's
(1972) classic study of vehicle source effects is that quality
claims are more effective in prestige magazines because
consumers associate the claim with the media environment,
independent of the logical merits of the claim. Similarly, the
framework suggests that the "peripheral processing" effect
discussed in the context of the elaboration likelihood model
(Petty, Cacioppo and Schumann 1983) might be triggered, in
part, by media context rather than simply low involvement. In
environments where consumers traditionally process
advertising cues holistically, by association, consumers would
eschew central processing simply because they are not
psychologically predisposed to think logically. This may hold
true for Internet media as well as other environments. On a
gaming site, for instance, users would have no need for
product information, and advertising cues would likely be
processed associatively, partaking of the ambience and feelings
consumers were experiencing while visiting the site.
The Available Processing Capacity Dimension
The "available processing capacity" dimension of our mediaclass selection framework (Exhibit 2) corresponds to the
"involvement" dimension in both the FCB and Rossiter/Percy
Grids. Recognizing a large and complex literature on the
subject of involvement, Ratchford (1987) notes, "At risk of
32 Journal of Interactive Advertising Spring 2001
gross oversimplication, a reasonable summary of the
[involvement definitions] would seem to be that involvement
implies attention to something because it is somehow relevant
or important." Both the FCB Grid and Rossiter/Percy Grids
address a kind of product involvement. The media-class
selection model addresses the complement of this,
involvement capacity. That is, how much involvement are
audience members ready and able to mobilize for an ad in the
media context. This capacity depends, in part, on the format
and uses/gratifications of the medium itself. The Internet has
enormous capacity for involvement, because users can extend
the time they spend indefinitely, searching a site, investigating
links, and so forth, until they have achieved their usage goals.
Information processing capacity also depends on the
competing tasks audience members are likely to face in a
particular media environment - what else is vying for their
attention (Web 1979). The effects of advertising clutter (Web
and Ray 1979) and "zapping" from one station to another
during commercials to see portions of other programs
(Abernethy 1991; Zufryden, Pedrick and Sankaralingam 1993)
are illustrative of only two of the problems faced in a tevision
environment. These may be expanded to include everything
from side conversations to visiting the bathroom, the net effect
of which is to reduce the processing of advertising messages
within a media context (Abernethy 1990). The model
portrayed in Exhibit 2 suggests that each media class should be
classified according to the effect distractions are likely to have
on processing capacity. Exhibit 2 suggests some general
classifications, but the key to the model is not the content, but
the framework and the principles behind it. Newspapers and
television both tend to be cluttered, thus leaving consumers
with little excess capacity for processing advertising messages.
However, by increasing the size of the newspaper ad or the
length of the television commercial, these media can be
adapted to higher involvement messages.
Evaluating Media to Address IMC Tasks
Implicit in our discussion of Exhibit 2 is the notion that media
exposure is governed by a kind of "psychological program" - a
set of mental instructions that conditions media consumption
behavior. The placement of media alternatives in the exhibit
reflects the fact that some of these "programs" address
information processing tasks, while others serve more
entertainment-oriented or other emotional uses and
gratifications. Similarly, some media consumption "programs"
are able to process advertising stimuli in depth, while others
grant advertising only cursory attention.
The notion of "media exposure programs" is useful when we
address the shortfalls of our media-class evaluation
framework. While Exhibit 2 provides some very useful
guidelines for matching media with promotional tasks, it falls
short of capturing the full variation in media environment
represented by the alternatives advertisers now face. For
instance, properly viewed, the Internet is not a medium, but a
collection of media (see Wells and Chen 2000 for the possible
dimensions of these differences), used by different kinds of
people (Wells and Chen 1999; Rodgers and Cannon 2000) for
very different reasons (Stafford and Stafford 1998; Korgaonkar
and Wolin 1999; Rodgers and Sheldon 1999). A study by
Cannon (1982) suggests that matching value-profiles of
editorial content versus advertising messages might be a way
to further refine the match of promotional tasks with the
media environments to which they might be best suited. Value
profiles capture additional dimensions of the "media exposure
programs" that drive different media environments. Of course,
media planner judgment provides the most widely accepted
method for peforming this matching. This judgment may be
informed by experience, or formal analyses of the way
consumers process advertising media, such as Rodgers and
Thorson's (2000) analysis of interactive media.
