1. International Journal of Forecasting 10 (1994) 339-352
Strategic marketing forecasting, market segment selection
firm performance *
Noel Capon*, Peter Palij
Graduate School of Business, Columbia University, New York, NY 10027. USA
and
Abstract
In this study we investigate several hypotheses relating strategic forecasting to market segment selection and firm
performance. In the context of a strategic marketing simulation, subjects in 14 competitive industries made
strategic forecasts for market segment size and benchmark prices. Our results show that firms differentially select
those segments with attractive characteristics; that some strategic forecasts for these targeted segments are more
accurate than for non-targeted segments; that strategic forecasts are more accurate the higher the level of
competition; and that superior forecasting performance is positively associated with superior firm performance.
Implications and limitations of the study are discussed.
Keywords: Strategic marketing forecasting; Market forecasting; Market segment selection; MARKSTRAT
1. Introduction
Perhaps the most critical decision that senior
managers in multi-product, multi-market firms
must make is the choice of which products to
produce and which markets to address. In the
197Os, several strategic planning models were
developed to assist management in addressing
this problem. Although not universally accepted,
by the early 1990s these models and/or their
successors were woven into the fabric of
* Corresponding author. Tel: (212) 854-3466 or (212) 865
5332; Fax: (212) 749-3549.
‘“’Preliminary results from the non-degree executive pro-
gram subjects were presented at the November, 1991,
ORSAiTIMS Joint National Meeting.
strategic management at many major corpora-
tions (Capon et al., 1987; Greenley and Bayus,
1993).
The basic purpose of strategic planning models
is to provide managers with a coherent frame-
work to structure long-term planning. Although
strategic planning models display idiosyncratic
differences, they are typically presented as two
dimensional matrices in which the axes represent
market attractiveness and business strengths.
The models differ from each other in the manner
in which these dimensions are operationalized.
For example, in the GE/McKinsey screen, Ar-
thur D. Little and BCG portfolio models, market
attractiveness is represented respectively as a
weighted linear combination of such variables as
market growth rate, profitability, number of
competitors, and degree of regulation; as prod-
0169-2070/94/$07.00 @ 1994 Elsevier Science B.V. All rights reserved
SSDI 0169-2070(94)09007-R
2. uct life cycle stage; and as the forecast long-run
market growth rate (Wind, 1981).
Across these two dimensions, each model uses
a mix of measurable current variables, and
medium- and long-term forecasts. The appro-
priate values for current variables may be dcbat-
able, but the forecast variables represent a far
more difficult problem. Indeed, Capon and Hul-
bert (1985) lament the fact that virtually all
forecasting research at the firm level has ad-
dressed short-term forecasting (e.g. sales fore-
casts for production planning and inventory
management; short-term revenue forecasts for
cash Now management).
More recently, several researchers have
attempted to address firm-level forecasting issues
by using a strategic marketing simulation meth-
odology (LarreLhC and Gatignon, 1990). For
example, Glazer et al. (1989, 1990) examined
how closely short-term forecasts based on mana-
gerial judgment conformed to the formal prop-
erties of rationality. They found that one-year
forecasts for aggregate market size, average
industry price and number of marketed products
in four ‘industries’ were efficient but biased.
However, not only were these studies concerned
with short-term forecasting, the forecast vari-
ables were not particularly strategic. By contrast,
the study reported in this paper, using a similar
methodology, focuses specifically on strategic
forecasting issues.
Capon and Huibert (1985) coin the term
‘strategic forecasting’ to embrace those types of
long-term forecasts that are strategic for the
firm. Important dimensions requiring such fore-
casts include customer needs, competitive struc-
ture, competitive action, degree of regulation
’ Rclatcd arcas are role playing (Armstrong, 1988) and
new product forecasting (Mahajan and Wind, 1988). Role
playing research focuses on developing tools to overcome
managerial biases in forecasting. Most new product forecast-
ing research has avoided firm-level strategic forecasting
issues; segment ideIlti~c~~tion and sclcction arc typically
taken as given, and forecasts focus on prospects and the
optimal marketing mix for new products.
and market and/or market segment growth rates
(Wind, 1981; Wind and Robertson, 1983; Arm-
strong et al., 1987).’
The forecasting literature has generally taken
a pessimistic view of such medium- and long-
term forecasts, based primarily on their reliance
on managerial judgment (Armstrong, 1986). For
example, although Hogarth and Makridakis
(1981) recognize the importance of judgment for
these forecasts, they list over two dozen poten-
tially dist~~rting biases; and Paich and Sterman
(1993) find that even when conditions are delib-
erately designed to increase forecasting accuracy,
managers often fail to overcome judgmental
biases. In addition, Chakravarti et al. (1981) take
a pessimistic view of the value of managerial
judgment for marketing decisions in the context
of marketing decision models.’
The expert literature reaches a similarly pes-
simistic conclusion regarding the predictive abili-
ty of experts. Beginning with Thorndike’s (1918)
study of officer candidacy selection by military
experts, a long series of studies has found
.I
. . [that] expert judgment {a) is seldom better
than that of novices and (b) is easily improved
upon when replaced with simple equations.”
