Strategic Concept Formation of Consumer Goods Based on
Knowledge Acquisition from Questionnaire Data
Yoko Ishino, Koichi Hori, Shinichi Nakasuka
Research Center for Advanced Science and Technology,
University of Tokyo,
4-6-1 Komaba, Meguro-ku, Tokyo, 153-8904 Japan
Phone: +81-3-5452-5289
FAX: +81-3-5452-5312
ishino@ai.rcast.u-tokyo.ac.jp,
E-mail:
hori@ai.rcast.u-tokyo.ac.jp,
nakasuka@space.t.u-tokyo.ac.jp
ABSTRACT
Product’ concept formation, which occurs in the early stage of
product development, is critical to the successfbl development of
a new product or to the suitable improvement of a current
product. We propose a novel method for computer aided
strategic concept formation based on knowledge acquisition
from questionnaire data. Product concept should be developed
based on consumers’ needs that are usually embedded in
consumer survey data, and moreover the foresight of a domain
expert should be added to it using a domain strategy. To meet
these requirements, our proposed method adopts the 3-phased
interactive computing process, where ( I ) evolutionary
algorithms such as simulated breeding (including Genetic
Algorithm) and inductive learning techniques are used to extract
one type of strategic knowledge, (2) a technique like simple
expert system is used for the other type of strategic knowledge
and (3) reinforcement learning technique is employed to
converge thoughts using two types of strategic knowledge. It
enables the user to generate a creative and well-grounded
product concept based on marketing strategy, while also
stimulating user’s creativity. The system called BICSS was
developed sased on this method. The proposed method has been
qualitatively validated by a case study.
1. INTRODUCTION
A process of product development is quite complex.
Because development of the concept occurs at the
beginning of the product development process, it therefore
affects the degree of success of the final product. A
product concept is a brief statement that explains the
product a manufacturer intends to make and describes the
product attributes, benefit and value to the user. An
example of a product concept for beer is: “This beer has a
clear and light taste. People feel refreshed in mind and
body when they drink it. This beer suits people in their 20s
or 30s who enjoy having fun times with friends.
The product concept should be developed based on
consumers’ needs that are usually embedded in consumer
”
’ In this paper, ‘product’ means packaged consumer goods.
0-7803-5731-0/99/$10.0001999 IEEE
I -1043
survey data. Moreover, the foresight of a domain expert
should be added to the product concept using a domain
strategy. In reality, the development of a product concept
is often left to only the subjective discretion or intuition of
experts and is executed in a very unclear manner because
of it’s difficulty and complication. Since concept
development is an ill-defined problem, there are currently
few effective tools that support product concept creation.
As a statement of a product concept is a set of various
features, product concept formation can be regarded as a
kind of feature selection problem. Because features can be
divided into groups or levels, all features cannot be
considered at the same level, which is one of the
difficulties of concept formation. Then, it is necessary to
use strategic approaches to avoid an overload of
combinations of features. The second difficulty is how to
define and treat marketing strategies. Finally, it is
necessary to draw out the creativity of experts related to
their foresight. To add to these three difficulties, the
characteristics of the marketing data should be considered
and suitable techniques should be applied. The marketing
data we can handle (e.g. questionnaire data) contains noise
and a data distribution that cannot be previously assumed.
Summing up, the necessities of computer aided
concept formation are: (i) Determine the format that will
express and convey the product concept visually, (ii)
Determine the strategic knowledge that will correspond to
real marketing strategy, (iii) Determine the effective
methods that can create the concept based on strategic
knowledge with expert’s creativity stimulated.
In the end, the problem is how domain experts get
simple and accurate knowledge from noisy data and then
get a creative and well-grounded product concept based
on the strategy.
This paper is organized as follows. In Section 2, we
explain the proposed models to approach the problems. In
Section 3, we explain the system called BICSS that was
developed based on the proposed method. In Section 4,we
show the results of the experiment applying BICSS to a
practical problem (the case of “beer products”). In Section
5, we briefly refer to related work. Finally, in Section 6,
some concluding remarks follow.
