Perceptual Mapping - PowerPoint Presentation

Slide1

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.Slide2

Perceptual Mapping2

Perceptual Mapping

MBTN | Management by the NumbersThere 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 InformationSlide3

Sample Perceptual Map (Attribute Rating Method)

3Sample 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 Rugged

Rugged

The Market for Sports Utility Vehicles (circa 2005)Slide4

I believe

[insert product name] is an excellent [insert product category].

StronglyAgreeAgreeNeither Agree Nor DisagreeDisagree

Strongly

Disagree

How is this Perceptual Map Created?

4

How is

this

Perceptual Map Created?

MBTN | Management by the Numbers

Strongly

Agree

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

productSlide5

How is this Perceptual Map Created?

5How is this Perceptual Map Created?

MBTN | Management by the NumbersNext, 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

MSlide6

How is this Perceptual Map Created?

6How is this Perceptual Map Created?

MBTN | Management by the NumbersThen, calculate the Avgerage Preference Rating score for each product based on the survey population (summarize 2nd question)Slide7

How is this Perceptual Map Created?

7How is this Perceptual Map Created?

MBTN | Management by the NumbersNext, 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 + β

1

Attrib1

in

+ β

2

Attrib2

in

+ …+

β

M

AttribM

in

+

ε

in

Overall Preference

= -2.7 +

1.25 *

Prestige

+

2.5 *

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.Slide8

Sample Perceptual Map (Attribute Rating Method)

8Sample Perceptual Map (Attribute Rating)

MBTN | Management by the NumbersSports 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 Rugged

Rugged

Insight

Now we also know that Prestigious / Not Prestigious and Rugged / Not Rugged had the highest absolute value coefficients in the regression.Slide9

Constructing an Ideal Vector

9Constructing the Ideal Vector

MBTN | Management by the NumbersCan 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

= -2.7 +

1.25 *

Prestige

+

2.5 *

Ruggedness

+…

YES!Slide10

Constructing an Ideal Vector10

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 Rugged

Rugged

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.Slide11

Multi-dimensional Scaling (MDS)

11Multi-Dimensional Scaling (MDS)

MBTN | Management by the NumbersVery 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

S

imilarity

M

ethod

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.Slide12

Multi-dimensional Scaling

12Multi-Dimensional Scaling

MBTN | Management by the NumbersMovie Similarity Matrix

About Schmidt

Lord of Rings

Gangs of NY

Maid in Manhattan

A Guy Thing

Bowling for Columbine

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

5.0

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.Slide13

Multi-dimensional Scaling

13Multi-Dimensional Scaling

MBTN | Management by the NumbersNotice 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.Slide14

Overall Similarity with Perceptual Attributes

14Overall Similarity with Perceptual Attributes

MBTN | Management by the NumbersOne 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

[ ]Slide15

Sample Perceptual Map (Attribute Rating Method)

15MDS 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 ValueSlide16

Multi-dimensional Scaling (MDS)

16Multi-Dimensional Scaling (MDS)

MBTN | Management by the NumbersAnother 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. Slide17

MDS Example with Vectors and Stress

17MDS Example with Vectors and Stress

MBTN | Management by the NumbersBefore 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.Slide18

MDS Example with Vectors and Stress

18MDS 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, .20 - .40 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?Slide19

Multi-dimensional Scaling (MDS)

19Multi-Dimensional Scaling (MDS)

MBTN | Management by the NumbersFirst 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.

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.)Slide20

Further

Reference20

Further ReferenceMBTN | Management by the NumbersDolan, Robert J. "Perceptual Mapping: A Manager's Guide." Harvard Business School Background Note 590-121, July 1990.Michael Deighan, Stuart W. James, and Thomas C. Kinnear, StratSimMarketing: The Marketing Strategy Simulation, Interpretive Software, 2011Sawtooth Technologies http://www.sawtooth.com