Prioritizing Media Vehicles by Cost/Effectiveness
The vehicle evaluation stage of the planning process (box D in
Exhibit 1) is both the most controversial and easiest to
address. It is at this stage that most planners despair of finding
a common ground between old and new media. First,
measurements of media exposures are not available for every
media class. Second, it is very difficult to compare media as
diverse as outdoor posters and direct mail. Comparing old and
new media - television with the Internet, for instance, is even
more difficult yet.
Granting that comparisons are difficult, the criteria for
evaluating cost/effectiveness nevertheless do not really change
across media classes. This is true even when new media are
involved. The basics go back to the classical ARF model
(Advertising Research Foundation 1961), in which media
effectiveness is evaluated in terms of media distribution,
audience size, advertising exposure, advertising perception,
advertising communication, and advertising response. Gensch
(1970, 1973) provided a similar framework in which he
proposed to evaluate media based on target population
weights, vehicle appopriateness weights, commercial exposure
weights, commercial perception weights, and cumulative
frequency weights.
33 Journal of Interactive Advertising Spring 2001
Exhibit 3. Evaluating Media Vehicle Efficiency
We suggest that planners may effectively evaluate alternative
media vehicles using a much simpler scheme of modified cost
per thousand (CPM), as suggested by Exhibit 3. CPM
evaluates the cost of message distribution by considering how
much money it takes to purchase 1,000 exposure
opportunities. An obvious refinement would be to consider
the cost of purchasing 1,000 target market exposure
opportunities, or CPM TM. An even more useful index would
be to estimate the cost of actually creating 1,000 effective
advertising exposures, however that exposure is defined. We
can call the end result the cost per thousand effective target
market exposures (CPM ETM), the ultimate basis for comparing
media vehicle cost/effectiveness, or efficiency, with a given
media class.
If M represents the media audience (in thousands), T the
target market (also in thousands), T∩ M would represent the
intersection of the two, or the target audience. We may use
p(e|m) to represent the probability that a given individual, e,
has been effectively exposed to the advertising message, given
that s/he is also a member of the media audience (event m).
The measure of media vehicle efficiency would be as follows
(Equation 1):
Cost
CPMETM = -----------
Furthermore, the general logic of "prototyping" provides a
useful way for extending existing data sources to
"unmeasured" media (Baron 1990/1). It suggests that media
can be grouped into categories with relatively similar audience
characteristics. One or more of the group are then taken as a
prototype, and data are then "borrowed" to help estimate the
characteristics of the unmeasured vehicle (Cannon and
Boglarsky 1992). In the industry, the term, "prototyping," is
generally applied to magazine media. However, the logic is
widespread. For instance, in newspapers, it is common to take
circulation figures and multiply them by average readers-percopy figures from other newspapers, thus yielding an estimate
of total audience.
Of course, in Internet media, actual audience figures are
generally captured by the web-site protocols. However, the
logic of prototyping can be helpful in estimating target market
audience. Hit rates and click throughs do not necessarily tell
the site manager what proportion are actually in the target
market for different products that might advertise on the site.
If another site, or perhaps an outside research agency, were to
conduct a study of target market concentration among website users, the results could be used to "prototype" data for
other sites as well. To illustrate, consider an Internet campaign
that is targeted to people who have purchased an SUV in the
past 12 months. Simmons or MRI conduct large-scale
syndicated research studies in which they ask respondents
(among other things) to indicate various aspects of their web
usage behavior. They also ask questions regarding product and
brand usage. Their cross-tabulated reports would indicate the
proportion of Internet users who had purchased an SUV. This,
in turn, can be used as an estimate of the conditional
probability of target market membership (SUV purchase),
given media usage (a particular type of Internet activity) p(t|m). Let M represent the number of people in your Internet
audience and T the number in your target market. Equation 2
gives the estimated intersection of the two (T∩M), or the
target market audience:
[1]
T∩M = M * p(t|m)
[2]
T∩M * p(e|m)
In fact, the estimates required for CPMETM are not particularly
demanding. However accurate their data, planners must
estimate the size of their audience, and better, the size of their
target market audience. Purveyors of media space recognize
this and generally go out of their way to provide these data, be
it from subscriber studies or some independent source.
