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. REFERENCES Aaker, D. A.: Managing Brand Equity: Capitalizing on the Value of a Brand Name, The Free Press (1 991) Keller, K. L: Conceptualizing, Measuring, and Managing Customer-based Brand Equity, Journal of marketing, Vo1.57 (January), pp. 1-22, (1993). Simon, C. J. and M. W. Sullivan: The Measurement and Determinants of Brand Equity: A Financial Approach, Marketing Science, (Winter), 1993. Aaker, D. A.: Building Strong Brands, The Free Press ( I 996) Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining, AAAVMIT Press (1 995) Osaki, M., Takagi, H., and Ohya, K.: An Input Method Using Discrete Fitness Values for Interactive CA, J. of Intelligent and Fuzzy Systems, Vo1.6, No. 1, pp. 13 1-145 ( 1 998). Dawkins, R.: The Blind Watchmaker, Longman, Essex (1986). Parmee, I. C.: The Concrete Arch Dam: An Evolutionary Model of the Design Process, in Int ‘I ConJ on Artificial Neural Nets and Genetic Algorithms, Innsbruck, Austria, pp.544-555 (1993). Furuta, H., Maeda, K. and Watanabe, E.: Application of Genetic Algorithm to Aesthetic Design of Bridge Structures, Microcomputers in Civil Engineering, Vol. 10, No.6, pp.415-421 (1 995). Graf, J. and Banzhaf, W.: Interactive Evolution of Images, in McDonnell, J. R., Reynolds, R. G. and Fogel, D. B. eds., 4IhAnnual ConJ on Evolutionary Programming, San Diego, CA, USA, MIT Press, pp.53-65 (1995) I -1048 T.Terno, Y.lshino: Interactive Genetic Algorithm Based Feature Selection and Its Application to Marketing Data Analysis. in Huan Liu, H.Motoda (eds.): Feature Extraction Construction and Selection: A Data Mining Perspective, Kluwer, pp.393-406 ( 1 998)