2. PROPOSED APPROACH
2.1. VBA-LAYER MODEL
In consumer goods, products are treated in the “brand”
context. The idea of using a name or a symbol to enhance
a product’s value has been known to marketers for a long
time, thus “brand” has some power. In recent years, the
marketing concepts of Brand Equity (BE) or Brand
Identity (BI) have been widely used [ 13 [2] [3] [4].
We thought that BI could be nearly equal to the
product concept and found out that the constituents of BI
could be classified to the three classes, ‘value’, ‘benefit’
and ‘attribute’, and BI could be expressed by the
connection of them. The ‘attribute’ means physical or
functional features and images of a product (e.g. “clear
taste”, “light taste” or “traditional brand image”). The
‘benefit’ means the emotional and instant feeling supplied
by a product (e.g.feel refreshed in mind and body). The
‘value’ means rational and final goal by consuming the
product (e.g. enjoy having fun times withj i e n d s ) .
We propose Value-Benefit-Attribute Layer Model
(VBA-Layer Model) to express product concept. This
novel model has a hierarchical structure corresponding to
the mental depth. The VBA-Layer Model is the format
that can express and convey the product concept visually.
By using VBA-Layer Model, the relationship of elements
in three layers can be grasped quantitatively. The product
concept can be expressed as links between conspicuous
elements of each layer. The each weight of the elements
and the links is calculated from questionnaire data, so that
it can be shown in a display, where the larger size of the
elements means the more important, and the thicker line
between elements means the tighter connection. Figure 1
shows the schematics of VBA-Layer Model.
m
:each feature
1
Figure 1. The Schematics of VBA-Layer Model
2.2. STRATEGIC APPROACH
Using the VBA-Layer Model we can visualize a product
concept as explicit information. Next, we insert marketing
strategy into concept development. Learning from the real
world where generally there are two types of strategy, topdown strategy and bottom-up strategy, two types of
marketing strategic knowledge are taken into
consideration in order to acquire an effective solution that
will fit the real marketing problems. We adopted “base
strategic knowledge”
and “modifying
strategic
knowledge”. Base strategic knowledge corresponds to the
top-down strategy at a managerial level, e.g. “What is the
position of this product vis-&vis competitors? Modifying
strategic knowledge is consumer-oriented strategy that is
bottom-up, e.g. “Which combination of product attributes
can raise consumers ’purchase intention?”
In our proposed method, the following techniques are
employed. To extract the modifying strategic knowledge,
evolutionary algorithms are used in which simulated
breeding (including Genetic Algorithm) and inductive
learning techniques are combined, because these
algorithms enable us to acquire simple and clear rules
from a questionnaire data in spite of the noise and the
uncertain distribution of the data. For the base strategic
knowledge a method like simple expert system is used,
because there is a generally observed formula for
managing products in marketing.
”
2.3. INTERACTIVE COMPUTING
Finally we proposed the 3-phased interactive computing
process as an effective method that can create the concept
based on strategic knowledge with expert’s creativity
stimulated. In general interactive computing method is a
generate-and-test method that is suitable for creative
support, therefore using this a base, we created an altered
application to fit the problem. Our method procedure was
as follows:
1) The ‘Base strategic knowledge’ that will be used is
determined by a technique like simple expert system.
2) The ‘Modifying strategic knowledge’ is extracted in
the form of decision rules by the interactive
evolutionary algorithms and suitable rules are selected
from all.
3) The product concept generated by using ‘base strategic
knowledge’ and ‘modifying strategic knowledge’ is
evaluated by a domain expert, and then according to
the evaluation a reward is returned to each rule
(knowledge) by reinforcement learning technique.
All the above 3 phases are interactive processes between
human and computer. These processes are repeated at
each stage until a domain expert is satisfied as Figure 2
shows. The 3-phased interactive computing process
enables the expert to generate a creative and wellgrounded product concept based on marketing strategy,
while also stimulating an expert’s creativity.
I -1044
thickness of lines in the same structure. Furthermore, the
important clusters of elements are pointed out on VBALayer model by using replies of the free word association
for each product/ brand.