Of all the estimates required for the analysis of media vehicle
efficiency (CPMETM), p(e|m) is the most troublesome. Planners
tend to question the meaningfulness of comparing direct mail
campaigns, where CPMETM may be $1,000 or more, with mass
media campaigns, where the figure may be well below $10.
Note, however, that we are arguing that this figure should be
used only to compare media vehicles within a given class,
34 Journal of Interactive Advertising Spring 2001
where they are assigned the same IMC task. The differences
are ironed out when we consider the probability of advertising
response, as we will in the next section. Consumers vary
dramatically in how they respond to different kinds of media
exposue. Outdoor advertising exposures may be inexpensive,
but they are ineffective for eliciting consumer purchase
responses in most situations. This makes direct mail much
more cost/effective. The same is true for the Internet. The fact
that CPMETM for Internet campaigns might be higher than for
mass media campaigns does not suggest that mass media are
more efficient. The objectives are usually quite different.
Exhibit 4. The Frequency Value Planning Process
Adjusting the Schedule to Maximize Frequency Value>
Selecting the most efficient media vehicles does not necessarily
produce an efficient media schedule, even within a given
media class. The problem is that media vary in the degree to
which they overlap, and duplicate exposures have a different
impact on consumers than original exposures. We have
already noted three generations of tools that have sought to
address this issue.
First was reach and average frequency. This recognized that
some exposures were duplicates, but it failed to account for
frequency distributions - the fact that not everyone reached by
a media schedule receives the same number of exposures.
Second, effective frequency planning (EFP) sought to address
the problems with average reach and frequency by identifying
an effective level of exposure that audience members must
receive before they are counted as "exposed." However, this
ignores the fact that there is no single level of exposure
required for advertising to be effective. Typically, the first
exposure is most effective, followed by diminishing returns for
subsequent exposures.
The third generation is only now being discussed. In it,
frequency value planning (FVP) seeks to address the flaws in
EFP by acknowledging the exposure value of every different
level of frequency to which audience members are exposed. As
with our models of media class selection (box C of Exhibit 1)
and media vehicle evaluation (box D of Exhibit 1), it lends
itself equally well to new as old media.
The FVP process is easiest to understand when it is broken
down into its component steps. Exhibit 4 does this. Each of
steps is discussed below.
Step A: Developing Media Strategy
Boxes C, D and E of Exhibit 1 do not constitute media
strategy. They are analyses that support it. The strategy itself
involves a general plan for targeting of media to people in
support of the firm's IMC program. The target audience's
response to media exposure - the basis for evaluating the
effects of frequency -- depends on the advertising problem and
the interaction of the advertising with the target market. For
instance, a campaign that uses the Internet to deliver critical
product information to people who are in the market for a
new car would have a characteristic pattern of advertising
response that is quite different from a campaign using
television advertising to create brand image in the minds of
future car buyers.
The key to this step is identifying key factors affecting the
shape and the slope of the advertising response curve. A
number of studies have addressed this topic over the years.
Unfortunately, the work has been largely judgmental, with
relatively little rigorous classificational research or testing of
theorized relationships. Foote & Belding Communications
(FCB) developed a comprehensive project to identify the
factors that determined the need for more or less frequency
(Ostrow 1982; Wray 1985). While the FCB propositions were
not anchored in specific studies found in the literature, they
represented a comprehensive effort by advertising
practitioners who were seeking to develop a valid, workable
system for estimating advertising response, addressing
marketing factors, copy factors, and media factors. Cannon
(1987) reviewed the literature and developed 27 theoretical
propositions regarding the relative need for frequency.
Presumably, these would be valid for new and old media alike.
35 Journal of Interactive Advertising Spring 2001
Step B: Developing a Trial Media Schedule
Step B in Exhibit 4 involves the construction of an actual
media schedule, much as a planner would when using EFP.
