DAVID A. AAKER*
A measure of brand acceptance involves a refinement of the usual stochastic model
market prediction. Using this measure, the effect of consumer promotions and purchasing habits on brand acceptance is investigated and a brand health indicator is
developed.
A Measure of Brand Acceptance
and a prediction is obtained for each of the resulting
groups. For a new-trier group, the intent m~y. be to h~lp
optimize promotional efforts by determmmg which
it. Ot?er
buyer types accept the brand and which reje~t
factors (such as susceptibility to promot10n) bemg
equal, it will be more profitable to direct promotions
toward those who tend to accept the brand.
A similar approach is possible in promotional tests.
The data base is again partitioned-this time by descriptors of the promotion of interest. Consequently,
the effect of controllable variables upon market dynamics can be observed [6, 9].
A serious problem with partitioning the data base is
that valuable sample size is sacrificed. Since the data
source is usually consumer panels, even one partition
can sometimes reduce the data base to an uncomfortable
level. Further, this partitioning procedure fails to exploit the available information.
.
The purpose of this article is twofold. Ftrst, a new
measure of brand acceptance is introduced and empirically illustrated which refines the con~e~tional
model market prediction by more fully explo1tmg the
available information. Using this measure, segmentation studies and promotional tests are possible even
when the model is applied to the unpartitioned data
base. Practical, sensitive measures of brand health can
also be generated using this measure; these control for
promotional effort and sample composition.
The second purpose of this article is substantive.
The effect on ultimate brand acceptance of a consumer
deal (such as a cents-off coupon) associated with a
first or trial purchase is studied. Dissonance theory,
for example, predicts that such a reward would reduce
the tendency for the new trier to justify his purchase by
accepting the brand. Such a proposition has obvim~s
implications for promotional decision making. In addition, the effect of purchasing habits such as brand
loyalty and usage level upon consumer brand accept-
INTRODUCTION
The primary objective of most applications of st_ochastic models of buyer behavior is to generate predictive measures of market share or sales. Since such
models are usually based upon a knowledge of individ_to detect
ual purchase decisions, they have the potenti~l
actual market dynamics accurately and sens1t1vely. As
a result, their predictive measures are often much more
meaningful than extrapolations from aggregate market
data. When purchase histories of those trying a new or
existing brand for the first time-new triers of a brandprovide the data base, these predictive measures become
measures of brand acceptance.
These can be used in several ways, one of which is in
evaluating a brand's performance. A manager of~ new
brand is faced with major decisions and needs a timely
and accurate assessment of the likely future of the brand.
A manager of an existing brand needs to monitor the
health of his brand to detect any indications of weakness. The problem is to isolate brand acceptance or rejection decisions. Does a significant model-proje~ted
trend reflect a set of enduring judgments by new tners
about a brand, or does it reflect a change in sample
composition or a temporary reaction to unusual promotion?
A second use of model prediction is in segmentation
studies, in which the data base is partitioned according
to meaningful buyer characteristics, such as age or sex,
* David A. Aaker is Assistant Professor of Business Administration, University of California, Berkeley. He gratefully
acknowledges the many contributions made by Professors W. F.
Massy G. S. Day, and M. L. Ray of Stanford University and
Profes~or
Barr Rosenberg of the University of California at
Berkeley. Clerical and reproduction services were provided by
the Institute of Business and Economic Research, University of
California, Berkeley. Dr. I. J. Abrams of the Market Research
Corporation of America made the data available.
160
Journal of Marketing Research,
Vol. IX (May 1972), 160-7
A MEASURE OF BRAND ACCEPTANCE
161
ance are also examined. What percentage of brandloyal buyers enticed to try another brand can be persuaded to switch loyalties? Are heavy users more
difficult to convince than light users once they have
tried a brand?