The other part is strategic in which experts can create a
new concept according to their strategy, which is equal to
a new BI. In this strategic part, two types of strategic
knowledge, base strategic knowledge and modifying
strategic knowledge, are defined and utilized. The
procedure for the strategic part is:
By Using BSK and MSK
Stop
ifcxpcrt is satisficd
BSK: Basc Stratcgic Knowlcdgc
MSK: Modifying Stratcgic Knowlcdgc
Figure 2. The 3-phased Interactive Computing Process
3. SYSTEM OVERVIEW
We propose the system named BICSS (Brand Identity
Creation Support System). Figure 3 illustrates the
architecture of this system. The intended user for the
system is a marketing expert, such as a brand manager.
The system consists of two parts. One part is the analytic
by which experts can understand the current BI situation.
This is calculated based on questionnaire data and is
represented by the VBA-Layer model. The weight is
calculated based on the frequency obtained from
questionnaire data, with respect to each element of each
layer of Value, Benefit and Attribute. Similarly the weight
of Value-Benefit link and Benefit-Attribute link is
calculated based on the correlation revealed in the same
data.
.....................
Basic Structure
-*
/p
I
Stratcgic ~ o o p
I
<Algorithm of Concept Formation>
Step 1 : Initialization 1
Answer several questions that relate to the position of
the newlcurrent brand, so that the base strategic
knowledge is selected. Then, based on this, the initial
VBA-Layer structure is determined. This is Value-layer
weight ( V J , Benefit-layer weight (B,), Attribute-layer
weight (Ak), V-B link weight (V,B,) and B-A link weight
(LIPk).The i , j and k respectively represent the number
of value, benefit and attribute elements.
Step 2: Initialization 2
Execute SIBILE' to obtain the decision rules between
attributes of product and purchase intention. In this
stage, all decision rules are stored, and named the
modifying strategic rules.
Step 3: Selection of modifying strategic rules
Select subjectively and interactively the modifying
strategic rules that a user feels suitable for the task
brand.
Varying Attribute-layer weight (ak),Varying Benefitlayer weight (b,), Varying Value-layer weight (vJ,
Varying V-B link weight (v,b,) and Varying B-A link
weight (b,~,)are calculated as follows:
The weight ak;
If the feature is expressed in selected rules, then
ak= 1 .Ol(number of all features in selected rules)
Else ak=O.O
i
i ( B A * a-)
blur =
,"=:
( B J m* a m )
04m=I
'$1
I
v,b, =
"I='
( V B m *bm)
9
bian
b, = bman
e
f:
m=i n=I
2 v,bn
", = n
.
l
Decision Analytic LOOP
I/
Stratcgic- p w l e d g e
,
Modulc
Modifying Strategic
(if,llthen....)
(if._..
then ,.,,)
Knowledge by SlBlLE
1
Step 4: Fusion of base strategic knowledge and modifying
strategic knowledge
J
DFof
Qucstionnairc
Figure 3. The Architecture of BlCSS
The visualized techniques are: the weight of each element
is reflected in the size of each node in the VBA-Layer
structure, and the weight of each link is reflected in the
1-1045
SIBILE is a method in which simulated breeding is used to get
effective features from data and inductive learning (C4.5) is used
to acquire simple decision rules. In this experiment, fitness
function of GA is: F ( T ) = m f + P B + f l , where, T means a
corresponding decision tree, A=(accuracy of top three rules
generated from T to classify the data positive), B=(accuracy of T
to classify the data positive), C=(number of tree nodes with all
features)/(number of nodes of T). a ,/i'and y are parameters
experimentally determined. In our experiments, we set a = 1.0 ,
p = I .O , y = 0.5 . Please see [l I ] in detail.
D is defined as the weight of the modifying strategic
knowledge as compared with the base strategic
knowledge. The following values are calculated;
V,‘= V,+ D * vi ,
B/‘=B/+D*b/
,
Ak’=A+D*m ,
(V,B/)’= V I B /+ D
(B/Ak)’= B/Ak + D
* vtb/
* b,ak
,
D can be varied as an expert pleases. At each time D is
altered, he/she can confirm the result that visualizes the
VBA-Layer. Helshe can repeat until an appropriate D
value is obtained.
Step 5: Application of reinforcement learning to
modifying strategic knowledge
At the appropriate D value an expert evaluates the
VBA-Layer structure that expresses a product concept.
According to the evaluation (we set three levels: Good,
Moderate, Bad), the reward is delivered to the selected
modifying strategic rules, so that preference weight of
each rule is changed.