(Hammond, 1987, p. 3).
Hogarth and Makridakis recommend that long-
term forecasting focus on identifying sources of
environmental uncertainty, deliberately adopt
ambiguous rather than precise goals, rely on
simple time series models whenever possible,
and generally avoid complexity. Makridakis
(1986) echoes Hogarth and ~akridakis’ recom-
mendations while emphasizing the complemen-
tary roles of judgmental and quantitative fore-
casts.
Long-term forecasting research has focused on
macroeconomic variables in areas such as busi-
ness cycles, energy, population and transporta-
tion (Ascher, 1978; Adams, 1986); medium-term
forecasting research has focused on financial
‘See .Little and Lodish (ISSI) for a response on the
relative value of statistically based and judgmental models for
decision making in marketing.
3. N. Capon. P. Palij I International Journal of Forecasting 10 (1994) 339-352 341
analyses and capital allocation decisions (see nexus of customers, competitors and tech-
Hogarth and Makridakis, 1981). Microeconomic nologies, the marketing strategist must segment
research has emphasized industry-level forecasts the market, select those segments in which the
as inputs to national-level macroeconomic fore- firm should compete, and discard others (Hul-
casts (Adams, 1986, pp. 161-165). Little work bert, 1985, pp. 30-31). Segment selection, and
has addressed such firm-level strategic forecast- positioning decisions within the segment, repre-
ing problems as market or market segment sent the bridge between the SBU strategy and
growth rates. Marketing research has stressed tactical marketing actions that the firm executes
product forecasts at the industry level. leaving in the market place. It is at the market segment
firm-level strategic and market segment forecasts level that critical resource allocation decisions
needing extensive exploration (Armstrong et al., are made for securing customers at the expense
1987). In a rare exception, Cortes-Rello and of competitors. The ability to predict key dimen-
Golshani (1990) use an expert system meth- sions of available market segments over the
odology to predict 10 year market demand for a medium and long term is a critical strategic
capital good. necessity.
Based on the literature, an awkward situation
arises. Strategic planning models are the princi-
pal tools available for assisting senior managers
in making strategic choices, and these models
use as inputs strategic forecasts of key variables.
However, these strategic forecasts, typically
based on managerial judgment, are generally
viewed as frequently inaccurate and biased.
Furthermore, little research has addressed the
question of improving such forecasts.
In this paper we develop several hypotheses
relating to the use of strategic planning models,
the relationship between strategic forecast ac-
curacy and market segment selection, and be-
tween strategic forecast accuracy and firm per-
formance. Next, we test these hypotheses using a
large sample of strategic market forecasts se-
cured in the context of the MARKSTRAT
simulation (Larredhe and Gatignon, 1990). Final-
ly, we discuss the results, explore some limita-
tions and make recommendations for future
research.
Strategic planning models provide a useful
framework for the marketing strategist to struc-
ture the set of decision options for selecting
those market segments in which the firm will
compete. Using the market attractiveness, busi-
ness strengths paradigm, the received wisdom of
strategic planning models states that the strateg-
ist should strive to identify, then select for effort,
those market segment options that display high
levels of market attractiveness, where the firm
also has substantial business strengths (Kotler
1984, p. 61; Day, 1990, p. 200).
Hl: The firm’s selection of market segments to
address is positively related to their market
attractiveness, and to the firm’s current business
strengths in those market segments.
2. Development of hypotheses
2.1. Hypothesis 1
Perhaps the broadest use of strategic planning
models is at the strategic marketing level. In the
context of a strategic business unit (SBU)
strategy that broadly defines the competitive
In order to test this hypothesis, we focus on
the product portfolio matrix developed by the
Boston Consulting Group (BCG). In this
strategic planning model, market attractiveness
is defined as the expected market segment
growth rate; we use two measures-the medium-
term market segment size, and the annualized
growth rate needed to reach the expected
medium-term market segment size from the
current size. The BCG approach operationalizes
business strength in the market segment as the
firm’s current relative market share (RMS), and
directly links RMS to profitability (Henderson
and Zakon, 1980; Day, 1986). The resource
4. 342 N. Capon, P. Palij I International Journal of Forecasting IO (1994) 339-3.52
allocation actions taken by the firm in the follow-
ing periods are used to indicate management’s
decision to focus on the segment.”
The BCG approach to strategic planning has
received mixed reviews. Several writers view the
model positively (e.g. Dutton et al., 1983;
Burke, 1984) and some empirical evidence sug-
gests that it is widely used in industry (e.g.
Capon et al., 1987). However, it has fallen out of
favor among academics (e.g. Wensley 1982;
Seeger 1984) largely because of its reliance on
the experience curve and the strongly implied,
and much debated, linkage between market
share and profitability (e.g. Jacobson, 1988).
However, in the context of the MARKSTRAT
simulation, use of this framework is quite appro-
priate as strong experience curve effects are
embedded in both the production, and research
and development functions. Consequently, the
correlation between market share and profitabili-
ty is high, 0.89 for the industries in this study.