The only difference is that the planner is working against a
different criterion. An effective reach schedule of 3+ means the
planner will try to place ads in vehicles that have relatively
high audience overlap if they have a relatively small budget
with which to work. In contrast, if the planner were working
against a frequency value criterion, the plan would generally
seek to minimize duplication and extend reach as much as
possible (Ephron 1995; Jones 1995). This is because the
advertising response curve is typically concave (characterized
by continually diminishing returns). Therefore, lower levels of
frequency deliver relatively higher value to the schedule.
Step C: Estimating Advertising Exposure
Audience research services typically provide media exposure,
or opportunity to see (OTS), data. Obviously, any audience
response will come from actual ad exposures, not OTS.
Therefore, an adjustment must be made to convert vehicle to
advertising exposures, using what we characterized as p(e|m)
in our earlier discussion of CPMETM. While a considerable
literature exists regarding this subject, very little has been
written to provide practical guidance in making these
adjustments. This speaks to the difficulty of making
meaningful generalizations. It also explains why most media
planning models have relied on OTS rather than advertising
exposure, notwithstanding the broadly accepted criticisms of
the approach.
We will argue that, regardless of how difficult the process of
estimation, or how inaccurate the estimates, the issue cannot
be ignored. Cannon and Riordan (1994) suggest that it was a
failure to consider the problem of advertising exposure that
caused the industry to misinterpret McDonald's classic brand
switching study (Naples 1979), thus setting the stage for EFP.
The Cannon and Riordan analysis suggests that even the
crudest estimates of advertising exposure rates would have
unmasked the problem, and helped head off two decades of
misguided industry effort. The fact is that we know advertising
exposure rates are substantially lower than vehicle exposure
rates. To ignore the fact is analogous to financial executives
ignoring the time value of money, simply because they have no
accurate way to predict future interest rates.
The adjustments become particularly important when we seek
to compare conventional with Internet media. Internet media
often measure actual advertising exposure, signaled by such
indicators as click-throughs. Ignoring the difference would put
these media at a disadvantage relative to media where
"exposure" is really defined by OTS. Furthermore, Internet
media themselves vary in measurement approach, with some
measuring web-page exposure ("hits") while other
measurements are based on actual audience response. Again,
the adjustments are important if we are to compare the media
alternatives.
We can draw on efforts from conventional media to illustrate
two different approaches one might take to develop a system
for estimating exposure rates. One is to develop a set of norms,
or adjustment rules, for discounting the value of OTS. For
instance, research that observed television audience members
found eyes-on-screen time averaged 32.8% for commercials
compared to 62.3% for programs (Krugman, Cameron and
White 1995). This suggests that advertising exposure would be
only (32.8/62.3=) 52% of OTS.. Abernethy's (1990) detailed
review of observational and survey studies led him to estimate
32% television commercial avoidance, or 68% advertising
exposure. These figures provide a basis for adjusting OTS
figures downward to represent actual advertising exposures.
Beardon, Headen, Klompmaker and Teel (1981) reviewed
studies addressing attention levels for daytime television,
noting that they varied between 20% and 50% of program
ratings. In prime time, attention levels were reported at 76% of
program ratings for station-break and 84% for in-program
commercials. Again, a planner would adjust the average
exposure estimates up or down, depending on whether an ad
was placed in daytime or primetime television, in a stationbreak or in a program. For magazines, Roper Starch publishes
a book called Adnorms, in which average advertising exposure
rates are given for different categories of products in different
magazines. These exposure rates can also be used, much as the
television data we have discussed to adjust OTS estimates. For
instance, if the ad exposure rate were 50% for a magazine that
reached 10% of the target market, the reach used to estimate
frequency distribution would be (.5 x .1 =) 5%.
The second approach is to use an estimating formula, such as a
regression model (Cannon 1982). Philport (1993) discusses
factors that might be used to estimate magazine exposure.
Donthu, Cherian and Bhargava (1993) discuss the factors that
might be used to estimate exposure rates in outdoor
advertising. Again, the same basic approach would work
equally well or even better for new media.