Step 6: Repetition of Steps
Steps 3-5 are repeated until an expert is satisfied. Even
if he/she is satisfied with the result in early cycles, this
cycle should be repeated at least 5 times.
Step 7: Indication of a result of reinforcement
Finally, some modifying strategic rules are selected
based on the preference weight that has changed
through Steps 3-6. The final concept is shown.
Step 8: Repetition of Steps
Steps 1-7 are repeated until an expert is satisfied with
the final result.
4. EXPERIMENTAL RESULTS
To validate the effectiveness of the proposed method, we
have carried out intensive experiments from a practical
case study on consumer questionnaire data.
4.1. METHODS
the purchase intention questions, respondents wrote down
their own free associations of each brand on a selfadministered questionnaire.
0
Domain Expert:
Three domain experts who are responsible for product
management in certain packaged consumer goods
companies evaluated the resulting knowledge. Each is
well versed in basic theory of marketing strategy and
product concept.
0
Experimental Methods:
Three assignments were given: First, to compose a brand
concept to revise “Lager” brand. Second, to revise
“Malts” brand, and third, to create a new brand concept
for launch on the market.
0
Implementation:
The experimental system BICSS was implemented on a
Windows95-Based Personal Computer. The SlBlLE
programs were written in Java language, and other
programs were written in Visual Basic language.
4.2. RESULTS
4.2.I
VBA-Layer Model
The effectiveness of VBA-Layer Model was investigated
by using the questionnaire data of 5 brands of beer. The
statistical tests, X 2 tests, were conducted on the 5 brands’
distribution of elements of ‘Value’, ‘Benefit’, ‘Attribute’
and ‘links between Value elements and Benefit elements’
respectively. In the case of ‘links between Benefit
elements and Attribute elements’, the test could not be
conducted because the number of degrees of freedom was
much larger than the number of samples. Table 1 shows
the results. In all cases, it was verified that the 5 brands
differed in characteristics of the data distribution in 95%
probability. Apart from the statistical test, experts could
easily distinguish the characteristics of each brand simply
by looking at the VBA-Layer Model results displayed on
the screen. It was verified that the VBA-Layer could
express and convey the product concept visually.
0
Questionnaire Used:
The mail survey was sent to 150 persons in August 1998.
The questionnaire had both qualitative and quantitative
questions about beer products in Japan. The questionnaire
contained numerous questions on brand imagery in
particular. The 150 mail surveys sent out yielded 99
useable respondents who were asked about 5 brands of
beer products each. The final number of respondents for
each brand summed to 347. The number of cases is near
the standard level in the task domain, and therefore large
enough to apply the proposed method.
1 1 value elements, 13 benefit elements and 35 attribute
elements were selected through pre-interviews.
Respondents of the questionnaire evaluated how well each
of the 35 attributes fit the brand (Fit, Moderate, and Does
not Fit). Then they selected one observed value from 11,
and chose some observed benefits from 13. At the same
time respondents answered their purchase intention for
each brand, by using a 7-point scale. Prior to answering
!
y
,
Dismissal
value
value: ~ ( 6 . ~ )
Value ( 6 ~ 3 6 )
67.49
51 .OO
Benefit
82.72
60.48
(6=44)
I
IAttribute (6.136)
1005.72
links betwenn Value
and Benefit ( ~ = Z M )
links betwenn ~
and Attribute
I
~
I
164.22
31 5.46
I
281.44
the testing could not be valid because the
number of demees of freedom is much larger
than the number of samples
~
~
f
I
qi :the number of degrees of freedom
p : probabitily. ~ 4 . 0 5
Table 1. The results of X 2 tests
1-1046
I
i
~
I
I
4.2.2
Strategic Concept Formation
We used protocol analysis to investigate the results of the
experiments. In this subsection, results of one experiment
are described in detail, and then results of all experiments
are briefly described.