2.2. Hypotheses 2 and 3
Broadly speaking, three perspectives govern
the strategic forecasts required by strategic plan-
ning models. Espousing a normative view, tradi-
tional neoclassical economic models assume ‘hy-
perrational’ behavior (Simon, 1978): managers
are sequential, exhaustive and optimal analysts
of all available data. Managers carefully forecast
each relevant variable. in each market segment,
in each period, in order to exploit opportunities
as competitive dynamics and other conditions
change within segments. For such forecasters,
accuracy should be independent of factors such
as market segment participation and competitive
intensity.
In direct contrast, empirical forecasting evi-
’ Note that sales volume in a market segment ex post does
not imply that the firm has made the strategic decision to
target that segment for effort ex ante. for a variety of reasons
including inadequate segment specification, improper im-
plementation and variety seeking by customers, firms may
secure significant market share in non-targeted market seg-
ments
dence indicates little hope for any accuracy in
making such strategic forecasts. As noted previ-
ously, these forecasts are highly susceptible to
bias (Hogarth and Makridakis, 1981); the ac-
tion-outcome-feedback loop is weak, typically
stretching beyond a product development cycle;
relatively precise values are required for effec-
tive model use; and relevant variables are affect-
ed by complex, diverse and often unknown
forces. Since any accurate forecasts are seren-
dipitous, accuracy should be independent of
factors such as market segment participation and
competitive intensity.
Rather than adopting either polar perspective,
the traditional neoclassical economic normative
approach or the pessimism of extant empirical
studies, each of which leads to similar predic-
tions regarding forecast accuracy, we choose to
work within the bounded rationality paradigm
(Simon, 1957, 1979). Bounded rationality implies
an intermediate position on forecast accuracy.
Forecasters are unable to cope with the mag-
nitude of processing tasks required for hyper-
rationality and so use processing short-cuts such
as heuristics and schemata. However, as a prob-
lem’s salience increases, a variety of active
strategies are adopted, including those hypoth-
esized by the neoclassical perspective
(Eisenhardt and Zbaracki, 1992). Thus, as re-
sources (e.g. marketing research. product de-
velopment. production, advertising and promo-
tion) are focused on a market segment, its
increased salience leads forecasters to pay in-
creasing attention to strategic variables relevant
for that segment. Ceteris paribus, increased
attention should lead to more accurate forecasts
so that the bounded rationality perspective leads
directly to Hypothesis 2.
H2: The firm’s forecasts of strategic dimensions
for target market segments are more accurate
than for avoided segments.
The notion of salience relates both to those
segments that are targeted for effort, and to the
degree of difficulty in competing in those seg-
ments. Thus, we anticipate that, ceteris paribus,
5. N. Capon. P. Palij I International Journal of Forecasting 10 (1994) 339-352 343
increased effort, and therefore increased accura-
cy, should be related to increased levels of
competition in targeted segments. Increased ef-
fort under these conditions is consistent with
anecdotal evidence from managers that effort on
problems typically exceeds effort on oppor-
tunities.
H3: The accuracy of the firm’s strategic forecasts
in target market segments increases as the level
of competition increases.
2.3. Hypothesis 4
Strategic planning advocates posit a positive
relationship between the use of strategic plan-
ning models and firm performance. This positive
view of the influence of managers and strategic
planning on firm performance has been chal-
lenged by research focusing on the importance of
internal and external environmental factors (e.g.
Hannan and Freeman, 1977; Pfeffer and Salan-
cik, 1978; Fredrickson and Mitchell, 1984). Em-
pirical findings are mixed, but generally suppor-
tive of a positive link between strategic planning
and firm performance (Armstrong, 1982; Capon
et al., 1987, 1994; Boyd, 1991).
As discussed above, good strategic planning
requires the ability to make good strategic fore-
casts. Indeed, in Urban et al.‘s (1990) strategic
planning model for the automotive industry, nine
of 24 of the model’s parameters were substantial-
ly or entirely based on judgmental forecasts
made by managers. Clearly, the success of such
formal models in providing useful input to deci-
sion making depends upon the accuracy of the
underlying parameters. However, regardless of
forecasting accuracy, many strategic variables
such as level of diversification and product line
maturity are also expected to impact firm per-
formance. Thus, we expect a weak positive link
between forecast accuracy and performance.
H4: The greater the accuracy of the firm’s long-
run strategic forecasts, the better its perform-
ance.
3. Method
3.1. Direct experiment
Methodologies available for testing the hy-
potheses range from laboratory experiments to
field studies. Laboratory studies have the benefit
of control, but are seriously challenged by topics
requiring a series of complex decisions evolving
over time (Singer and Brodie, 1990). Field
studies capture complexity but typically at the
cost of lack of control. Simulations conducted as
direct experiments (Sterman, 1987) offer an
intermediate technique for testing strategic fore-
casting hypotheses related to complex decisions
(Glazer et al., 1990; Green and Ryans, 1990).
