Perceptual Map of Foreign Beers in the PRC: An Example BackgroundBecause of its potential to surpass the US as the largest beer market in the world (in terms of volume) by the year 2000, the People’s Republic of China (PRC) is a very attractive market for every multinational beer company. Several multinational beer companies are currently operation in China. Some companies have been quite successful while some are still struggling to gain foothold in this market. The example demonstrated in this paper is an actual research that was conducted for a multinational beer company. The objective of this study is to investigate the beer consumption behavior of Chinese consumers as well as to investigate the relative positions of several leading foreign beers in these consumers’ mind. Only the procedure, findings, and managerial implications of the perceptual mapping of the research will be demonstrated and discussed here.Research procedureStep1: Exploratory ResearchThe objective of the exploratory research was to identify product attributes that are important to Chinese beer drinkers. The product attributes identified were then used to design the questionnaire for the main study. In the exploratory research, a focus group interview of eight Chinese consumers was conducted to identify attributes or characteristics of beer that are important to them. Altogether, ten product attributes were identified: package design, taste, color, level of alcohol, variety of sizes, price, country of origin, availability, advertising and reputation.Step2: Questionnaire DesignBased on the product attributes identified in the focus group interview, the questionnaire for the main study was designed. (See the questionnaire distributed earlier.) Although five brands of foreign beers were included in the study: Carlsberg, Foster, Guinness, Heineken, and San Miguel. Note that the first ten questions for each brand is the question for measuring the overall attitude toward the brand and the intention to choose the brand. The attitude rating question is needed for the identification of the “ideal line”on which the ideal position is located by using the attitude toward the brand as the dependent variable and he factor scores as the independent variables. The beta coefficients of the independent variables (which represent the relative important of each factor score in influencing the attitude) will be used to calculate the angle for the ideal line. The intention rating is needed to measure the relationship between the attitude toward the brand and the intention to buy by using the intention as the dependent variable and the attitude as the independent variable. This regression analysis is needed to see whether or not attitude toward the brand is a goodpredictor of intention to buy the brand.Step3: The Main StudyResearch Design. The research design used in the main study was a sample survey by face-to-face interview.Sample and Sampling Procedure.The data were collected from Chinese beer drinkers in Guangzhou, Beijing, and Shanghai by convenient sampling at restaurants and bars/pubs. Altogether, 67 complete and useable questionnaires were obtained. 79% of the samples were male. For martial status, 56.7% were married. In terms of education, 88.1% had college education. For occupation, 44.8% were professional, 43.3% were white collars.Data Collection. The data were collected by personal interview at restaurant and bars/pubs. The interviewers read the questions in the questionnaire to the respondent and recorded the answers obtained in the questionnaire.Step 4: Data AnalysisData Coding and Data Entry. After the data collection was completed, the questionnaires were coded and entered into SPSS for Windows Version 12.0.1. Since each respondent evaluate five brands of beers, the data from each respondent are represented by five “cards”or rows of data input. Exhibit 1 shows the sample of the data input file.Statistical Analysis. To create perceptual maps for the foreign beer in China, the coordinates for each brand of beers are needed. For perceptual mapping by factor analysis, the coordinates for the brands are obtained by averaging the factor score of each brand across the respondents. The factor scores can be obtained by the following procedure:a.From the main menu, select “Analyze” then select “Data Reduction” then select “FactorAnalysis”(see Exhibit 2). The factor analysis window will be open at this point. The factor analysis window consists of a box showing all variables in the data set and the “Variables”box. There are also five bottoms for options including “Descriptive”, “Extraction”, “Rotation”, “Scores”, and “Options”.b.Select variables that represent product attributes (i.e., X1 to X10) from the box showingthe list of variables into the “Variables” box (see Exhibit 3). This step identifies variables to be analyzed by factor analysis.c.Open “Rotation”box and select “Varimax”rotation (see Exhibit 4), and then click“Continue”. This step specifies the type rotation1to be used.d.Open “Scores” box and select “Save as variables” and for “Method” select “regression”(see Exhibits 5), and then click “Continue”. This step will create factor scores by regression method and add them to the data set as new variables.1Varimax is an orthogonal rotation method. The objective of rotation is to simplify the rows and columns of the factor matrix to facilitate interpretation. Varimax is an orthogonal rotation method that centers on simplifying the columns of the factor matrix, that is, making as many values in each column as close to zero as