Figure 1
PROBABILITY DISTRIBUTION OF p(l)
f(p (1))
THE BRAND-ACCEPTANCE MEASURE
Let n represent a discrete time or purchase occasion
and p(n) the probability of purchasing a given brand
at time (or purchase occasion) n. The expectation of
p(n) over the population, E[p(n)], is the model's mean
value function or its· predicted market share trend
measured in discrete (not real) time. If the sample's
interpurchase times and time origins are fairly homogeneous, the differen~e
between discrete and real time
is small. The model's mean value function is usually
determined from estimated model parameters. Its
asymptotic market share prediction, E[p( CJJ)], is approached as n increases. E[p( CIJ)] is the model's market
share prediction upon which most studies rely.
Consider a set of binary coded, brand choice decisions of length 5 (another length could be chosen without altering the argument), where 1 denotes the purchase of the brand of interest and 0 another brand.
Let x denote a specific sequence of brand decisionse.g., 1 0 1 1 1 or 1 1 1 0 0. Intuitively, knowledge of x
would seem useful in predicting p( CJJ) for an individual
in the sample. If x were 1 1 1 1 1 for an individual, his
p( CJJ) likely exceeds E(p( CJJ)], the expected value for
the group. Similarly, those with a sequence of 1 0 0 0 0
would probably develop a p( CIJ) value below E(p( CJJ )].
In fact, using probability theory, the distribution of
p( CJJ) conditional on x can often be obtained. The mean
of this distribution is then E[p( CJJ) I x]; it provides a
measure of brand acceptance that is substantially more
useful than E[p( CIJ)]. This new measure does not ignore
information nearly always available to the model user
at the parameter estimation phase-namely the knowledge of the brand decision vectors for each sample
member.
For each application of the model, there are 25 or 32
potential data cells defined by the binary, five-purchase
sequence and 32 potential dependent variables,
E[p( CJJ) I x], for segmentation studies or promotional
tests. To proceed it is necessary only to characterize
the sample members of these cells in terms of segmentation variables or their exposure to promotional efforts.
An empirical application will clarify. First, however,
the E[p( CIJ) I x] term will be illustrated by considering
it in the context of two specific brand choice models.
The Heterogeneous Bernoulli Model
Morrison has suggested and empirically explored a
heterogeneous Bernoulli model [11]. In this simple
model, each individual in the sample is assumed to
follow a Bernoulli process-that is, his purchase probability is an unchanging parameter (p(n) = p for all n).
c/i
= p[p(l) = O]
0
1
p(l)
Recognizing that all individuals in the population do
not have the same purchase probability, this parameter
is distributed over the population with a beta distribution (with parameters R and N). In this case a familiar
argument [12, p. 53] demonstrates that E[p( CJJ) Ix] =
(R + t)/(N + 5), where t is the number of l's in x;
t = 0, 1, · · ·, 5. This expression directly indicates that
those who generate an x with many ones are more likely
to have a higher p( CIJ )-to eventually "accept" the
brand-than those who generated an x with few ones.
The New-Trier Model
The new-trier model was designed to model the purchase process following the first purchase of a brand
new to a buyer. The brand may be newly introduced or
it may be an existing brand with which the new trier is
unfamiliar. In the latter case, he has never before used
the brand or use was so long ago that it has been effectively forgotten.
Let the time origin (n = 0) be the first purchase of
such a "new" brand. The model assumes that the new
trier comes to a decision about the brand while using
it after the first purchase.1 This decision is represented
by p(l). Recognizing that new triers' brand decisions
are heterogeneous, the model distributes p(l) over the
population with a truncated beta distribution which
might appear as in Figure 1. The probability mass at
zero reflects the probability that the new trier has completely rejected the brand. The decisions of those who
did not immediately reject the brand (the probability
of not immediately rejecting is 1 - <P) are distributed
over the population with a beta distribution with parameters R and N.
The model further assumes that the new brand is
1
A special case of the new-trier model is actually used here.
The complete model, described in detail in [4], includes a formal
trial period construct. An application of this special case is reported in [2].
162
JOURNAL OF MARKETING RESEARCH, MAY 1972
For the new-trier model, it has been shown [l, pp.
