Download Utilizing Consumer Expectational Data to Allocate Promotional Efforts

Utilizing Consumer
Expectational Data to
Allocate Promotional Efforts
basis for segmenting a potential market for any product
T HE
has important implications for effective marketing and pro-
J. ALEX MURRAY
motional strategies. The traditional approach to segmenting the
market has been to differentiate by demographic or regional characteristics, limiting the dimensions in which a market could be
scrutinized.' However, progressively more efficient approaches to
market segmentation are required if optimal results are to be
achieved from product promotion.
The central purpose of this paper is to illustrate the application
of a forecasting technique to the problem of segmenting a highly
fiuctuating consumer market—the durable goods market—to best
satisfy the necessary criteria for efficient segmentation. The advantage to the marketing manager of knowing beforehand which
regions hold the largest sales potential is obvious. Preliminary
tests indicate that use of consumer expectational data will be an
important step in locating market targets for the firm selling consumer durable goods.-
The autbor describes a dynamic, time - related approach
to allocating promotional efforts to selected markets. Consumer Expectational Data are
used to segment the market
more efficiently and to optimize the company's overall
marketing program, particularly the impact of its promotional strategy.
Journal of Marketing,
1969), pp. 26-33.
Vol.
33
(April.
Dynamic Need in Market Segmentation
The success of any marketing segmentation program will ultimately depend on the qualifying factors used in the segmenting
process. In a recent article analyzing segmenting techniques,
Brandt defines these as "the process of grouping individuals whose
expected reactions (promotional elasticities) to the producer's marketing efforts will be similar during a specified time period."'' In
addition, an important part of the total segmenting process for the
marketer is to anticipate shifts in sub-segments so that promotional
effort can be optimized within the economic constraints of the firm.
If a firm's product sales curve constantly fiuctuates over both
time and region, then a matching dynamic strategy is required to
allocate promotional resources efiliciently. The benefit of an index—
Optimal Strategy Index (OSI)—which would allow manufacturers
' The argument that socioeconomic variables do not provide an adequate basis for market segmentation has been disputed. See Frank
M. Bass, Douglas J. Tigert, and Ronald T. Lonsdale, "Market Segmentation: Group Versus Individual Behavior," Journal of Marketing
Research, Vol. V (August, 1968), pp. 264-270.
2 Jagdish N. Sheth, "A Review of Buyer Behavior," Management Science, Vol. 13 (August, 1967), pp. 718-755.
"* Steven C. Brandt, "Dissecting the Segmentation Syndrome," JOURNAL
OF MARKETING, Vol. 30 (October, 1966), pp. 22-27, at p. 25.
26
Utilizing Consumer Expectational Data to Allocate Promotional Efforts
Willingness to Buy
27
Ability to Buy
present stock of durables,
priority pattern for durable
good purchases, anticipated
price changes, age and condition of present durable
goods , etc. ,
household's financial condition at time t, anticipated
financial condition at time
t+1. Job situation at time t,
t+1, credit availability and
cost, overall economic environr.er.t, etc . ,
\
Probability of
Purchasing durable
good J, in time
period t+1
FIGURE
1. Formulating the household's purchasing decision.
distributing nationally (or internationally, if considering the North American Market) the advantage of
being able to "zero in" on highly potential markets
should be investigated. The advantage of being able
to anticipate locations of demand will lower stock-out
costs and be of help in the firm's logistics planning.
Criteria for the Basis of Market Segmentation
A search of the relevant literature discloses many
different criteria for selecting various bases used in
segmenting a potential market.^ Each particular
measure has specific applicability for different product groups. The following is a consolidation of what
generally is expected from a market segment to be
of maximum use to a decision maker:
1. Determinants should be sensitive to the "raison d'etre" for market segmentation—they
should reflect household buying behavior.
2. Determinants should anticipate changes in the
segment (that is, predict behavioral movements over time).
3. Determinants should be selective for use as a
target measure (for example, regional).
4. Determinants should have a significant degree
of reliability.
5. Determinants should be available at a reasonable cost.
The above criteria can be used as a measure for
comparing the relative strengths of different bases
for segmenting a market.
Looking at the Durable Goods Market
The probability of a purchase being refiected in a
household's buying expectations can be separated into
two categories: (a) buying plans, which are "crystallized" sentiments of the many variables, both subjective and objective, that require respondents to
make complicated judgments about purchase possibilities, and (b) household attitudes, which are interpreted as individual judgments or feelings about
the many economic, political, and international events
•• Same reference as footnote 3, at p. 27.
that affect the household. The following scheme
shows the general factors involved in determining a
purchasing decision for consumer durable goods.