possible (i.e., making the number of “high”loadings as few as possible).e.For “Extraction” use defaults for “Method” (i.e., Principal component), “Analyze” (i.e.,Correlation matrix), “Display”(i.e., Unrotated factor solution) and “Extract”(i.e., Eigenvalues over 1), and then click “Continue”. This step will command the program to compute correlation matrix for the variables from the raw data. This correlation matrix will be, in turn, analyzed to obtain a factor matrix. Principal component analysis is specified as the method of choice in extracting the factors. For the number of factors to be extracted, the latent root or eigenvalue criterion (greater than 1) will be used. The (unrotated ) component matrix will also be given as an output.f.For “Descriptive,”use default for “Statistics”(i.e., Initial solution) and then click“Continue”.g.For “Options”use default for “Missing Values”(i.e., Excluded cases listwise). For“Coefficient Display Format”, select “Sort by size”(see Exhibit 6), and then click “Continue”. This step will create factor matrices of which variables are sorted by size in descending order.h.At this point, we are back at the factor analysis window. Click “OK” to run the program.The output will be shown in the output window.Results and InterpretationBased on the above factor analysis procedure, the following output are obtained: Communalities, Total V ariance Explained, Component Matrix (or Factor Loadings Table), Rotated Component Matrix (or Rotated Factor Loading Table), and Component Transformation Matrix. The relevant outputs for perceptual mapping are the Total Variance Explained and the Rotated Component (or Factor) Matrix. See the output distributed earlier for detail.Step1: Determination of the Number of Factors to RetainThe first step in the analysis of results is to determine the number of factors to be retained for further analysis. The Total Variance Explained in the output contains the information regarding the ten possible factors and their relative explanatory power as expressed by their eigenvalues. In addition to assessing the importance of each factor, eigenvalues are also used to determine the number of factors to be retained. According to the latent root or eigenvalue criterion, two factors will be retained because only component 1 (factor 1) and component 2 (factor 2) have latent roots or eigenvalues greater than 1 (see Exhibit 7).Step2: Naming the FactorsThe naming process involves substantive interpretation of the pattern of factor loadings2for the variables, including their signs, in an effort to name each of the factors. Before interpretation, a minimum acceptable level of significance for factor loading must be selected. 2A factor loading represents the relation between an original variable and its factor.All significant factor loadings typically are used in the interpretation process. But variables with higher loadings influence to a greater extent the name or label selected to represent a factor.The Varimax rotated component analysis factor matrix which contains factor loadings is shown in the Rotated Factor Matrix (or factor loading table) in the output (Exhibit 8). For the sample size of 67, factor loadings have to be ±0.65 or above to be considered significant. From the matrix, it’s clear that taste, color, package design, reputation, and country of origin load significantly on Factor 1, and price, availability, level of alcohol, level of advertising, and variety of sizes load significantly on Factor 2. None of the variables loads significantly on more than one factor.For Factor 1, all the five variables (taste, color, package design, reputation, and country of origin) represent different dimensions of the product. It is reasonable to name Factor 1 as product quality. For Factor 2, the five variables (price, availability, level of alcohol, level of advertising, and variety of sizes) represent the other three components of the marketing mix. It is reasonable to name the factor marketing activities.Note that the naming process is based on the subjective opinion of the researcher. For this reason, the process of naming factors is subject to considerable criticism. But if a logical name can be assigned that represents the underlying nature of the factors, it usually facilitates the presentation and understanding of the factor solution and therefore is a justifiable procedure.Step 3: Identification of Positions of the BrandsSince two factors are obtained in the factor analysis process, two factor scores are generated and added to the data set. Factor scores are composite measure of each factor computed for each subject for each brand. In this study, two factor scores can be used to replace the original set of ten variables (fac_1 and fac_2).The positions for the brands included in the study are obtained by averaging the factor scores across the respondents for each brand. The two average factor scores for the brands become the coordinates for plotting on a two-dimensional map. See the average factor scores for the five brands of foreign beers in the output distributed earlier.Step 4: Plotting the Perceptual MapWith the average factor scores of all the five brands, a perceptual map can be plotted by hand on graph paper or by software such as Microsoft Excel. If software is used to plot the map, make sure that both axises of the map has the same ratio. This is important because we still need to identify the ideal line and ideal position for the brand. This ideal position, in turn, will be used to guide the marketing mix strategy.One can use either factor 