I need help with part 1 please.

Perceptual Maps The construction of a perceptual map helps marketing strategists better understand the positioning of their product/service, and the positioning of their competitors' products/services. A product's position, of course, is the way in which consumers perceive a product on important features relative to competing products. As a very simplified example, if a marketer is interested in how her brand of beer (brand A) and two competing brands of beer (brands B and C) are perceived by target consumers, she might ask consumers to answer questions such as: To what degree do the following characteristics describe Brand A? (On a scale of 1-10, where l = not at all, and 10 = perfectly describes). perfectly High priced: 1 2 3 4 5 6 7 8 9 10 not at all AAAHH H Light: 1 2 3 4 5 6 7 8 9 10 not at all HHHHHHHH. To what degree do the following characteristics describe Brand B? (On a scale of 1-10, where l = not at all, and 10 - perfectly describes). High priced: : 1 not at all A 2 3 4 10 5 6 7 8 9 H A Light: 3 4 5 8 9 10 not at all + perfectly describes To what degree do the following characteristics describe Brand C? (On a scale of 1-10, where l = not at all, and 10 - perfectly describes). High priced: 1 2 ? 3 4 5 6 7 8 9 10 not at all F H H H H describes Light: 8 9 10 If the target market being investigated is fairly homogenous, and so averaging the responses would not forego important data, she can look at the average responses by consumers in this target segment, and plot those mean responses on a two-dimensional map. For instance, if Brand A received an average response of 8 on the "high priced" dimension and 3 on the "light" dimension, and Brand B received an average response of 3 on the "high priced" dimension and 2 on the "light" dimension, and Brand C received an average response of 5 on the "high priced" dimension and 4 on the "light" dimension, she could plot these mean responses as follows: high priced A II not light H HHH HH light B . + low priced Although the above is obviously a very simplified example, perceptual maps can be quite useful when investigating consumer perceptions of many different brands on many different attributes. In general, software systems are used to plot these results in multidimensional space. The number of dimensions of a perceptual map is equal to the number of attributes that are investigated in a perceptual mapping application. In the simplified example above, there are just two product attributes: high priced and light. So in this example, the resulting data can casily be plotted in two-dimensional space. However, most perceptual mapping applications involve more than two product attributes, and when this is the case, creative plotting strategies are needed to represent the multidimensional data results in two- dimensional space. Many times, vectors or lines are used to represent the many different attributes, and the distance between each brand and the attribute line denotes the degree to which consumers perceive that attribute as describing that brand. For example, consider the amount of information that appears in the perceptual map recreated on the next page. In this example, the authors have attempted to visually represent a large number of armchair types in this case, the armchairs can be thought of as the different brands) on a large number of attributes. Chuang, Y., & Chen, L.-L. (2008). "How to Rate 100 Visual Stimuli," International Journal of Design. Complex Emotional Exaggerated Traditional Y M . Heavy Contemporary Realistic Simple Rational Source: Chuang, Y., & Chen, L.-L. (2008). "How to Rate 100 Visual Stimuli," International Journal of Design. Many of the current perceptual mapping software systems utilize a type of factor analysis in their analysis of data such that when the different brands are all perceived in the same way on an attribute, that attribute is deleted, since it generates no new information. In other words, because all brands are perceived in exactly the same way on that attribute, the inclusion of the attribute does not contribute any information to an understanding of the relative positioning of brands, and so the attribute falls out of the data mix. This approach also results in the combination of two or more attributes if the data related to those attributes are highly correlated. If, for example, when consumers are responding to questions regarding attributes of winter tires, they perceive the attribute "handling" in exactly the same way as the attribute traction" across all rated brands, it is likely that these two attributes are viewed by the consumer as one and the same. In this case, the two highly correlated attributes should be combined into one attribute, since the data from one attribute merely mimics the other, providing no new information. Often when this is done, the resulting combined attribute is given an appropriate name, such as "handling and traction." Part 1 You are responsible for a brand of frozen pizza. You have four close competitors in the region, and you are interested in obtaining a better understanding of consumers' perceptions of all five frozen pizzas (Brands A, B, C, D, E) on four product attributes: nutritional content, price, freshness of ingredients, and doughy crust. You survey a large number of members of your target market segment, young moms, with the following question format: To what degree do the following characteristics describe (insert-Brand A, B, C, D, E)? (On a scale of 1-10, where l = not at all, and 10 = perfectly describes). (Attributeinsert one: high nutritional content, high price, fresh ingredients, doughy crust): 1 2 3 4 5 6 7 8 9 10 not at all H HHHHHHH (Such that all five brands are rated on all four attributes.) The resulting data are fairly consistent among respondents, meaning that this particular target market segment is fairly homogeneous in its perceptions of frozen pizzas. Therefore, you feel comfortable averaging responses. The results of the survey, with average responses rounded to the nearest whole number, are displayed below: Attributes Brand A Brand B Brand C Brand D Brand E ABS Nutrition Price Ingredients Crust Please create a perceptual map for these results in