The MARKSTRAT 2 simulation (Lar-
reche and Gatignon, 1990) provides a realistic
environment in which participants seek long-run
success through a series of strategic and tactical
marketing decisions, moderated by feedback on
the results of their decisions (Kinnear and Klam-
mer, 1987). Five firms compete in an established
market (sonites) comprising five well-defined
market segments, and in a second undifferen-
tiated independent market (vodites) that is un-
tapped at the beginning of the simulation. Each
firm starts the simulation with a differing mix of
corporate strengths and weaknesses (some firms
are stronger, some firms are weaker), and with
two sonite products in different competitive
positions; each firm may offer up to five prod-
ucts. For each cycle of the simulation, a team
managing its firm assesses the results of prior
decisions, evaluates marketing research data,
and allocates its limited budget to R&D, market-
ing mix expenditures and marketing research.
The firm decisions are run, and results returned
for the next decision cycle. Firm performance
(e.g. sales, profit, market share) is a function of
firm and competitor decisions, individual firm
capabilities including budgets, and the evolving
MARKSTRAT economy. Initial firm parame-
ters, underlying economic conditions for each
industry of five firms, and the general pattern of
segment growth and preferences are set identi-
cally at the start of each simulation.
MARKSTRAT focuses on short- and medium-
6. term decisions; long-term decisions such as aban-
doning both sonite and vodite markets, investing
in new technologies, or adding new distribution
channels are not available. Short-term decisions
are set by the ‘annual’ decision cycle. Medium-
term decisions reflect changes in resource alloca-
tions among market segments, and investments
in product development. New product entry into
a market segment implies a 2-4 year product
development cycle: l-2 years to develop a prod-
uct, 1 year for iIltroduction and feedback on
market segment acceptance, and 1 year for
product modification.
3.2. Subjects
Subjects were participants in full-time MBA,
executive MBA and non-degree executive pro-
grams conducted by a leading business school.
Full-time MBA students, representing a range of
business experience, were participating in a
second-year elective course in marketing
strategy; executive MBA students. participating
in a required advanced course in marketing
strategy, were mid-level executives (median age
33) attending classes once per week under firm
sponsorship; non-degree students were senior
marketing executives participating in a l-week
residential advanced program in strategic mar-
keting.
3.3. Procedure
The approximately 250 subjects were placed in
70 three to five person groups. (Full-time MBA
groups were self selected; the others were ran-
domly assigned.) The 70 groups comprised 14
industries of five teams (firms): four full-time
MBA, six executive MBA, and four non-degree
students. Data were collected as part of required
classroom activities. For both MBA groups, the
eight decisions were made weekly during the
semester; the non-degree students made seven
decisions at a rate of two decisions per day.
At the start of the simulation. each firm
received a summary of results for the prior year
(zero), a complete set of aggregate market re-
search data, and a decision support package
comprising a variety of spreadsheet tools to assist
in analyzing firm and marketing research data
(James et al., 1991). After a decision was run,
each team received its firm report and the
specific marketing research data it had re-
quested; use of the decision support software
was optional. Upon completion of the simula-
tion, each team was required to make formal
oral (to the class) presentations explaining ac-
tions taken, perceptions of industry events and
lessons learned. Such presentations by teams in
previously run industries provided pretest guid-
ance.
3.4. Variables
3.4.1. Strategic marketing variables
Informal discussions with previous MARK-
STRAT participants revealed a large and com-
plex array of variables perceived as strategic:
these included internal capabilities, budget allo-
cations, size and growth of the market segments,
competitive conditions in each market segment
and changes in consumer preferences.
We selected as strategic variables both market
segment size (units) and market segment growth
rate, each of which addresses the market attrac-
tiveness dimension of strategic planning models.
In addition, we selected market segment leader’s
price. Price plays an important strategic role in
MARKSTRAT. First, it provides a measure of
profit potential, given the firm’s Iikely product-
ion cost. Second, it captures customer prefer-
ences by playing a direct role as a product
positioning attribute in the sonite market, and as
an indirect positioning attribute for vodites.
3.4.2. Forecast market segment size
This variable is a forecast of the number of
units sold into the segment by all competitors 3
years ahead.
3.4.3. Forecast market segment growth rate
This variable is the compound annual growth
rate required to reach the forecast market seg-
ment size from the current market segment size.
7. This variable is the forecast price, 3 years
ahead, of the product having the largest market
share in the market segment,
3.4.5. Firm performance
Direct measures of firm performance in
MARKSTRAT are aggregated across products
and segments. Several candidate variables were
examined: sales units, sales revenues, unit mar-
ket share, revenue market share, gross profit
share and net profit. The smallest of the 15
correlations between these variables was 0.88.
Revenue market share was selected because it
both permits identical treatment of the different
sonite and vodite markets, and normalizes firm
results across the sample industries.
3.4. (5;.Market attractiveness
This segment-level variable was oper-
ationalized in two ways: as the forecast market
segment size and as the implicit forecast market
segment growth rate (see above), The formal
debriefs presented by previous MARKSTRAT
teams indicated that participants generally as-
sessed market attractiveness by estimating the
future size of a market segment, then checking
to see if the growth rate seemed reasonable.
