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Perceptual Mapping This module introduces two perceptual mapping methodologies: attribute rating and overall similarity. Authors: Ron Wilcox and Stu James © 2013 Ron Wilcox, Stu James and Management by the Numbers, Inc. There are two primary methods of constructing perceptual maps from consumer-level data: Attribute Rating Method PERCEPTUAL MAPPING Perceptual Mapping Overall Similarity Method Insight “Simple Graphics are often the most powerful way to communicate complicated statistical information.” - Edward R. Tufte, The Visual Display of Quantitative Information MBTN | Management by the Numbers 2 The Market for Sports Utility Vehicles (circa 2005) Prestigious Cadillac Escalade Hummer H2 Jeep Grand Cherokee Chevy Tahoe Nissan Pathfinder Toyota 4Runner Not Rugged Rugged Ford Explorer Jeep Cherokee SAMPLE PERCEPTUAL MAP (ATTRIBUTE RATING METHOD) Sample Perceptual Map (Attribute Rating) Not Prestigious MBTN | Management by the Numbers 3 Direct measurement of consumer perceptions of attributes and products based on the following question format: [Insert product name] is a [insert attribute] [insert product category]. Strongly Agree Agree Neither Agree Nor Disagree Disagree Strongly Disagree One question for each attribute for each product HOW IS THIS PERCEPTUAL MAP CREATED? How is this Perceptual Map Created? I believe [insert product name] is an excellent [insert product category]. Strongly Agree Agree Neither Agree Nor Disagree Disagree Strongly Disagree One question for each product MBTN | Management by the Numbers 4 Next, calculate the average attribute rating for each vehicle based on the survey population (summarize 1st question) Product 1 Product 2 ……….. Product N Attribute 1 Attribute 2 [average rating] …………. Attribute M MBTN | Management by the Numbers HOW IS THIS PERCEPTUAL MAP CREATED? How is this Perceptual Map Created? 5 Then, calculate the Avgerage Preference Rating score for each product based on the survey population (summarize 2nd question) MBTN | Management by the Numbers HOW IS THIS PERCEPTUAL MAP CREATED? How is this Perceptual Map Created? 6 Next, run a regression analysis of the data such that the attribute ratings for each product are the independent variables and the product preference is the dependent variable. Overall Preferencein = α + β1 Attrib1in + β2 Attrib2in + …+βM AttribMin + εin For example, the results of the regression analysis for the vehicles in this study might be: HOW IS THIS PERCEPTUAL MAP CREATED? How is this Perceptual Map Created? Overall Preference = -2.7 + 1.25 * Prestige + 2.5 * Ruggedness +... Insight The coefficients that are the greatest in absolute value will form the axis of your perceptual map. Note: This presumes all of the survey questions are on the same 1-5 scale. If the scale is not the same for all measures, this would not necessarily be true. MBTN | Management by the Numbers 7 Sports Utility Vehicles with Average Ratings Prestigious Cadillac Escalade (1.2, 4.7) Hummer H2 (4.5, 4.6) Jeep Chevy Tahoe (1.9, 3.8) Grand Cherokee (2.5, 3.8) Nissan Pathfinder (3.6, 4) Toyota 4Runner (3.5, 3.3) Not Rugged Rugged Ford Explorer (2.1, 2) Jeep Cherokee (4, 1.7) Insight Not Prestigious Now we also know that Prestigious / Not Prestigious and Rugged / Not Rugged had the highest absolute value coefficients in the regression. MBTN | Management by the Numbers SAMPLE PERCEPTUAL MAP (ATTRIBUTE RATING METHOD) Sample Perceptual Map (Attribute Rating) 8 Overall Preferencein = α + β1 Attrib1in + β2 Attrib2in + …+βM AttribMin + εin Can we get anymore information from this regression? YES! Overall Preference = -2.7 + 1.25 * Prestige + 2.5 * Ruggedness +… CONSTRUCTING AN IDEAL VECTOR Constructing the Ideal Vector • Take the ratio of the coefficient of the second-most important perceptual attribute to the most important. In this case we would have 1.25 / 2.5 = ½. • Plot the ideal vector with slope defined by this ratio and whose beginning point is at the origin of the graph (assumes most important attribute is on the X axis) as shown on the following slide. MBTN | Management by the Numbers 9 The Market for Sports Utility Vehicles (circa 2005) Prestigious Cadillac Escalade Hummer H2 Jeep Grand Cherokee Chevy Tahoe To more preferred (slope = ½) Nissan Pathfinder Toyota 4Runner Not Rugged From less preferred CONSTRUCTING AN IDEAL VECTOR Constructing the Ideal Vector Rugged Ford Explorer Jeep Cherokee Insight By drawing lines perpendicular to the ideal vector to each product we can “order” the vehicles from least preferred to most preferred. Not Prestigious MBTN | Management by the Numbers 10 Now let’s move to the Overall Similarity Method of creating perceptual maps. Here, rather than comparing products on particular attributes, we instead measure their overall similarity using a scaled method or by ranking products from most similar to least similar. While the methodologies are different, they offer similar interpretive challenges. On a scale from 1 (very different) to 5 (very similar), please compare [Product NameA] with [Product NameB]. Very Different (1) (2) (3) (4) Very Similar (5) MULTI-DIMENSIONAL SCALING (MDS) Multi-Dimensional Scaling (MDS) One question for each product pair Insight Notice how the question does not care why the responder rates the products as similar or different only the degree to which the responder perceives them to be. MBTN | Management by the Numbers 11 Here is a sample similarity matrix for a set of movies that corresponds to the average similarity ratings for a population based on the question from the previous slide. Movie Similarity Matrix About Schmidt Lord of Rings Gangs of NY Maid in Manhattan A Guy Thing About Schmidt 5.0 Lord of Rings 4.2 5.0 Gangs of NY 3.0 4.0 5.0 Maid in Manhattan 2.0 1.5 1.7 5.0 A Guy Thing 