36 Journal of Interactive Advertising Spring 2001
Step D: Estimating the Frequency Distribution
As a rule, frequency distribution formulas are based on
estimates of the probability that a given person will be exposed
to a media vehicle, or the probability of OTS. Our discussion
of step C above suggests that planners should use the
probability of advertising exposure instead, that is, multiplying
OTS by p(e|m). This does not change the actual calculations. It
simply yields a more realistic estimate of exposure frequency
distribution.
The literature suggests that sequential aggregation methods
provide what is perhaps the most practical tool for estimating
the distribution, since they strike a balance between theoretical
grounding, accuracy and speed of computation (Lee 1988; Rice
and Leckenby 1986). Such methods are also inherent in some
proprietary packages used by media planners (Lancaster 1993;
Liebman and Lee 1974). However, the specific frequency
estimation program is not important. Any frequency
distribution works equally well in FVP, as long it is accurate.
Different approaches tend to work better for some media than
others, so planners need to consult the literature to determine
which is likely to work best for any give aspect of their IMC
program. Leckenby and Hong (1998) evaluated the accuracy of
six models for use in Internet media and found that all but the
binomial distribution model were accurate within acceptable
levels.
Step E: Estimating Advertising Response
Step E seeks to estimate the level of audience response
associated with each level of the advertising frequency
distribution (step D). It is here that our model really addresses
the concerns of critics such as Hoffman and Novak (2000).
Media planning begins with media, and ultimately, advertising
exposure, because this is what media deliver. However, it is not
enough to consider whether to consider whether people see a
television ad aimed at creating product awareness. We must
ask whether it really makes people aware of the product.
Similarly, it is not enough to consider whether people hit on a
business-to-consumer direct-response web site. We must ask
whether the hit really results in a consumer response.
One way to look at the issue is to think of advertising response
value as a type of conditional probability -- p(r|ei), where r
represents audience response, and ei represents effective
exposure to level "i" of advertising. That is, how likely are
consumers to respond to the advertising message in the
desired manner given 0, 1, 2, 3 and so forth exposures? If we
plot these response probabilities, they form an advertising
response curve. Returning to our earlier comparison of an
outdoor versus a direct mail advertising campaign, the
CPMETM for direct mail campaign is much higher than that of
the outdoor campaign. However, p(r|ei) for the outdoor
campaign is virtually zero at every level of exposure. The direct
mail campaign would be likely to elicit positive advertising
responses, so it would be much more cost-effective.
As we have noted, the shape of advertising response curve will
typically be concave. That is, response to increasing levels of
advertising exposure is characterized by continually
diminishing returns. Such a curve can be represented by the
formula shown in equation [3] (Cannon, Leckenby and
Abernethy 1996).
R = M * (1-e(-a-b*i) )
[3]
where
R = Advertising response value, or what we have referred
to as p(r|ei)
M = Maximum response value, or p(r|e∞)
a = Parameter representing the Y-intercept (p(r|e0))
b = Parameter representing the slope of the curve
i
= the number of advertising exposures
* Note that "e" in the equation represents a natural
logarithm, not the event of effective exposure, as in
p(r|ei)
Note that the equation has three key parameters: M, a and b.
M and a can be estimated directly. b can be determined by
using equation [4] with an estimate of the response value to
the first advertising exposure (i.e. R1, or the value of R where
i=1).
B = -ln(1-R1/M)
[4]
The rationale for using a mathematical curve rests in the fact
that advertising response is not capricious. It operates
according to principles. If the principle is diminishing returns,
as we have suggested, the incremental response value of
advertising must be lower for each subsequent exposure. How
37 Journal of Interactive Advertising Spring 2001
much lower depends on the situation, which, in turn, is
reflected in the slope and magnitude of the curve. If you
experiment with different values, you'll find that there is
virtually no variation in the way you can plot hypothetical
response values without violating the principle of diminishing
returns, given an initial level of response (response level with
zero advertising exposures), a minimum response (response to
one exposure), and a maximum response (response to an
infinite number of exposures). These values are determined by
the mathematics of the curve. Again, this holds true regardless
of the nature of the medium, be it conventional, Internetbased, or whatever.