1 of factor 2 as the y (vertical) or x (horizontal) axis. In this example, factor 1 (product quality) is the y (vertical) axis and factor 2 (marketing activity) is the x (horizontal) axis. Note that the two axises cross each other at (0, 0) position. This is so because factor scores are standardized scores (with the mean of zero and standard deviationof one). See he perceptual map for five foreign beers in the PRC in Exhibit 9.According to the map, the respondents perceived that Carlsberg to be highest in terms of product quality (0.445) followed by Heineken (0.363), San Miguel (0.190), Guinness (-0.406), and Foster’s (-0.592), respectively. In terms of marketing activities, San Miguel were perceived by the respondents to be highest (0.408). This is followed by Carlsberg (0.138), Foster’s (-0.110), Guinness (-0.190), and Heineken (-0.246).Step 5: Identification of the Ideal Line and the Ideal PositionThe ideal line for this map can be identified by first regression the two factor scores (which represent product attributes) on the overall attitude toward the brand. Based on the standardized coefficients,Attitude toward the brand = 0.644*FS1 + 0.375*FS2, R2 = 0.555Since the coefficients for both FS1 and FS2 are significant and have positive sign, the upper right quadrant is the place for the ideal position. Based on the magnitude of the coefficients, it is clear that perceived product quality has more positive impact on consumers’attitude toward the brand than perceived marketing activities. As such, the ideal line should incline toward the vertical axis rather than toward the horizontal axis.The ideal line can be identified by adding the two coefficients together and equating the sum (0.644+0.375=1.019) to 90 degree. Then the degree of for each coefficient is calculated based on this sum. For example, if the coefficient of FS1 is used, the degree of the coefficient is equal to (0.644x90)/1.019 = 56.9 degree. So the ideal line is about 57 degree from the horizontal axis. The ideal position should be some where along the ideal line.According to perceptual map, it is clear that Carlsberg is in a relatively good position. It has high product quality image and its marketing activities (pricing, distribution, and advertising) is acceptable. San Miguel is perceived as having the best marketing activities (i.e., very reasonable price, extensive distribution, and high level of advertising). Heineken has high quality but its marketing activities were perceived as very low. Guinness and Foster’s were perceived as low in both dimensions.One thing managers have to do after obtaining the map is to ask themselves whether or not the map reflects reality in the marketplace. According to the manager who sponsored the study, this perceptual map reflects quite accurately the situation in foreign beer market in China. Both Carlsberg and Heineken have high quality image, but Carlsberg is much more active in terms of distribution and advertising. San Miguel is currently the most aggressive brand in terms of distribution and advertising. Its image is still lower than those of Carlsberg an Heineken because its country of origin is the Philippines. Its image, however, is still more positive tan those of Guinness and Foster’s, both of which are not active in distribution or promotion.Managerial ImplicationsBased on the perceptual map, the marketing mix strategy for each brand can be derived. Let’s use Guinness as an example. Guinness is an Irish beer (stout) originated in Ireland more than 200 years ago. Guinness stout is famous for its black color, rich foam, and rich flavor because of its special brewing formula. It is clear that Guinness is different from all other foreign beer in terms of its appearance (color).Based on the map, Guinness needs to move its position in the consumer’s mind to the ideal position in order to be successful. Specifically, it needs to move to the right and upward. To move effectively, manager of Guinness can use the Rotated Factor Matrix as the guideline to formulate their marketing mix strategy. For example, to move upward, the most effective mean is to improve either taste, color, or package design because the factor loadings of these three product attributes are the highest. Package design may not be very difficult to change or modify. The taste and the color, however, are very difficult if not impossible to change, because they are unique characteristics of Guinness stout.So managers of Guinness have to find out whether the taste or the color of the beer of both is the problem. A blind taste test may be first conducted to see if, whether seeing the color, the taste of Guinness is bad for Chinese customers. If the blind taste test shows that the taste is bad, then Guinness may have to reformulate their taste, if they want to be successful in China. If the blind taste test shows the taste is acceptable, another taste test is needed. And this time, customers are allowed to see the color of the beer. If the test shows that the taste is bad, it means that color has an impact on customers’perception of taste. Guinness may need to reformulate its product to come up with a lighter color. On the other hand, Guinness may choose to educate the Chinese customers the unique taste and color of its beer.To move to the right, the most effective way is for Guinness to increase its distribution. The next best thing that Guinness should do is to adjust its price to be more “reasonable”. Finally, Guinness can increase its advertising/promotion activities.Exhibit 2Exhibit 4Exhibit 6Exhibit 8Exhibit 911。