the space below. Perceptual Maps The construction of a perceptual map helps marketing strategists better understand the positioning of their product/service, and the positioning of their competitors' products/services. A product's position, of course, is the way in which consumers perceive a product on important features relative to competing products. As a very simplified example, if a marketer is interested in how her brand of beer (brand A) and two competing brands of beer (brands B and C) are perceived by target consumers, she might ask consumers to answer questions such as: To what degree do the following characteristics describe Brand A? (On a scale of 1-10, where l = not at all, and 10 = perfectly describes). perfectly High priced: 1 2 3 4 5 6 7 8 9 10 not at all AAAHH H Light: 1 2 3 4 5 6 7 8 9 10 not at all HHHHHHHH. To what degree do the following characteristics describe Brand B? (On a scale of 1-10, where l = not at all, and 10 - perfectly describes). High priced: : 1 not at all A 2 3 4 10 5 6 7 8 9 H A Light: 3 4 5 8 9 10 not at all + perfectly describes To what degree do the following characteristics describe Brand C? (On a scale of 1-10, where l = not at all, and 10 - perfectly describes). High priced: 1 2 ? 3 4 5 6 7 8 9 10 not at all F H H H H describes Light: 8 9 10 If the target market being investigated is fairly homogenous, and so averaging the responses would not forego important data, she can look at the average responses by consumers in this target segment, and plot those mean responses on a two-dimensional map. For instance, if Brand A received an average response of 8 on the "high priced" dimension and 3 on the "light" dimension, and Brand B received an average response of 3 on the "high priced" dimension and 2 on the "light" dimension, and Brand C received an average response of 5 on the "high priced" dimension and 4 on the "light" dimension, she could plot these mean responses as follows: high priced A II not light H HHH HH light B . + low priced Although the above is obviously a very simplified example, perceptual maps can be quite useful when investigating consumer perceptions of many different brands on many different attributes. In general, software systems are used to plot these results in multidimensional space. The number of dimensions of a perceptual map is equal to the number of attributes that are investigated in a perceptual mapping application. In the simplified example above, there are just two product attributes: high priced and light. So in this example, the resulting data can casily be plotted in two-dimensional space. However, most perceptual mapping applications involve more than two product attributes, and when this is the case, creative plotting strategies are needed to represent the multidimensional data results in two- dimensional space. Many times, vectors or lines are used to represent the many different attributes, and the distance between each brand and the attribute line denotes the degree to which consumers perceive that attribute as describing that brand. For example, consider the amount of information that appears in the perceptual map recreated on the next page. In this example, the authors have attempted to visually represent a large number of armchair types in this case, the armchairs can be thought of as the different brands) on a large number of attributes. Chuang, Y., & Chen, L.-L. (2008). "How to Rate 100 Visual Stimuli," International Journal of Design. Complex Emotional Exaggerated Traditional Y M . Heavy Contemporary Realistic Simple Rational Source: Chuang, Y., & Chen, L.-L. (2008). "How to Rate 100 Visual Stimuli," International Journal of Design. Many of the current perceptual mapping software systems utilize a type of factor analysis in their analysis of data such that when the different brands are all perceived in the same way on an attribute, that attribute is deleted, since it generates no new information. In other words, because all brands are perceived in exactly the same way on that attribute, the inclusion of the attribute does not contribute any information to an understanding of the relative positioning of brands, and so the attribute falls out of the data mix. This approach also results in the combination of two or more attributes if the data related to those attributes are highly correlated. If, for example, when consumers are responding to questions regarding attributes of winter tires, they perceive the attribute "handling" in exactly the same way as the attribute traction" across all rated brands, it is likely that these two attributes are viewed by the consumer as one and the same. In this case, the two highly correlated attributes should be combined into one attribute, since the data from one attribute merely mimics the other, providing no new information. Often when this is done, the resulting combined attribute is given an appropriate name, such as "handling and traction." Part 1 You are responsible for a brand of frozen pizza. You have four close competitors in the region, and you are interested in obtaining a better understanding of consumers' perceptions of all five frozen pizzas (Brands A, B, C, D, E) on four product attributes: nutritional content, price, freshness of ingredients, and doughy crust. You survey a large number of members of your target market segment, young moms, with the following question format: To what degree do the following characteristics describe (insert-Brand A, B, C, D, E)? (On a scale of 1-10, where l = not at all, and 10 = perfectly describes). (Attributeinsert one: high nutritional content, high price, fresh ingredients, doughy crust): 1 2 3 4 5 6 7 8 9 10 not at all H HHHHHHH (Such that all five brands are rated on all four attributes.) The resulting data are fairly consistent among respondents, meaning that this particular target market segment is fairly homogeneous in its perceptions of frozen pizzas. Therefore, you feel comfortable averaging responses. The results of the survey, with average responses rounded to the nearest whole number, are displayed below: Attributes Brand A Brand B Brand C Brand D Brand E ABS Nutrition Price Ingredients Crust Please create a perceptual map for these results in the space below