Some researchers argue that advertising is often characterized
by an S-shaped curve, where low levels of advertising generate
minimal returns, increasing rapidly once advertising has
reached some threshold level (Ackoff and Emshoff 1975;
Pavlou and Stewart 2000). The increase then begins to taper
off again with diminishing returns. We have noted that the
weight of current evidence from the literature appears to
suggest that S-shaped curves seldom characterize real
advertising situations (Simon and Arndt 1980; Schultz and
Block 1986). Nevertheless, the FVP model will accommodate
any pattern of advertising response. One need only supply the
advertising response values for each level of exposure.
Step F: Calculating Frequency Value
The response value of a campaign is simply the weighted sum
of responses for audience members exposed at each level of the
frequency distribution. To illustrate, consider a specialized
Internet banner program designed to stimulate click-through
to a related web site containing product information. Based on
a preliminary test, you estimate that 50% of the target market
is likely to visit the web site and see the banner. 40% visit once,
10% visit two or more times. Your study shows that there is a
25% chance that people who visit the site once will click on the
banner, while the probability increases to 30% for people who
visit two or more times. The "click-through" response value for
the one-visit group would be (.40 x .25 =) 10%. The value for
the 2+ exposure group would be (.10 x .30 =) 3%. The total
response value of the campaign would be (.10 + .03 =) 13%
(Exhibit 5). That is, 13% of the target market is likely to click
through to the target site, which was the objective of the
campaign.
Exhibit 5. Estimating Frequency Value for a Hypothetical
Internet Program
Level
of Frequency
Exposures
Distribution
Probability
of Response
Frequency
Value
O
50%
0%
0%
1
40%
25%
10%
2+
10%
30%
3%
Total frequency value
13%
Frequency value planning allows us to take our analysis a step
farther. The company may estimate that the monetary value
associated with 1% of the target market clicking through to the
target site is $10,000. The total value of the Internet program,
then, is ($10,000 x 13 =) $130,000.
Step G: Evaluating the Schedule
Obviously, the schedule's frequency value can be increased by
simply increasing the media weight. However, more weight
costs more money. Furthermore, in most cases, the increase in
value will be subject to diminishing returns, resulting from a
concave advertising response curve. To address this problem,
simple frequency value, whether in percentages or dollars, is
not as useful as frequency value per GRP, or in this case TRPs
(target market rating points). In the case portrayed in Exhibit
5, we don't know how many TRPs the schedule includes,
because we don't know how many high-level exposures (more
than 2) there are in the distribution. For illustrative purposes,
assume that the schedule included 65 TRPs. The value per
TRP would be (13%/65=) 20%. That is, 20% of all target
market exposures result in the desired audience behavior.
Second, the monetary value is ($130,000/65=) $2,000 per TRP.
As long as the cost per TRP is less than $2,000, the program
will pay out.
Step H: Comparing alternative schedules
Finding that one's media schedule yields a positive return
(referring to our discussion above) does not mean it is the best
possible schedule. Step H is shown in Exhibit 4 as a feedback
loop, in which planners begin developing a new media
schedule, in hope of finding a more efficient combination of
media vehicles. Cannon and Riordan (1994) spoke of "optimal
frequency planning." FVP follows their logic, but it recognizes
the fact that planners will never be sure the schedule is
optimal. Rather, it is only better or worse than other schedules
being considered.
38 Journal of Interactive Advertising Spring 2001
Step I: Implementing the Schedule
As with all media plans, the ad space or time should be
purchased in the most cost-effective manner possible. Our
emphasis on tools for developing effective media strategy in no
way detracts from the importance of these tactical issues.
Indeed, FVP can be used at the buying level as well to ensure
that the actual media buys hold to the principles of valueoriented media scheduling. But the basic parameters of the
buy will be determined by the schedule that emerges from the
evaluative process portrayed in Exhibit 4.
SUMMARY AND CONCLUSIONS
The premise of this paper is that convetional media planning
procedures, or at least the latest thinking regarding
conventional media planning, can also be applied to new
media. The term, "new media," is deliberately vague. New
media would include the full range of emerging media
alternatives, from global satellite television to wireless
interactive media. We have focused on the Internet, both
because of its enormous significance in today's world, and
because it has been to focus of allegations that conventional
media planning will not work on new media (see Hoffman and
Novak 2000). However, the Internet represents only one of a
much larger set of emerging interactive media (Katz 2000).