Figure 2
64--6J that:
CUMULATIVE PROBABILITY OF REJECTION
P[p(n) =
OJ
(2)
E[p( oo) I x]
1
y
[l where y
aJ
N
R
+
5
= 1,2, 3,4,5
y = 0
the position of the last 1 in x; y = 0, 1,
I xJ is a function of the
position of the last 1, distinguishing 0 1 0 0 0 from
0 0 0 0 1 and reflecting the fact that a purchase indicates
that rejection has not yet occurred.
The proposed brand acceptance measure, E[p( oo) I xJ,
is really a transformation of the brand choice vector x,
based upon a specific model of brand choice. The transformation's appropriateness depends upon the extent
to which the model's assumptions are fulfilled. In the
case of the Bernoulli model, the resulting transformation
was simply the number of ones in x, a measure commonly used in empirical studies. With a more complex
model, such as the new-trier model, the measure becomes more sensitive and refined.
Not every brand-choice model generates 32 groups
with different E[p( oo) I xJ values. In fact, the heterogeneous Bernoulli model generates six different measures, since it does not distinguish, for example, tetween
0 0 1 1 0, 1 0 0 0 1, and 1 1 0 0 0. The new-trier model
defines 16 different E[p( oo) I xJ values. It does not distinguish between 1 1 0 1 0, 1 0 1 1 0, and 0 1 1 1 0, for
example.
Although this discussion has used brand-choice
models, the method can also be used on purchase incidence models, which focus upon sales instead of
market share measures.
=
· ·., 5. Notice that E[p( oo)
1
0
2
3
n
4
vulnerable soon after the first purchase, since it has
not yet had an opportunity to become ingrained as
part of a family's habitual purchasing process. After a
time of course it does become less susceptible to
' from other
' brands. Accordingly, the model perattack
mits new triers initially to accept the brand (p( 1) > 0)
and later to reject it (]J(n) = 0). The mechanism used
is the cumulative probability of rejection (P[p(n) = OJ)
which is permitted to increase geometrically through
time (at a rate of 11) from its initial value, cf>, to a final
value, a. That is:
P[p(n)
= OJ = cf>+ (a - cf>)(l -
11n-1), n
= 1, 2,
The cumulative rejection probability distribution might
appear as in Figure 2.
At time n, p(n) is distributed over the population
for which p(n) > 0 with the same beta distribution
(with parameters R and N). Letting f[p(n) J denote the
distribution of p[nJ:
[ P[p(n) > OJJ[p(n) ip(n) >OJ 0
f[p(n)J
=
~ 1
n = 1, 2, · · ·
= OJ
P[p(n)
< p(n)
p(n)
= 0
n
1, 2, · · ·
=
or:
[1 - a+ (a+ cf>hn-lJ
r(N)[p(n)JR-1[1 - p(n)r-R-l
r
r(R)r(N - R)
J
(1) /[p(n)] =
l
l
cf> +
~
:: f(~)
~~
.~.
1
(a_ )(1- n-1) p(n)
11
cf>
n = 1, 2, · · · .
The second part of (1) is simply the cumulative rejection probability. The first part contains the beta distribution multiplied by P[p(n) > OJ, the probability
that rejection has not occurred at time n.
AN EMPIRICAL APPLICATION
In this section, a regression model is introduced
which has as its dependent variable the E[p( oo) I xJ
term as defined by (2). The independent variables included two buyer characteristics and two descriptors of
consumer promotions associated with the first or trial
purchase of the brand. A structural analysis of the regression results will, hopefully, help to answer two questions: (1) Which buyer types (segments) accept the
brand and which reject it? (2) What effect does consumer promotion, such as cents-off deals, have on newtrier brand acceptance? Then the possibility of using
the results to generate a measure of brand health will
be explored.