3.4.7. Business strength
This segment-level variable was oper-
ationalized as the firm’s current relative market
share. Relative market share is the firm’s market
share (of all products in a segment), divided by
the market share of the segment leader (or by
the number two share firm if the focal firm was
the segment leader).
.3.4.8. Target market segment
In the sonite market, the five market segments
are defined as a set of ideal points in a three
dimensional positioning map whose axes are
perceived price, perceived power and perceived
design (the latter two are physical attributes).
Two methods assess the target segments of
current products-product design characteristics
and advertising positioning; target segments for
future products are identified from the design
characteristics of active R&D projects. In each
case, the minimum Euclidian distance from the
product to each of the five ideal points is defined
as the market segment targeted.
The three methods vary by time horizon;
product design characteristics and ideal points
are measured contemporaneously; the advertis-
ing positioning decision in period t is compared
with the revealed actual ideal points in period
t + 1; the R&D design characteristics in period t
are compared with the actual ideal points in
period t t 3. For the undi~erentiated vodite
market, any product entry, or R&D project,
respectively, defines a targeted market.
The measure of target market segment em-
ployed for any decision period was the union of
these three measures under the rationale that for
any target market segment, good strategic fore-
casting is a critical matter.
3.4.9. Competitive activity
This variable is defined by adapting the Jac-
quemin and Berry (1979) entropy measure of
diversification. Level of competition is defined
as:
where Pi is each firm’s market share in the
market segment; the larger the index, the greater
the level of competitive activity. This measure is
sensitive to the number of competitors, the
distribution of market shares among com-
petitors, and the mix of cfose and distant com-
petitors (Palepu, 1085).
Tradition&y, competitive-type measures have
focused on market concentration (e.g. concen-
tration ratios-total market shares of the n
largest firms; Herfindahl’s index, H-sum of the
squared market shares for each firm) as in-
dicators of public policy concern, rather than on
competitiveness per se. For public policy analy-
sis, Stigler (1983) recommends the Eferfindahl
index over entropy measures, based in part on its
insensitivity to firms with small market shares.
However, since the MARKSTRAT enQiro~ment
comprises a fixed number of firms, each with the
8. 336 N. Capon. P. Palij I International Journal of Forecasting 10 (1994) .U9-352
ability to offer multiple products to multiple
segments, the dimensions captured by the en-
tropy measure seemed more appropriate as in-
dicators of a segment’s competitiveness.
3.4.10. Forecast accuracy
Several indices of forecasting accuracy are
available. However, since market segment sizes
varied by an order of magnitude, we rejected
both the simple error and mean square error
measures (Mahmoud, 19S6, pp. 509-511). In-
stead, we selected the absolute percent error
(APE) and mean absolute percent error
(MAPE). These measures give equal weight to
over- and under-prediction, reflecting the
strategic issue that for market segment size and
growth rate, under-prediction may result in op-
portunity losses as, or even more, serious than
direct losses resulting from over-prediction.
MAPE was used to aggregate forecast accuracy
across segments.
3.5. Data
Data to test the hypotheses were drawn from
firm decisions, the results for each firm from
running the simulation, and 3-year forecasts of
unit size and leader’s price-by market segment
for sonites, by total market for vodites. Forecasts
for each sonite segment and the vodite market
were collected from each team. Forecast data
were collected at periods 0 through 5 together
with the firm decisions; the six forecast/actual
pairs comprised periods O/3; l/4; 21.5; 316; 417;
518 (five pairs for the seven period industries). In
total, 2400 forecast/actual pairs were collected
from the 14 industries.
3.6. Analysis
Ordinary least squares (OLS) and logistic
regression was used to test the hypothesized
relationships. Firm and type of student program
effects were entered as dummy covariates in
each analysis. Data were omitted in those in-
stances when students did not provide forecasts,
when actual sales in vodites were zero (even
though some groups provided forecasts) and
when the APE exceeded 200%. Less than 4% of
possible data were dropped, primarily in indus-
tries with late introductions of vodites.
For Hypothesis 1, the dependent variable was
the target market segment indicator (0, 1);
independent variables were, alternatively, fore-
cast market segment size and forecast market
segment growth rate together with relative mar-
ket segment share and the covariate dummies.
Two logistic regression models examined these
relationships.
Hypotheses 2 and 3 were tested simultaneous-
ly via two OLS regression analyses in which the
APES for forecast market segment size and
forecast market leader’s price provided alter-
native dependent variables. Independent vari-
ables were the target market segment indicator
(Hypothesis 2), level of competition and the
target market segment indicator X level of
competition interaction (Hypothesis 3). For each
regression analysis, other independent variables
were the covariate dummies, plus one-period
lagged dependent variables that were included to
account for potential autocorrelation effects.
OLS was also used to model Hypothesis 4.
The dependent variable was total firm sales in
units (across all segments) for each period.