(Figure 1). The purchase decision of the household
is separated into willingness to buy refiected in the
buying intention, and ability to buy refiected in the
anticipated economic well-being of the household.
Each of these two factors is in turn determined by
many other factors which are separately weighted by
the household unit itself.
Thus the particular problem facing the executive
marketing product j is that of knowing the probability of household k, purchasing durable good j ,
in time period t -I- 1. Stated in more general terms,
the executive is interested in predicting household
k's willingness to purchase good j and its ability to
make such a purchase. From this, he is able to
direct the firm's promotional effort for maximum
efficiency.
Expectational Data: A Segmenting Process
The use of consumer anticipational or expectational data in forecasting models is not new. It
originated in 1946 at the Survey Research Center
of the University of Michigan in connection with the
annual Surveys of Consumer Finances. These were
expanded in 1951, when semi-annual surveys of Consumer Attitudes and Intentions to Buy were inaugurated. They are now conducted on a quarterly basis.
The value in adapting this important survey technique to segmenting markets has not been recog-
• ABOUT THE AUTHOR. J. Alex Murray
is Associate Professor oi Business Ad
ministration at the University of Windsor. Windsor. Canada. He earned his
PhD at the University of Illinois, and
has been a contributor to the journal
oi Marketing Research. This paper is
part of a continuing study with Canadian consumer expectational data
supplied through the courtesy of the
Maclean-Hunter Research Bureau.
28
nized by most market researchers. Yet, expectational
variables can be shown to provide an adequate basis
for market segmentation for durable goods as well
as to meet the criteria set above. This will allow
management to plan optimal use of its limited promotional and marketing resources. The number of
national and regional expectational surveys in the
United States and Canada has increased over the
last decade. The cost of buying into this type of
service has become quite reasonable.
The principal difference and or advantage of expectational data over traditional economic variables,
such as dispo.sable income, is the added contribution
from studying factors underlying the decision process in household economic behavior.' Anticipatory
data are able to register in advance changes within
the household unit not always reflected in income
statistics until months later. Changes in the job
situation or anticipated price changes will influence
the purchase probability for consumer goods. (The
fiuctuating nature of a specific durable good is not
subject to the same demand variables as non-durables
or services.)
Data for this Study
The Canadian expectational data used in this
study originated in September, 1960, with continuing quarterly surveys on household buying intentions and attitudes. The Canadian approach has
been somewhat unique in certain respects: (1) it
was the first regular quarterly survey (others were
on a semi-annual basis) of an entire country, and
(2) because of the six-month planning horizon used
in the questionnaire, an overlap of three months is
available for adjustments to the forecasts.
The basic questionnaire on the Buying Intentions
Survey has remained unchanged since its inception
in 1960, with two main divisions in the questionnaire—the Basic Data Section and the Household
Expectational Section. The former deals mainly
with household composition, socioeconomic status,
and community size. There are also questions on
income category and education. The latter part of
the survey is concerned with attitudinal questions on
economic conditions and buying intentions for selected consumer durables.
The purchasing plans for the following consumer
durable goods are included in the Canadian questionnaire:
automobiles
dishwashers
new houses
clothes dryers
refrigerators
deep freezers
washing machines
vacuum cleaners
television sets
gas and electric ranges
air conditioners
Answers to the survey questions are used in pre5 Robert Ferber, "Research on Household Behavior,"
American Economic Review, Vol. LII (March, 1962),
pp. 19-63, at p. 38.
Journal of Marketing, April, 1969
dicting consumer mood and buying intentions for
the item specified."
A recent successful innovation in expectational
forecasting was executed by Juster, wherein subjective probabilities attached to survey questions
enabled the forecaster to weigh the relative importance of the purchasing plan of each household.'
This refinement would enhance the reliability of the
present segmenting model.
In order to develop an Optimal Strategy Index
for a specified durable good, a three-stage segmenting process of the expectational survey results is
required. In the next section, an example is developed to demonstrate how a manufacturer of refrigerators would obtain an OSI for his product and
the advantages the OSI would hold for his company.
(Refrigerators were selected for this example because of the interest shown by a group of manufacturers; however, any of the above durable goods
surveyed could have been used.)