Data Description
The data source was the MRCA National Consumer
Panel. The product class was a frequently purchased
consumer good distributed primarily through grocery
A MEASURE OF BRAND ACCEPTANCE
163
outlets. Approximately 135,000 purchases were made
by over 8,000 families in the 3-year period studied.
Four brands (which represented just over half of the
market) were analyzed, including one introduced during
the period (Brand 3).
The danger does exist that consumer panel members
become sensitized to transaction details. However, the
act of recording purchases can reasonably be considered
to become habitual and disassociated from purchase
decisions, thus making the data very nearly nonreactive.
New triers' purchases of the four brands were identified; the numbers so classified were 680, 631, 988, and
483 for Brands 1 through 4, respectively. To qualify
as a new trier of an existing brand, at least 15 months
and 5 purchases of other brands had to have elapsed
since the last purchase of the brand in question (see [4)
for an empirically based discussion of this decision
rule).
The model's parameters were estimated for the groups
of new triers-one group for each of the four brands.
The x 2 goodness-of-fit p-level (a low p-level indicates a
poor fit or a rejection in the sense of classical hypothesis
testing) were 0.09, 0.88, 0.52, and 0.40. This set of numbers is quite compatible with a viable model; only one
brand involved a rejection at the 0.10 level, and that
just barely. In addition, the model's mean value function predicted well in an absolute sense and in comparison with two other models [4]; it was concluded
that the model was a viable representation of the
process.
section, these variables had practical value beyond that
of being an obvious mechanism to increase sample
size. Other descriptive variables-notably socioeconomic variables-were judged to be less relevant and
not worth including in an already complex and expensive data reduction process.
The Independent Variables
To interpret the results: ai = f3o + f31; a2 = {3 0 + {32 ;
as = f3o + f3a ; and a4 = f3o. The model, (4), was run
on each of the four brands and their aggregation. The
results, presented in the form of (3), are in Table 1.
The dummy variable coefficients for the brands are
discussed in the next section; now the analysis turns to
the coefficients of the remaining variables and the
model's viability.
Eight independent variables were calculated for each
new trier. One was a dummy variable, d, with a value
of one if the first purchase of the new brand was made
on a consumer deal, and zero if not. A second variable,
s, reflected the size of the deal; it was the percentage
differential between the average nondeal prices for the
brand and the deal price actually paid or zero if no
deal was made. The value was constrained to be positive. Because of local conditions and the specific nature
of the deal, the deal price occasionally exceeded the
average price. A wide variation in prices made it infeasible to use previous family purchases to determine
the average price.
Two variables were determined from the six purchases
preceding the first purchase of the new brand. One, v,
was an index of purchasing volume-the average daily
usage of the product, in thousandths of ounces per day.
The other, l, was an index of brand loyalty-simply
the number of purchases of the family's favorite brand
divided by six. This favorite brand, of course, had to be
different from the new trier's new brand. The loyalty
index was thus defined with respect to the product
class, not to any particular brand. The remaining four
variables were dummy· variables denoting which of the
four brands was involved. As discussed in the following
The Regression Model
The complete model was:
P = aib1
(3)
+ a2b2 + aaba + a4b4
where:
P =
b; =
v =
l =
d =
s
=
expected value of the new-trier model's asymptotic
brand purchase probability, given the brand purchased and the subsequent five-purchase sequence-E[p( oo) Ix]
dummy variable for brand i (i = 1, 2, 3, 4)
index of purchasing volume
index of brand loyalty
dummy variable--coded as one if first purchase
was on a consumer deal, and zero otherwise
deal size.
Equation (3) is overidentified. If the values of b 1 ,
hz, and b3 are known, then b4 is uniquely determined.
Equation (4) overcomes this problem:
(4)
P
=
f3o
+ f31b1 + f32b2 + {33b3
+
a5v
+ a5/ + a1d + a s.