Independent variables were the MAPE of fore-
cast market segment size, the MAPE of forecast
market segment leader’s price, a period indicator
that captured inherent trends in the MARK-
STRAT environment, and the firm and type of
student effect covariate dummies. Several addi-
tional covariates captured major strategic deci-
sions that might reasonably have been expected
to be related to firm performance: the number of
sonite market segments targeted and the pres-
ence/absence in vodites captured a diversifica-
tion dimension; the research and development
budget captured product line maturity; and the
number of salespeople, and budgets for advertis-
ing and market research captured other critical
strategic dimensions in the MARKSTRAT simu-
lation.
4. Results
Hl: The firm’s selection of market segments to
address is positively related to their market
9. N. Capon, P. Palij I International Journal of Forecasting 10 (1994) 339-352 347
attractiveness, and to the firm’s current business
strengths in those market segments.
This hypothesis is supported for both oper-
ationalizations of market attractiveness and for
business strength (see Table 1). The positive and
significant coefficients for forecast market seg-
ment size and forecast market segment growth
rate, and for relative market share in each of the
two analyses, indicate that firms acted according
to the received wisdom of strategic planning
models: participate in market segments provid-
ing large potential opportunities where the firm
is currently strong.
H2: The firm’s forecasts of strategic dimensions
for target market segments are more accurate
than for avoided segments.
This hypothesis was tested by two regression
analyses, one each for the APE of forecast
market segment size and forecast market seg-
ment leader’s price. Partial support was adduced
for this hypothesis inasmuch as the coefficient for
target market segment in the regression of fore-
cast market segment leader’s price was both
negative and significant (see Table 2). (The
coefficient for target market segment was not
significant for forecast market segment size.) The
negative sign indicates smaller forecast errors,
hence increased accuracy, for targeted market
segments.
Table 1
Relationship between market attractiveness, business
strengths and market segment selection
Independent variable
Forecast market segment size
Forecast market segment
growth rate
Relative market share
Firm 1
Firm 2
Firm 3
Firm 4
Full-time MBA students
Executive program participants
Constant
* pco.05; **p<o.o1.
Coefficients
0.00227**
1.272*”
1.590** 1.628**
-0.243 -0.253
-0.321 -0.294
-0.162 -0.199
-0.319 -0.336’
-0.183 -0.172
-0.159 -0.237
-0.205 0.559**
Table 2
Relationship between market segment selection and strategic
forecasting accuracy
Independent variable Coefficients
Forecast market Forecast market
segment segment
size leader’s price
Targeted market 0.286 -0.0722**
segment (TMS)
Level of competition
(LC)
-0.672** -0.0593**
TMS x LC
One year lagged size
One year lagged
leader’s price
-0.172 0.0241*
0.246”*
0.3so**
Firm 1
Firm 2
Firm 3
Firm 4
Full-time MBA
students
-0.220 0.0076
-0.0028 0.0177
-0.174 -0.0010
-0.553 0.0120
-0.204 -0.0157
Executive program
participants
0.0814 0.0107
Constant
F (10.1899)
Adjusted RZ
* P<O.O5; P<O.Ol.
1.643** 0.205**
40.08** 54.03**
0.17 0.22
H3: The accuracy of the firm’s strategic forecasts
in target market segments increases as the level
of competition increases.
This hypothesis was supported for forecast
market segment leader’s price inasmuch as the
coefficient for the target market segment x level
of competition interaction was significant (see
Table 2). However, the coefficient for the main
effect of level of competition was negative and
significant indicating that, regardless of whether
or not the segment was targeted, the accuracy of
the firm’s strategic forecasts improved as the
level of competition increased. The key factor
driving the interaction effect was the relatively
low level of forecast accuracy in low competi-
tion, non-targeted market segments. Forecast
accuracy was highest for high competition,
targeted market segments, significantly greater
than for both targeted, low competition and non-
targeted high competition segments; the latter
two segments were not significantly different
from one another.
10. The main effect for level of competition was
found also for forecast market segment size. The
significant coefficients for one year lagged vari-
ables in each regression, forecast market seg-
ment size and forecast market segment leader’s
price, indicate the presence of autocorrelation
that these variables removed.
H4: The greater the accuracy of the firm’s long-
run strategic forecasts. the better its perform-
ance.
This hypothesis received mixed support (see
Table 3). The MAPE for forecast market seg-
ment leader’s price was significant and negatively
related to performance, thus supporting the
hypothesized relationship, but the MAPE for
forecast market segment size was not significant.
As expected, the period was significant, as were
three of four firm dummies. The magnitude and
direction of the firm coefficients reflect the
typical rankings of firm performance; over a
large number of industries, firms 1, 3 and 4
typically outperform firms 2 and 5,
Table 3
Relationship between forecast accuracy and firm perform-
ancc
Independent variable
Forecast market segment size (MAPE)
Forecast market segment leader’s price
(MAPE)
Number of sonite segments
Vodites
Advertising hudget
R&D budget
Number of salespeople
Marketing research hudgct
Period
Firm 1
Firm 2
Firm 3
Firm 4
Full-time MBA students
Executive program participants
Constant
F (15,384)
Adjusted R’
* P < 0.05; P < 0.01.