In the assignment to revise “Lager” (Kirin Corp.), one
expert chose the following policy. “The present status of
this brand was unsatisfactory. The brand was in the
mature stage of the product life cycle. The main target
consumer of this brand was a heavy user of beer. The
brand should be set into a different product positioning
from all other beers.” The base strategic knowledge was
selected using this policy. Then the expert chose the
modifying strategic rule whose key feature was “familiar
imagery”. As a result of these choices, the structure
obtained can be interpreted as follows. “This brand has
high quality and familiar imagery. It has good flavored
taste. It gives relaxation and goes with meals well.” The
expert felt this concept was good because it was different
from both “Super Dry”(Asahi Corp.) and “IchibanShibori”(Kirin Corp.) and capitalized on the strong points
of “Lager”.
each expert evaluated the resultant concept as satisfying.
In the successful processes it was observed that not only
the link between value, benefit and attribute but also the
combination of the features in the attribute layer affected
the expert’s consideration. It was also observed that each
expert reached the satisfying conclusion by findings
acquired from the experimental process. This shows that
the proposed method is good for composing the product
concepts. The remaining one case, where the user (expert)
was less than satisfied with the conclusion, suggests there
are still some limits on the pre-determined features.
0
Stimuli for creativity
As mentioned above findings were often obtained
irrespective of individuals or assignments. In addition,
also in the stage for choosing the modifying strategic rules,
the expert received stimulation to hidher creative thoughts
because there were a number of rules whose conditional
clause consisted of various feature combinations.
Effectiveness of this method
The development of satisfying concept depended on two
matters. First, it was the matter whether unique and enough
features were prepared in advance or not. Second, it was
whether the methodology is appropriate or not.
In this experiment it could be inferred from the
successful result in 5 out of 6 cases that the pre-determined
features were generally sufficient. In addition, as to the
methodology there was a merit that falling into a local
thought could be prevented because not only the final
optimized decision rules but also all other decision rules
obtained through every IEC process could be accumulated
and used as modifying strategic knowledge. The
undesirable influence of the defect of IEC could be
reduced and almost all users could achieve a satisfying
result.
5. RELATED WORK
Figure 4. Example of Actual VBA-Layer Structure
Using this method, the user (expert) discerned a new
concept through the process that had stimulated her mind
using the VBA-layer structures. All users (experts)
reported the experimental process and findings were
useful and meaningful.
4.3. DISCUSSION
Experimental results have been evaluated by protocol
analysis in a qualitative way. There are several evaluation
points of view.
0
Satisfying Ability of the Resulting Concepts
In these experiments 6 valid cases’ by three experts were
obtained. (7 cases were conducted, but one was
interrupted due to time limitations.) In 5 out of 6 cases
One ‘case’ means execution of one assignment by one person.
1-1047
In recent years, development of knowledge discovery in
databases or data mining i s a key issue in various fields,
because of progress in data accumulation at large scale.
From this point of view various analyses have been
carried out on various problems including marketing
decision support [ 5 ] . Here, the Interactive Evolutionary
Computing (IEC) including Genetic Algorithm and
Simulated Breeding technique has attracted a great deal of
attention. The IEC is an effective method in fields where
human analysis, inference and integration in unstructured
and ill-defined problems play an important role. Since
many human creative activities cannot be broken down
into specific requirements, an interactive solution could be
effective and useful. Though psychological preference and
evaluation scales used by humans are changeable and vary
depending on the individual tasted, an IEC search has
been shown to be robust against data noise [6]. Early
studies are mainly focused on the graphics area [7], but
recently IEC has also been applied to engineering [8] [9]
[IO], knowledge discovery in larger scaled database, and
acquired knowledge in questionnaire data [ 1 I].
The IEC, however, has defects that there is restriction
about the number of individuals or generations in the
genetic operations in order to lighten the burden on users,
and that there sometimes occurs local optimized
convergence because subjective judgement in the IEC
process has too much influence on convergence.
The IEC was used to extract the ‘Modifying strategic
knowledge’ in our proposed method, where not only the
finally optimized decision rules but also all other decision
rules obtained through every IEC process can be
accumulated and used as a considered set. Therefore, the
influence of the defect in IEC can be reduced.
[ I I]
6. CONCLUDING REMARKS
In this paper, we have proposed a novel method for
strategic concept formation in the early conceptual stages
of product development. This method enables the
construction of computer-based representations of product
concept using marketing strategy, while also stimulating
the user to clarify the idea he/she had in mind. In these
experiments certain limits in using the pre-determined
features were suggested. As such, we intend to undertake
further improvements of the proposed method.
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