8
A Structural Analysis
A new trier's value to a brand must increase with his
usage. However, evidence from Table 1 suggests that a
higher-volume user is more difficult to win over, once
he has tried a brand, than the average user. The nature
of the data-reduction process in this study eliminated
the families (about one-third) with the lowest usage;
consequently, the comparison was between average and
high usages. The v regression coefficient was negative
for all brands combined (significant at the .20 level)
and for Brands 1, 2, and 4. It was only barely positive
for Brand 3.
As an explanation for these observations, perhaps
the heavy user's buying habits are more firmly entrenched because of more frequent usage. In Hullian
terms, the habit strength is greater and there is less
opportunity to forget. Thus it is more difficult to induce
164
JOURNAL OF MARKETING RESEARCH, MAY 1972
Table 1
EQUATION (3): REGRESSION COEFFICIENTS AND t-RATIOS
Brand I
IJisaggregate data
a,
az
as
a.
Volume (v)
Loyalty (/)
Deal (d)
Deal size (s)
Sample size
RZ
F
Aggregate data
Sample size
Rz
Brand 2
.116
-.010
(- .74)
.131°
(3. 62)
-0.56°
(-2.60)
.019
(.21)
680
.035
6.05°
18
.78
Brand 3
.106
- .018•
(-1.65)
.095°
(3.01)
-.008
(-.47)
-.030
(- .42)
631
.019
3.06h
18
.83
.121
.003
( .35)
.064b
(2.33)
.023
(1. 31)
- .068°
(-3. 30)
988
.016
4.00°
18
.60
Brand 4
.140
-.013
(-1.22)
.051
(l.30)
.006
(.24)
-.013
(- .18)
483
.006
.70
18
.54
All brands
.132
.101
.126
.116
-.007
(-1.39)
.089°
(5.40)
- .008
(- .84)
- .044b
(-2.40)
2,782
.018
6.70°
72
.64
• Significant at the .10 level.
t Significant at the .05 level.
0 Significant
at the .01 level.
the heavy user to switch loyalties. He might also be
more critical, since the perceived risk is probably higher.
The new brand (Brand 3), with the positive coefficient,
was surrounded by introductory promotion which
might have given it an advantage in appealing to users
of other brands. Consequently, the existing habit
strengths might present less of a barrier. However, for
existing brands, it seems that the value of a high-volume
user must be adjusted somewhat for the possibility
that he will be more difficult to sell even if he is persuaded to try a brand.
One might expect that a loyal buyer is worth much
more to a brand than a nonloyal buyer. Such a hypothesis is implicit in the interest that marketers have
shown in brand loyalty over the years, and is confirmed
by the data in Table 1. The loyalty coefficient was significantly (.01 level) positive for Brands 1, 2, and 3.
For the four brands combined, the t-ratio was 5.40,
indicating a very impressive level of significance.
Another approach was used to investigate further
the effect of loyalty on brand acceptance. A new-trier
sample for eight brands, including Brands 1-4, was
used. The sample had 4,765 new-trier purchases and
was divided on the basis of brand loyalty, with 0.60 as
the boundary. If a household purchased the same brand
on 4, 5, or 6 occasions in the 6 purchases preceding the
first purchase, it was regarded as loyal. The new-trier
model was fitted to the two groups. 2 The model predicted an asymptotic market share, E[p( oo)], of 0.145
2
The goodness-of-fit p-levels were 0.19 (loyal) and 0.004
(nonloyal). These levels are not overly disturbing considering
the large and heterogeneous samples involved, but should still
serve as a warning.
for the loyal group and 0.086 for the nonloyal groupa very substantial difference of 0.059.
The significance of brand loyalty in this context is
most interesting when viewed against prior empirical
work. Frank reviewed brand-loyalty research and concluded that "the pattern of results for brand loyalty as
a basis for market segmentation in food products is
not encouraging" and that "loyal customers do not
appear to have economically important differences in
their sensitivity to either the short-run effects of pricing,
dealing, and retail advertising, or to the introduction
of new brands" [8, p. 33]. Yet in this study responses of
loyal buyers were found to be significantly different
from those of nonloyal buyers to new brands being
tried.