Coefficients
0.012
--t).o665*
0.0074
0.0054
0.146**
0.0099**
0.0570**
-0.0003
-0.0240**
0.0467**
0.0093
0.0309**
0.0242*
0.0123
0.0145
-0.0306
63.6**
U.71
Three of the six strategic variables. research
and development budget reflecting product line
maturity, advertising budget and number of
salespeople were also significant. The presence
of the moderating variables in the equation did
not change the direction or significance of the
coefficient for forecast market segment leader’s
price, nor affect the significance of forecast
market segment size. The presence of these
variables was, however, important in raising the
R’ from 0.16 when they were not included, to
0.71.
The difference in results between forecast
market segment size and forecast market seg-
ment leader’s price when testing Hypotheses 2, 3
and 4 may reflect differences in forecast task
complexity. Thus, depending on the particular
segment and industry, overall segment size
changes for sonites through the simulation
ranged from - 10% to + 150%. and period-to-
period changes reached 100% on occasions.
~Period-to-period changes were even iarger for
vodites since sales were zero at the start of the
simulation.) By contrast, the market segment
leader’s price demonstrated far less variance, in
large part because the ideal point for price in
each market segment moves in a constrained
linear manner.
5. Discussion
In this paper we set out to investigate several
hypotheses related to strategic forecasting in the
context of strategic planning models. We argued
that for managers to make good strategic deci-
sions, they must be able to make good judg-
ments about the medium- and long-term en-
vironment. In a sense, our work is an extension
of Glazer et al.‘s (1989, 1990) important MARK-
STRAT studies that found a modest positive
relationship between forecast accuracy and firm
performance. However, the forecasts collected
by Glazer et al. were not particularly strategic.
They used short-term (1 year) forecasts com-
pared with our medium-term (3 year) forecasts
that reflect one or two product development
cycles. In addition, their three forecast variables
11. N. Capon, P. Palij I International Journal of Forecasting 10 (1994) 339-352 349
(market size, average industry price, and number
of marketed products) are not particularly re-
lated to key strategic decisions at the market
segment level, the focus of the MARKSTRAT
simulation. By contrast, our forecast variables of
market segment size and growth rate, and mar-
ket segment leader’s price, are highly strategic.
Furthermore, the majority of our subjects were
practising managers versus their exclusive re-
liance on MBA students; and, finally, our find-
ings are based on 14 industries whereas their
data is drawn from just four industries.
Despite the inherent difficulties in forecasting
beyond a development cycle, our hypotheses
were largely, though weakly, supported. In the
context of portfolio models of strategic planning,
we found that firms tended to select those
market segments that provided the greatest op-
portunities in terms of fast growth and high
potential size, where the firm also had substan-
tial business strengths as indicated by its relative
market share (Hypothesis 1). The positive impli-
cation from this finding is that portfolio dimen-
sions are relevant to market segment selection;
the negative implication is that misperceptions
about market segment size and growth rate may
lead to poor segment selection and risk the
problems associated with escalation of commit-
ment (Brockner et al., 1986).
Our results also indicate that the differentially
polar perspectives taken by neoclassical econ-
omics (positive) and traditional forecasting re-
search (negative) regarding long-term forecasting
accuracy may be premature. Consistent with a
bounded rationality perspective that managers
cannot evaluate each segment in each period
with the same degree of effort, we find that to
some extent salience of the forecasting context
impacts forecast accuracy. Thus, subjects were
better able to forecast market segment leader’s
price in those market segments to which they
had committed resources (Hypothesis 2). Such
differential forecast accuracy, as with misperceiv-
ing market segment potentials, risks a focus on
market segments to which prior commitments
have been made, thus raising the spectre of an
escalating commitment cycle with potentially
devastating results. Indeed, as managerial atten-
tion focuses on current market segments, oppor-
tunities further afield may be missed.
We also found that, consistent with our per-
spective that salience increases forecasting ac-
curacy, forecast accuracy for market segment
leader’s price was greatest in those market seg-
ments that were both targeted, and in which the
firm faced high levels of competition. An un-
expected result was the finding that managers
were better able to forecast both market segment
size and market segment leader’s price the more
competitively intense the market segment, re-
gardless of whether the segment was targeted.
One explanation for these results is that differ-
ing market segment profiles lead to differing
salience levels for managers. Targeted competi-
tive segments command the most attention
because committed resources are at high risk. By
contrast, non-targeted low competitive segments
command the least attention not only because
the firm has not committed resources but
because another firm (or firms) dominance
makes it unlikely that the firm will enter in the
future. Intermediate salience levels result from
either committed resources, but low risk because
of low competition, or in high competition seg-
ments, indicating opportunities for entry, that
are currently non-targeted. Such an explanation
accounts for both the main effect for level of
competition and for the interaction effect for
forecast market segment leader’s price.
Finally, we secured partial support for a posi-
tive relationship between strategic forecast ac-
curacy and firm performance. This result is
consistent with the generally positive, but mixed,
results on the relationship between strategic
planning and firm performance (e.g. Capon et
al., 1987, 1994). Briefly stated, this result dem-
onstrates that effort spent on the forecasting
dimension of strategic planning pays off.