1.0 2.0 3.4 2.1 5.0 Bowling for Columbine 3.5 2.5 2.2 1.9 1.2 Bowling for Columbine MULTI-DIMENSIONAL SCALING Multi-Dimensional Scaling 5.0 Using this data, we can use a MDS program to plot these products in a two-dimensional space that best maintains their relative similarities as shown on the next slide. MBTN | Management by the Numbers 12 MULTI-DIMENSIONAL SCALING Multi-Dimensional Scaling MDS for Movie Data Final Configuration, dimension 1 vs. dimension 2 1.0 MAID 0.8 BOWL 0.6 Dimension 2 0.4 0.2 SCHMIDT 0.0 -0.2 RINGS -0.4 AGUY -0.6 -0.8 -1.0 NYGANGS -0.6 -0.2 0.2 0.6 1.0 1.4 Dimension 1 Notice that the axes are not defined on this map due to the nature of how the data is collected. Also, recognize that we are attempting to describe products that have an unknown number of perceived attributes in a two dimensional space. Does this impact our ability to use the map? Possibly. Let’s explore this further. MBTN | Management by the Numbers 13 One way to aid our interpretation of the map is to include some additional questions in the survey which can be used to enhance the MDS analysis. In effect, we are adding additional “products” that are pure perceptions. Very Different Nissan Pathfinder and Rugged 1 [ ] Very Similar 2 [ ] 3 [ ] 4 [ ] Very Different Reliable and Hummer 1 [ ] Very Similar 2 [ ] 3 [ ] 4 [ ] Very Different Rugged and Reliable 1 [ ] 5 [ ] 5 [ ] OVERALL SIMILARITY WITH PERCEPTUAL ATTRIBUTES Overall Similarity with Perceptual Attributes Very Similar 2 [ ] 3 [ ] 4 [ ] 5 [ ] MBTN | Management by the Numbers 14 Luxurious Cadillac Escalade Chevy Tahoe Ford Explorer Hummer H2 Nissan Pathfinder Jeep Toyota Grand Cherokee 4Runner Reliable Good Value Rugged Jeep Cherokee Insight SAMPLE PERCEPTUAL MAP (ATTRIBUTE RATING METHOD) MDS with Non-Product Perceptions Notice that the position of the brands, though similar to the attribute rating method, is different. This is due to different methodology used in MDS which captures overall perception of the brands rather than the direct measurement of an attribute such as ruggedness. There are no axes for guidance, only relative positioning to the perceptions. MBTN | Management by the Numbers 15 Another approach for creating perceptual maps using the overall similarity method is to have study participants rank each pair of products from most similar to least similar. One could also use the same data collected in the prior example to create this rank order. In addition to ranking each pair, one could also collect rank order or ratings on various perceptions (such as ruggedness, good value, etc.) and preference or sales data to aid in interpretation of the perceptual map as we’ll see in the following example. MULTI-DIMENSIONAL SCALING (MDS) Multi-Dimensional Scaling (MDS) The following screen shows a perceptual map created from a survey from a particular target market segment in an automobile simulation. In this example, sales data was also collected for this segment, and brands were rated on three dimensions: Price, size, and dealer service. Finally, data was also collected about the characteristics of their ideal brand. Let’s take a look at this perceptual map and attempt to interpret the information provided. MBTN | Management by the Numbers 16 MDS EXAMPLE WITH VECTORS AND STRESS MDS Example with Vectors and Stress Before considering the vector and ideal brand information, let’s look at the relative positioning of the brands themselves. We could say that brands A and B are perceived as being fairly similar (and brands E, G and I as well). We could also say that brand F is perceived as very different from all other brands in the study. Take a moment to consider what underlying factors might be driving the positioning of the vehicles. MBTN | Management by the Numbers 17 Stress is a measure of how well the map captures the brand relationships in two dimensions. Lower is better. Under .20 is good, .20 - .40 is acceptable, over .40 means that the map is struggling to capture the relationships in 2 dimensions. r2 measures how well the position of the vector on the map reflects the ratings data collected (1.0 means perfectly correlated). Estimated preferred, expected or ideal position for the customer is marked by the “*” Top 10 brands for customer are listed in order of sales to the target customer segment MDS EXAMPLE WITH VECTORS AND STRESS MDS Example with Vectors and Stress With this additional information, what can we say about the map? MBTN | Management by the Numbers 18 First we can say that this map does a good job capturing the relative positioning of the brands in a two dimensional space (stress = .17). Next we can say that as we move from left to right on the map, we’re generally going from lower priced brands to higher priced brands and that it appears that this customer prefers a lower priced brand. We can also say that as we move from the bottom left to the top right brands are going from small to large (in size). Since the r2 is fairly high on these two dimensions (and the stress is low), these relationships are fairly accurate. MULTI-DIMENSIONAL SCALING (MDS) Multi-Dimensional Scaling (MDS) As we might expect, since brands A and B are closest to the ideal, their sales are the highest. We could also say that despite the relatively good positioning of brand J, something is keeping it from achieving higher sales that is not captured on the map (distribution, advertising, etc.) MBTN | Management by the Numbers 19 Dolan, Robert J. "Perceptual Mapping: A Manager's Guide." Harvard Business School Background Note 590-121, July 1990. FURTHER REFERENCE Further Reference Michael Deighan, Stuart W. James, and Thomas C. Kinnear, StratSimMarketing: The Marketing Strategy Simulation, Interpretive Software, 2011 Sawtooth Technologies http://www.sawtooth.com MBTN | Management by the Numbers 20