It should be emphasized that this study centered on
brand acceptance after trial. The loyal buyer is undoubtedly more difficult to induce to trial. Webster
[13] found that deal proneness was negatively correlated
with brand loyalty. The finding here suggests that brand
loyalty is vulnerable if trial can be induced (for a normative model that includes the task of stimulating trial,
see [3]).
One might logically expect deals to tarnish a brand's
image, since a brand that has to make a special offer
to obtain purchase might be perceived as inherently
inferior. Thus the coefficient of d should be negative, a
prediction supported by dissonance theory. A decision
to purchase a new brand creates dissonance or pressure
to develop a positive attitude toward that brand. If a
reward is associated with that purchase decision, the
likelihood of a positive attitude developing is smaller,
because reward reduces the tendency for the new trier
A MEASURE OF BRAND ACCEPTANCE
to justify his purchase by accepting the brand. Doob,
et al. [7] offer empirical support for such a hypothesis.
Adaption-level theory supports the dissonance hypothesis in that a user might associate the brand with
the reduced price and would be reluctant to accept it
at its normal price. In addition, one might expect the
deal to attract extremely price-conscious buyers whose
purchase does not represent a real trial of the brand.
This type of self-selection bias should accentuate the
expected result that those whose first purchase of a
brand is on a deal develop less favorable brand attitudes than those whose first purchase is not on a deal.
Continuing the same line of reasoning, one might
believe that as deal size is increased, the image-tarnishing effects and self-selection bias should also increase.
Similarly, a greater deal size should still further reduce
the dissonance associated with the deal purchase.
The hypothesis seems to have been only weakly confirmed. The d variable was significantly negative for
Brand 1 and negative-although not significantly-for
all brands combined. The s variable was significantly
negative for Brand 3 and for all brands combined.
However, the effect failed to emerge to any significant
degree for either the d or s variable for Brands 2 and 4.
The correlation between d and s was 0.57. The resulting collinearity tends to inhibit the image-tarnishing
hypothesis from emerging via a t-test. It also tends to
make the coefficient values for d and s unstable. In
particular, the large negative coefficient for the s variable in the Brand 3 results undoubtedly reflects, in
part, the positive d coefficient. To explore the effect of
the d variable further, the regressions were rerun with
the s variable omitted. Again, only in the Brand 1 result did the d variable have a significant t-value (-3.00).
In the other brand results, the t-values were low (-0.78,
-0.58, and 0.16). For all brands combined, the t-value
was significant ( -2.33). As before, the total new-trier
sample was divided by whether a deal was associated
with the first purchase of the brand, and the new-trier
model was fitted to the two groups. 3 The model predicted the same asymptotic market share, an E[p( oo)]
of 0.12 for both groups.
One must conclude that the hypothesis of imagetarnishing received less support than expected. This effect was not nearly as pronounced as the loyalty effect.
The result is reminiscent of the sleeper effect in communication research. After a time, the content of a
communication becomes disassociated with its source
and a negative effect of an unreliable source dies away.
It appears that a similar process may, to some extent,
have been operating here. The negative effect of a deal
purchase may decay over time until the brand becomes
virtually disassociated with the deal purchases; unless a
brand decision is made immediately, the effect of the
deal may be much less than one would suppose.
•The goodness-of-fit p-levels were again low: 0.07 (deal) and
0.004 (no deal).
165
Although the F-statistics for all regressions except
Brand 4 were highly significant, the R2 values were low,
ranging from 0.006 to 0.035. The R 2 values in this model
measured the percentage of variance in long-run brand
acceptance across the population which can be explained by the independent variables. The very low R 2
values indicate that random factors affecting individuals'
purchasing habits, together with the intrinsic measurement error in using E[p( oo) J x] to predict asymptotic
buying behavior, made the larger contribution to
variance in the dependent variable. The low R 2 values
should not, however, be taken as vitiating the importance of the results. As Bass, et al. noted, "the fact that
the R 2 values are low implies only that the variance
within segments is great, not necessarily that the differences in mean values between segments are not significant" [5, p. 267]. From a practical standpoint, an
effect which accounts for one percent of the variance in
individual buying decisions can have a much larger
effect on market share if the effect is stable over time,
whereas the factors contributing the remaining variance
are intrinsically random and average to zero by the law
of large numbers as individuals are aggregated into segments. From the standpoint of statistical sampling
theory, the !-statistics for the parameter estimates,
rather than the R 2 values, are the appropriate criteria
for the significance of the estimated effects of the independent variables.