A Procedure for Segmentation
The first step in this segmenting process is to divide the population or country into logical or manageable sectors for the company's marketing department. These sectors could be counties, provinces,
or even larger regions (for example, two or more
states). They should be large enough to yield adequate or worthwhile sales potentials. In the Canadian study provinces were found to be natural regions for comparison. Satisfactory results were
also found by combining the prairie provinces as a
unit and the maritime provinces as another unit,
leaving five regions (that is, British Columbia,
Prairies, Ontario, Quebec, and Maritimes) with a
more equally distributed population weight. A suggestion for users of this technique in the United
States would be to partition the country into natural marketing regions (for example. Northeast
Region, Midwest) in order to gain maximum benefit
from the indexes developed. Clayclamp and Massy
suggest a reverse process of aggregation (building
up similar microsegments) rather than disaggregation. This alternative can easily be incorporated in
the first stage without altering the basic dynamic
nature of the model."
The next two stages have the central objective of
locating which of the regions designated above will
'• For a summary of the tests and forecasting ability of
Canadian expectational data see: J. Alex Murray,
"Canadian Consumer Expectational Data: An Evaluation," Journal of Marketing Research, Vol. VI (February, 1969), pp. 54-61.
7 F. Thomas Juster, "Consumer Buying Intentions and
Purchase Probability: An Experiment in Survey Design," Journal of the American Statistical Association, Vol. 61 (September, 1966), pp. 658-696.
^ Henry J. Claycamp and William F. Massy, "A
Theory of Market Segmentation," Journal of Marketing Research, Vol. V (November, 1968), pp. 388-394.
29
Utilizing Consumer Expectational Data to Allocate Promotional Efforts
Newfoundland
Prince Edward Island
Nova Scotia
New Brunswick
w
c
o
Quebec
•.-(
bO
Ontario
a;
Manitoba
Saskatchewan
Alberta
British Columbia
8
3
10
Consumer anticipations for new refrigerators
(per cent)
FIGURE
2. Household buying intentions for new refrigerators by region for time period t 4- 1.
have the highest refrigerator sales per capita in the
next period and at the same time ranking ordinally
these other regions against the same criterion. The
introduction of time allows the OSI to refiect changes
taking place in a national market like the United
States or Canada on a quarterly or semi-annual basis,
depending on how fine and with what time horizon
the manager wants to segment his markets.
The survey results of consumer purchase anticipations for refrigerators for the provincial segments
selected were plotted for the first period,^ (Figure
2) The results show that three provinces (British
Columbia, Ontario, and Quebec) lead in consumer
• A recent study showed that there are significant (17<
level) differences in expectational data when parti-
tioned by province, socioeconomic groups, and buying
intentions. See Lee Maguire, "Canadian Expectational
Data: A Study of Provincial and Socioeconomic Differences," unpublished master's thesis (University of
Windsor, 1967).
purchasing plans for this durable good. As additional periods are graphed, percentage changes can
be recorded (semi-logarithmic paper would be suitable), and for each survey period a priority listing
of the regions is developed, using the amount of
positive or negative change as the benchmark.
The importance of this first analysis is in initially
ranking the areas of high sales potential (disregarding at this stage all factors except purchase intentions for the product in question). The household's
willingness to buy the particular durable good is of
prime importance since it indicates a need or at least
desire to make some future purchase, and it measures
the relative percentage (vis-a-vis each region) of
declared intentions. However, a further refinement
is necessary which will scrutinize results of the previous provincial ranking and examine individual sectors vis-a-vis a household economic index that reflects ability to buy. It is just as important for the
household to determine how it will finance the pur-
Journal of Marketing, April, 1969
30
100
90
r-(
\
80
a)
T3
C
Lndex
as
•o
3
O
•a
u
o
n
kO
c
o
o
10
•z.
(0
u
o
o
c
0)
50
Househo:
10
73
U
Hi
60
economl
o
70
o
u
c
.H
c
(d
(0
Vi
OT
3
o
z
m
3
ID
30
20
10
Regions
FIGURE
3. Household economic index by region for period t to t+1.
chase as it is to establish a desire or need for the
good. (Although disposable income will give an indication of the household's well-being, it is not able
to measure many of the additional variables contributing to the anticipated purchasing power of the
unit, such as credit availability and future job
outlook.)
The procedure for the second step is to measure
the most promising regions (those with the largest
positive change in purchasing plans) against their
household economic indexes developed from attitudinal questions on present and future economic wellbeing. Katona and Mueller have developed for the
Survey Research Center an appropriate method of
index construction for anticipatory data which is
suitable for the present model.'"
In Figure 3, the relative purchasing power
(charted as a household economic index) measures
each region against the others in terms of ability
to execute the planned purchases. A more optimistic financial outlook for British Columbia and
Ontario will be refiected in the final OSI, when combined with the higher purchase anticipations, than
for other areas. (See Figure 2.) Saskatchewan and
Quebec have traded third place and will have final
indexes that are very close. The above two stages
" George Katona and Eva Mueller, Consumer Expectations: 195S-1956 (Ann Arbor, Michigan: Institute of
Social Science, 1956), pp. 91-105.
have taken the selected regions and measured each
factor determining the household's purchasing decision, as depicted in Figure 1. The third and final
step is to combine both factors for each region into
one chart and one index.