Our conclusion is not only that all media can be held to a
common standard, but that they actually should be. In a world
where efficiency is the watch word of business, we cannot
afford to trust millions of dollars to the whims of creative
judgment. Not that creativity is unimporant. It is more
important than ever! However, it is a necessary, not a
sufficient condition for media planning success. The system
we have proposed seeks to put all media on an equal footing,
estimating the actual cost/benefit of the programs in which
they are utilized.
The traditional argument against cross-media comparisons is
that media vary so much in their capabilities that they cannot
be meaningfully compared. We address this by suggesting that
each media program within a larger integrated marketing
communications effort plays a unique and important role. The
effectiveness of the program should be evaluated in terms of
the specific audience response the program is designed to
achieve. Thus, if a direct mail campaign has an average
CPMETM of $1,000, this must ultimately be compared to the
frequency value the program develops. Doing this would likely
show that direct mail is much more cost/beneficial for eliciting
actual consumer purchases that billboards, even though the
CPMETM for a billboard campaign would be much lower.
The system we have proposed develops measures of value for
an entire media schedule. It estimates the actual percentage of
a target population responding to the campaign in a desired
manner. By extension, establishing the value of this response
enables planners to put an actual dollar value on each media
program. These can be further evaluated by their cost
efficiency, evaluating the total response they are expected to
elicit from audience members by the media effort (GRPs) it
took to achieve this response.
The system is neither complete, nor perfect. First,
implementation requires a great deal of judgment. This, in
turn, requires additional theory. For instance, where in the
matrix shown in Exhibit 2 do various media actually fall? How
much does this vary by market segment? What are the realistic
ranges in which different advertising response curves fall?
How do we determine which one actually applies in any given
situation?
The second problem is that, as we consider more interactive
media, it becomes difficult to associate advertising response to
a specific number of advertising exposures (Pavlou and
Stewart 2000). This, in turn, invalidates the notion of the
advertising response curve.
The third problem with the proposed system is that the effects
of one type of media exposure might interact with another.
Indeed, they should interact with eachother if the concept of
IMC is truly being used effectively. After all, what is media
synergy if not the interactive effects of the various media. But,
when
media
exposures
interact,
evaluating
the
cost/effectiveness of each media program separately does not
provide the complete picture. For instance, the value of using
magazines to stimulate Internet usage cannot be determined
by evaluating the magazine and Internet programs separately.
The desired response to magazine campaign would be Internet
usage; Internet response would purchase, or some other
similar criterion. But if the magazine campaign were cut back
for lack of efficiency, the Internet campaign would suffer as
well - something that did not show up in the FVP analysis for
the Internet program.
None of these problems is insurmountable. They are merely an
indication that we need to do more work. In fact, the problems
are not new. With respect to the need for judgment, this has
always been the case in media planning. The system we are
39 Journal of Interactive Advertising Spring 2001
proposing is simply a method of making judgments more
systematic.
We may offer a similar response to the second problem. When
we examine the actual nature of advertising interactivity (see
Heeter 2000), we see that media have always been interactive
to some degree. The fact that many new media are more
interactive suggests that we have an even greater need to look
for ways to better account for the interactive effects, but it is
not a reason for discarding conventional response-oriented
planning systems.
Finally, with respect to media interaction, we must again
supplement our model with judgment. To say that we need to
consider how one media program affects another is not to
ignore the importance of media programs' non-interactive
value. Media campaigns have always interacted with each
other to some extent. The advent of IMC and the
incorporation of new types of media suggests that we should
redouble our efforts to quantify the effects of media
interactions, but, in the meantime, judgment will have to
suffice.
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ABOUT THE AUTHORS
Hugh M. Cannon (Ph.D., New York University, 1979) is the
Adcraft/Simons-Michelson Professor of Marketing at Wayne
State University, a position he has held since 1988. ; e-mail:
Hugh.Cannon@Wayne.edu.
Spring 2001