To show the effect of random factors upon the R 2
values, the regressions were rerun with the s variable
removed and the data aggregated into 18 cells per
brand on the basis of the v-, l-, and d-variables. The vand /-variables were recoded as low, medium, or high
for purposes of the aggregation. The resulting R 2 values,
shown in Table 1, ranged from 0.54 to 0.83. The coefficient estimates obtained from the aggregate data base
were similar to those reported. The !-values associated
with the v- and /-variables were higher, however. For
all brands combined, the t-value for v was -1.68 and
for l, 9.66. Also, the all-brands-combined t-value for d
was -0.95, which should be compared to the -2.33
t-value obtained in the disaggregative data with s
omitted.
MEASURING BRAND HEALTH
A new product receives the benefit of close managerial
scrutiny and concentrated market research efforts, but a
continuing product has no such advantage. Managers
tend to rely heavily on aggregate trend data of sales or
market share, yet measures such as these tend to be
both insensitive and deceptive. They are insensitive because the bulk of the data represents habitual purchases. Purchases that can be closely associated with
decision points related to a brand are simply swamped.
What one sees is not so much a reflection of consumers'
brand reaction as the inertia of their purchasing habits.
Further, by not exposing underlying purchasing
166
JOURNAL OF MARKETING RESEARCH, MAY 1972
Table 2
MEASURES OF BRAND HEALTH
Brand
New-trier model,
asymptotic
market-share
prediction
E[p(c<J )]
0.171
2
0.127
3
0.141
4
0.143
Equation (3),
regression
coefficients
and standard
errorsa
0.132
(0.012)
0.101
(0.013)
0.126
(0.012)
0.116
Equation (5),
prediction
Yi
Gi
0.169
0.138
0.163
0.153
• The standard errors are those of (4) 's regression coefficients.
dynamics, aggregate measures can deceptively seem to
support inappropriate decisions. For example, a decline in market share is easily interpreted by a brand
manager to indicate that more dealing is required to stir
up the market. However, if new triers are actually rejecting the brand, such a tactic would be far from beneficial. Dealing would then only increase the population
of rejectors, and these would become more difficult to
attract in the future when the product is improved. They
could even become actively negative forces in the market. A sensitive measure of brand health can be viewed
not only as desirable in itself, but as a safeguard against
the misinterpretation of aggregate measures of a brand.
To obtain a more sensitive measure, it is necessary to
identify consumer brand decisions. A decision point
very relevant to a brand occurs just after it is first tried.
Instead of market share, some continuing measure of
the acceptance of the brand by new triers is needed to
provide a truly sensitive measure of the brand's health.
This argument suggests that the new-trier model
should be regularly applied to new triers as they are
identified. The model's asymptotic market-share prediction would be the desired measure. Using this approach, however, one might be monitoring the effect of
the changing composition of new triers, rather than the
brand's ability to gain acceptance. One set of new triers
may contain more brand-loyal consumers and more
consumers attracted by deals than another set. Fortunately, the regression model provides a mechanism to
adjust buyer acceptance for purchase details and buyer
characteristics. The coefficient ai from (3) is the appropriate adjusted indication of brand health. Its sensitivity and diagnostic value can be enhanced by contrasting it with comparable measures for other brands
and by monitoring it over time. Obviously it can also
be used to determine the health of new brands.