Figure 4 is a three-dimensional chart depicting
both purchase variables for British Columbia for
several time periods. An optimistic economic outlook coinciding with a large positive change in buying
plans indicates a higher probability of actual purchase than other combinations and therefore a
greater potential market for the refrigerator manufacturer. In periods one and two for British Columbia, both variables have moved upward, and indications are that it will be a prime market for
refrigerator sales during these periods. However, period three shows a drop in the household economic
index and a similar drop in buying intentions, predicting a falling market for this durable good. Regions must be examined not only separately but
also in relation to the others, and a single index (OSI)
which represents both variables will pinpoint prime
target markets for refrigerator manufacturers.
The basic construction of the index can be found
in an appendix to this article. The index gives equal
weight to both the economic activities of the household and the purchasing plan for the durable good.
However, this might not always be the most desirable weight component, and for each index the researcher may find that one of the variables is more
Utilizing Consumer Expectational Data to Allocate Promotional Efforts
1,
2,
3,
Time t
FIGURE
'^,...
31
... N
(in periods)
4. Expectational data for British Columbia over several time periods.
important than the other for a specific durable good
or a particular region being considered. For example, in June, 1966 the registered buying intentions for new refrigerators in the prairie provinces
(whose residents have been known to be somewhat
conservative) were quite low for that time of year.
But a month later, after a large wheat deal had
been finalized with China, an immediate buying
spurt was noticed. Although this additional income
was anticipated, it was not reflected in their buying
plans for the next period. Therefore, an additional
weight is applied in constructing the index for that
region for the attitudinal question "Do you think that
your family will be better off financially, the same,
or worse off in six months than it is now?"
The author is also experimenting with distributed
lags by giving some weight to past buying intentions, in order to account for household purchases that
are postponed for one or two following quarters.
It hardly needs to be said that the OSI constructed
in this paper is experimental and subject to revision.
Future research may produce improved selections by
enabling marketers to attach varying weights to
individual questions or to give more precise scaling
values to the answers. In the first attempt to construct an index, many of the more complex methods
could not be used, and equal weighting of both variables appeared to be the least arbitrary solution.
The OSI for periods 1, 2, and 3 have been constructed for provincial regions in Canada (Table 1).
The indexes show that certain areas have sustained
high probability of purchases (Ontario i, while others
fiuctuate over time. Managers will usually set standards of performance for their marketing efforts and
allocate funds on a priority basis. Priorities are
based on a descending order of market potentiality
with some cutoff point beyond which marginal returns (anticipated sales from promotional outlays)
can be expected. In this study an index of 100 was
arbitrarily set as minimum performance for inclusion as prime target markets. Although this is
somewhat contrary to marginal analysis, it was a
practical answer to a useful theoretical concept. In
period 1, Alberta was excluded with a reading of
96.5; however, it was included when the index
reached 112. Of cour.se, it could have been included
in the first period if the marketer had set a lower
fioor for the index, refiecting his decision to obtain
Journal of Marketing, April, 1969
32
TABLE 1
OPTIMAL STRATEGY INDEXES FOR ANTICIPATED
REFRIGERATOR SALES IN CANADA
Period
Region
Newfoundland
Prince Edward Island
Nova Scotia
New Brunswick
Quebec
Ontario
Manitoba
Saskatchewan
Alberta
British Columbia
1
2
92.8
93.2
94.6
87.5
111.4*
114.8
87.5
108.7
96.5
116.4
91.2
95.8
97.0
86.8
101.3
116.1
95.4
97.6
112.0
119.2
3
98.3
106.7
103.9
92.0
91.6
114.7
111.6
109.3
92.1
87.6
•The indices in boldface indicate prime target markets for promotional activity in the specified period.
a smaller marginal return on his marketing effort
for these sectors. On the other hand, the manager
might wish to withhold marketing funds until a
more favorable sales climate is registered, such as
period 3 when five regions had indexes which exceeded the minimum. It should be remembered that
a low index reading does not necessarily predict limited sales in the particular region for the next period. Rather, in comparison to alternate areas this
segment does not hold the high probability of a large
retum on promotional investment.