An important issue in testing performance
relationships such as these is model specification
error. Thus, in his meta-analytic review of the
planning-performance literature, Boyd (1991)
highlights the absence of moderating variables as
one of six major limitations of these studies. In
an attempt to deal with this problem, Capon et
al. (1987) employed four moderating variables in
12. 350 N. Capon, P. Palij i International Journal of Forecasting 10 (1 YW) .33Y-3.52
their study of planning-performance relation-
ships in the Fortune500(scale, degree of diversi-
fication, product line maturity and industry). In
this study, industry is an irrelevant variable as all
firms operate in the same industry. However,
firm size was accounted for by the firm and
period dummies; degree of diversification by the
number of sonite segments targeted and pres-
ence/absence of vodite variables; and product
line maturity by the research and development
budget. In addition, we captured the other major
strategic variables in the MARKSTRAT simula-
tion, namely, number of salespeople, and the
advertising and marketing research budgets.
Additional methodological issues relate to the
five other major criticisms of planning-perform-
ance studies were noted by Boyd. Notwithstand-
ing its simulation basis, compared with real
world forecasting, this study did not use small
and non-representative samples; collected data
directly from the principals competing in the
simulation rather than from surrogates; mea-
sured forecasting error as a ratio-scaled variable
compared with categorical or simple ordinal
variables; used time series versus cross-sectional
analyses; and used multiple forecasting in-
dicators.
Two important methodological issues are
raised regarding the use of MARKSTRAT simu-
lations: the need to account for firm effects and
the use of student subjects. First, the frequent,
and in the case of performance-related issues
highly significant, presence of firm effects sug-
gests that we should critically examine results
from MARKSTRAT simulations that do not test
for such effects (Ross, 1987). However, the
typical criticism of using MBA subjects in studies
such as these is somewhat mitigated by our
results. In none of the five analyses (regression
and logistic regression) was any student program
variable significant. In other words, there is no
evidence that full-time MBA students behaved
any differently from the two executive groups.
Perhaps the reason for this finding is the increas-
ing maturity of MBA students who in this study
averaged 27 years of age and had significant
industrial experience.
Notwithstanding the relatively positive results
from this study, several limitations are evident.
First, even though the MARKSTRAT simula-
tion provides a realistic simulation of the real
world, it is only a simulation. We cannot make
the leaps to real world practice; real world
results must await well-designed field studies.
Second, even though we were able to support
several hypotheses relating to strategic forecast-
ing, we did not investigate the methods by which
these strategic forecasts were made. Subjects in
the study could have used a variety of different
methods in the general categories of judgment,
counting, time-series and association (Georgoff
and Murdick, 1986). Future work should investi-
gate the effects of different ways of making
strategic forecasts. In addition, echoing Paich
and Sterman (1993), methods for measuring the
intrinsic difficulty of forecasting across market
segments are needed. Such effort would also
help address the need for a dynamic measure of
market competitiveness.
Third, the MARKSTRAT environment is rel-
atively benign. The product life cycles for sonites
and vodites are fairly predictable and, in general,
market segments evolve in an orderly manner;
major disturbances are driven by the activity of
direct competitors. These conditions correspond
to ‘convergent’ periods in Tushman and
Romanelli’s (1985) punctuated equilibrium
model of organizational development. Tushman
and Romanelli also hypothesize that firms enter
periods of ‘reorientation’ (such as IBM and
General Motors entered in 1992), driven by
sustained low performance and/or rapid environ-
mental changes, which result in discontinuous
and radical shifts in core values and beliefs, firm
mission and strategies, and in internal organiza-
tion and management processes. In their model,
organizations progress through convergent
periods, punctuated by reorientations that de-
mark and set bearings for the next convergent
period.
This study is essentially conducted during
periods of convergence where environmental
change is incremental in nature and there is
considerable opportunity for feedback learning.
We did not study periods of reorientation, driven
by major environmental shifts and shocks, where
13. N. Capon, P. Palij I International Journal of Forecasting IO (19%) 339-352 351
competitive challenges come from new direct
entrants, indirect competitors, and suppliers and
customers engaged in vertical integration strate-
gies. The problems of strategic forecasting in
periods of reorientation, with its limited possi-
bilities for feedback learning, are much more
difficult. However, arguably the payoff to good
strategic forecasting in these periods is at its
greatest.
Acknowledgments
The authors would like to thank Don
Lehmann, Bari Harlam and two reviewers for
comments on earlier drafts of this paper.
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Biographies: Noel CAPON is Professor of Business at
Columbia Business School. His teaching and research inter-
ests are in the areas of strategy and planning. and sales force
management. He is coauthor of Corporate Strategic Planning,
a study of planning practices in major U.S. manufacturing
corporations. A companion book on the underpinnings of
corporate financial performance is in an advanced stage of
preparation. Hc acknowledges support from the Redwoord
Foundation.
Peter PALIJ is a doctoral candidate at Columbia Business
School. His teaching and research interests are in the areas of
marketing strategy. decision making and competition. He
acknowledges support from the Institute for the Study of
Business Markets.