Although the ai coefficient's values provide a relative
indication of brand health, they have little intuitive
meaning in an absolute sense. In fact, they could be
negative. It is possible to adjust these values to obtain
a measure of brand health that is more interpretablethe predicted brand asymptotic market share for an
average group of new triers. One need only apply (3),
using for the independent variables the appropriate
average value of new triers:
(5)
where:
Yi
=
ai
+ a5v + a67 +
aa
+ ass,
asymptotic market share predicted for
brand i for an average group of new
triers
a;, a5, a6, a7, as = regression coefficients from (3)
v, l, d, s,
= (3)'s variable values for an average
group of new triers.
Yi
=
Thus Yi, which is really only a; plus a constant (a5V +
as!
a7d + ass), becomes a revised measure of brand
health. 4 For the data used in this study, this constant
was 0.037. Naturally, it might sometimes be appropriate to use special data bases to get the average group,
perhaps, for example, a group observed in a different
time period.
The asymptotic market-share values predicted by the
new-trier model, E[p( oo)], are reported in Table 2.
Those of Brands 3 and 4 were about 0.142, while that of
Brand 1 was higher (0.171), and that of Brand 2 lower
(0.127). Also shown in Table 2 are the a; coefficient
values from (3) and they; values defined by (5). These
new measures modify the analysis. Brand 3 now appears stronger. It is close to Brand 1 and substantially
above Brand 4. By the E[p( oo )] measure, it had trailed
Brand 3 and seemed much weaker than Brand 1. Brand
1 no longer seems as strong, relative to the other brands.
Brand 2 still seems weakest, but the gap between it
and Brand 1 has narrowed. Further, the y; value for
Brand 2 (0.138) is more attractive than the E[p( oo)]
value of 0.127. The large standard errors indicate that
the regression coefficients are, in reality, quite unstable
about these values, so these observations must be highly
qualified.
Comparing these observations with actual market
trends, Brand 3 was a very successful new entry, becoming one of the top three brands a year after it was
introduced. Its performance is certainly compatible
with the conclusion that it was an attractive brand.
Brand 4 was a smaller brand, whose market share increased in the face of the new entry. Brands 1 and 2
were the leading brands. The last half of the third year,
after Brand 3 made its biggest push, the market share
for Brand 1 was about the same as it was the previous
year. Brand 2, in contrast, had lost nearly 10 % of its
market share. The relative difference in the three brands'
ability to gain acceptance among new triers was undoubtedly a factor in explaining their relative performance. Since a brand must also obtain new triers in
+
•The standard error of they, value is easily computed if the
inverse of the cross-products matrix is available [10, pp. 1312].
167
A MEASURE OF BRAND ACCEPTANCE
the first place (presumably through promotion), its performance, of course, is not solely a function of its
ability to gain acceptance from the new triers it does get.
for each of four brands, and the results seemed reasonable when compared to the brands' overall marketshare performance.
SUMMARY
REFERENCES
Market predictions made by stochastic models of
buyer behavior are extremely useful but have important
limitations. This article has suggested a refinement of
this measure which exploits the available information
and provides the potential of obtaining for one model
application a market prediction for each purchase sequence. When purchase histories of those trying a
brand for the first time provide the data base, measures of brand acceptance are generated for each purchase sequence.
This measure provided the dependent variable in a
regression model designed to predict new-trier brand
acceptance from such explanatory variables as brand
loyalty, product class usage, deal coverage, deal size,
and brand identity. The regression results produced
some interesting conclusions. First, the higher-volume
user seemed to be more difficult to win over, once he
had been induced to try, than the average user. Second,
buyers with a tendency toward brand loyalty are more
likely to accept a new brand once they have tried. Finally, the influence of the deal and its size on brand acceptance was smaller than anticipated.
It was suggested that the machinery developed in
this research could be applied to measuring brand
health. A brand dummy variable in the regression model
directly provided a measure of the brand's ability to
gain acceptance among new triers. It had the important
characteristic of being corrected for the composition
of the new-trier group. Such measures were obtained
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