Conclusions
The consumer durable goods market is highly
volatile, and this market can best be segmented by
using relative measures. The OSI is a weighted,
relative measure for each regional grouping which
is determined from (1) the household's intention to
make a purchase of a specified durable good, and (2)
the household's own analysis of its anticipated economic situation in relation to its ability to finance
the durable good purchase. This time-related index
pinpoints high potential markets through a priority
listing of regions where the probability of a household purchase of specified durables is known to be
relatively high.
The example and data for this segmenting model
is of Canadian origin. However, as suggested above,
the same technique and principles will apply equally
well in the United States or other countries where
durable goods fluctuations are highly erratic and
where regional segmentation is just as important.
In fact, the international firm marketing in both the
United States and Canada will find that with comparable expectational data available, it will be able
to optimize the total marketing effort for the North
American market by integrating its planning
decisions.
The consumer expectational model meets the criteria set for determinants used in the segmenting
process as follows:
1. Expectations do reflect consumer buying mood.
Katona stated that "buying intentions are one of
several ways in which (purchasing) attitudes
may express themselves.""
2. The six-month planning horizon used in expectational questions makes the model a short-term
forecasting device able to indicate magnitudes
and, more important, turning points. However,
the optimum forecast period need not necessarily
have the same length as the optimum time horizon of an expectational question used as input
in the forecast. The exact relationship will require further tests.
3. In Canada, one natural market segment is the
province, and with the unique Canadian bicultural setting, the provincial region is an efficient
division to meet the requirements of a national
marketing program. This would differ in the
United States, and a number of states within a
section of the country may be combined to constitute one region.
4. Anticipational data have been shown to be a significant contributory variable in predicting consumer durable goods purchases in both Canada
and the United States.'- However, buying intentions alone are not the sole criteria for household purchases, and ability to buy has considerable weight in the final decision. The OSI is
sensitive to both purchasing measures in addition to being a dynamic relative indicator among
regions, locating optimal times for applying promotional effort.
5. Current research has been directed to increasing
the predictive power of expectational data
through newer techniques of subjective probability, but efforts have also been made to lower
survey costs of national samples by sub-sampling
large areas without reducing the reliability of
the results.'"'
In a leading paper on new criteria for market
segmentation the statement was made that "We
should discard the old unquestioned assumption that
demography is always the best way of looking at markets . . . and markets should be scrutinized for important differences in buyer attitudes, motivations,
and values."'^ As an attempt to formulate a workable model for a large segment of the consumer
market, expectational data do have theoretical and
empirical support for serious consideration by marketing management. At its best, expectational data
will locate, measure, and segment national markets
in a priority ranking. At worst, the data will direct
" George Katona, The Powerful Consumer (New
York: McGraw-Hill Book Co., 1960), p. 63.
'-• For a summary of tests and forecasting ability of
Survey Research Center data see: Eva Mueller, "Ten
Years of Consumer Attitude Surveys: Their Forecasting Record," Journal of the American Statistical
Association, Vol. 58 (December, 1963), pp. 899-917.
'•'' For an interesting discussion of cost saving techniques and their effectiveness see, Charles S. Mayer,
Interviewing Costs in Survey Research, (Ann Arbor,
Michigan: The University of Michigan Press, 1964.)
'< D. Yankelovich, "New Criteria for Market Segmentation," Harvard Business Revietv, Vol. 42 (September/
October, 1964), pp. 83-90, at p. 89.
33
Utilizing Consumer Expectational Data to Allocate Promotional Efforts
marketers toward the behavioral patterns of the final
decision maker.
Basic Construction of the OSI,.i
OSI,., = (P,, - P,, + 100) + (Bl - NI -I- 100)
Where:
OSI,-, is the Optimal Strategy Index for the
next period
P,, is the proportion of optimistic or up responses for the household economic activity
for the next period
Prt is the proportion of pessimistic or down
responses for the household economic activity for the next period
Bl is the positive buying intenticns for a selected durable good purchase
NI
is the negative purchasing intentions (nonintenders) for a selected durable good
One hundred was added to avoid negative values.
Also, a desirable feature is to empha.size change, and
this can easily be accomplished by dividing the resulting index by an index (OSI^) from a previous stable
period and multiplying by 100. For example, if in
British Columbia, P,. = 43.0, P., = 30.0, Bl = 5.0,
NI = 90.0, OSI« = 110.0, then
OSI,.. =
(43.0-30.0-f 100.0) -I-(5.0-90.0-1- 100.0)1 X 100.0
110.0
= 128.0 X 100.0
110.0
= 116.4
STATEMENT OF OWNERSHIP, MANAGEMENT AND CIRCULATION (Act of
October 23, 1962; Section 4369, Title 39, United States Code).
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