Presentation on theme: "Perceptual Mapping This module introduces two perceptual mapping methodologies: attribute rating and overall similarity. Authors: Ron Wilcox and Stu James."— Presentation transcript:
P ERCEPTUAL M APPING 2 Perceptual Mapping MBTN | Management by the Numbers There are two primary methods of constructing perceptual maps from consumer-level data: Attribute Rating Method 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
S AMPLE P ERCEPTUAL M AP (A TTRIBUTE R ATING M ETHOD ) 3 Sample Perceptual Map (Attribute Rating) MBTN | Management by the Numbers Prestigious Not Prestigious Cadillac Escalade Chevy Tahoe Jeep Grand Cherokee Nissan Pathfinder Hummer H2 Toyota 4Runner Ford Explorer Jeep Cherokee Not RuggedRugged The Market for Sports Utility Vehicles (circa 2005)
I believe [insert product name] is an excellent [insert product category]. Strongly Agree Neither Agree Nor Disagree Disagree Strongly Disagree H OW IS THIS P ERCEPTUAL M AP C REATED ? 4 How is this Perceptual Map Created? MBTN | Management by the Numbers Strongly Agree Neither Agree Nor Disagree Disagree Strongly Disagree [Insert product name] is a [insert attribute] [insert product category]. One question for each attribute for each product Direct measurement of consumer perceptions of attributes and products based on the following question format: One question for each product
H OW IS THIS P ERCEPTUAL M AP C REATED ? 5 How is this Perceptual Map Created? MBTN | Management by the Numbers Next, calculate the average attribute rating for each vehicle based on the survey population (summarize 1 st question) Product 1Product 2………..Product N Attribute 1 Attribute 2[average rating] …………. Attribute M
H OW IS THIS P ERCEPTUAL M AP C REATED ? 6 How is this Perceptual Map Created? MBTN | Management by the Numbers Then, calculate the Avgerage Preference Rating score for each product based on the survey population (summarize 2 nd question)
H OW IS THIS P ERCEPTUAL M AP C REATED ? 7 How is this Perceptual Map Created? MBTN | Management by the Numbers 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 Preference in = α + β 1 Attrib1 in + β 2 Attrib2 in + …+β M AttribM in + ε in Overall Preference = * Prestige * Ruggedness +... For example, the results of the regression analysis for the vehicles in this study might be: 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.
S AMPLE P ERCEPTUAL M AP (A TTRIBUTE R ATING M ETHOD ) 8 Sample Perceptual Map (Attribute Rating) MBTN | Management by the Numbers Sports Utility Vehicles with Average Ratings Prestigious Not Prestigious Cadillac Escalade (1.2, 4.7) Chevy Tahoe (1.9, 3.8) Jeep Grand Cherokee (2.5, 3.8) Nissan Pathfinder (3.6, 4) Hummer H2 (4.5, 4.6) Toyota 4Runner (3.5, 3.3) Ford Explorer (2.1, 2) Jeep Cherokee (4, 1.7) Not RuggedRugged Insight Now we also know that Prestigious / Not Prestigious and Rugged / Not Rugged had the highest absolute value coefficients in the regression.
C ONSTRUCTING AN I DEAL V ECTOR 9 Constructing the Ideal Vector MBTN | Management by the Numbers Can we get anymore information from this regression? 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. Overall Preference in = α + β 1 Attrib1 in + β 2 Attrib2 in + …+β M AttribM in + ε in Overall Preference = * Prestige * Ruggedness +… YES!
C ONSTRUCTING AN I DEAL V ECTOR 10 Constructing the Ideal Vector MBTN | Management by the Numbers Prestigious Not Prestigious Cadillac Escalade Chevy Tahoe Jeep Grand Cherokee Nissan Pathfinder Hummer H2 Toyota 4Runner Ford Explorer Jeep Cherokee Not RuggedRugged The Market for Sports Utility Vehicles (circa 2005) From less preferred To more preferred (slope = ½) Insight By drawing lines perpendicular to the ideal vector to each product we can “order” the vehicles from least preferred to most preferred.
M ULTI - DIMENSIONAL S CALING (MDS) 11 Multi-Dimensional Scaling (MDS) MBTN | Management by the Numbers Very Different (1) (2) (3) (4) Very Similar (5) On a scale from 1 (very different) to 5 (very similar), please compare [Product Name A ] with [Product Name B ]. One question for each product pair 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. 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.
M ULTI - DIMENSIONAL S CALING 12 Multi-Dimensional Scaling MBTN | Management by the Numbers Movie Similarity Matrix About Schmidt Lord of Rings Gangs of NY Maid in Manhattan A Guy Thing Bowling for Columbine About Schmidt5.0 Lord of Rings Gangs of NY Maid in Manhattan A Guy Thing Bowling for Columbine 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. 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.
M ULTI - DIMENSIONAL S CALING 13 Multi-Dimensional Scaling MBTN | Management by the Numbers 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.
O VERALL S IMILARITY WITH P ERCEPTUAL A TTRIBUTES 14 Overall Similarity with Perceptual Attributes MBTN | Management by the Numbers 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 Very Similar Nissan Pathfinder and Rugged 1 [ ] 2 [ ] 3 [ ] 4 [ ] 5 [ ] Very Different Very Similar Reliable and Hummer 1 [ ] 2 [ ] 3 [ ] 4 [ ] 5 [ ] Very Different Very Similar Rugged and Reliable 1 [ ] 2 [ ] 3 [ ] 4 [ ] 5 [ ]
S AMPLE P ERCEPTUAL M AP (A TTRIBUTE R ATING M ETHOD ) 15 MDS with Non-Product Perceptions MBTN | Management by the Numbers Insight 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. Cadillac Escalade Chevy Tahoe Jeep Grand Cherokee Nissan Pathfinder Hummer H2 Toyota 4Runner Ford Explorer Jeep Cherokee Luxurious Reliable Rugged Good Value
M ULTI - DIMENSIONAL S CALING (MDS) 16 Multi-Dimensional Scaling (MDS) MBTN | Management by the Numbers 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. 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.
MDS E XAMPLE WITH V ECTORS AND S TRESS 17 MDS Example with Vectors and Stress MBTN | Management by the Numbers 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.
MDS E XAMPLE WITH V ECTORS AND S TRESS 18 MDS Example with Vectors and Stress MBTN | Management by the Numbers 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 r 2 measures how well the position of the vector on the map reflects the ratings data collected (1.0 means perfectly correlated). Stress is a measure of how well the map captures the brand relationships in two dimensions. Lower is better. Under.20 is good, is acceptable, over.40 means that the map is struggling to capture the relationships in 2 dimensions. With this additional information, what can we say about the map?
M ULTI - DIMENSIONAL S CALING (MDS) 19 Multi-Dimensional Scaling (MDS) MBTN | Management by the Numbers 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 r 2 is fairly high on these two dimensions (and the stress is low), these relationships are fairly accurate. 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.)
F URTHER R EFERENCE 20 Further Reference MBTN | Management by the Numbers Dolan, Robert J. "Perceptual Mapping: A Manager's Guide." Harvard Business School Background Note , July Michael Deighan, Stuart W. James, and Thomas C. Kinnear, StratSimMarketing: The Marketing Strategy Simulation, Interpretive Software, 2011 Sawtooth Technologies