NEW PRODUCT MANAGEMENT
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CHAP1106.pptChapter 6
Analytical Attribute Approaches: Introduction and Perceptual Mapping
What are Analytical Attribute Techniques?
- Basic idea: products are made up of attributes -- a future product change must involve one or more of these attributes.
- Three types of attributes: features, functions, benefits.
- Theoretical sequence: feature permits a function which provides a benefit.
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Gap Analysis
- Determinant gap map (produced from managerial input/judgment on products)
- AR perceptual gap map (based on attribute ratings by customers)
- OS perceptual map (based on overall similarities ratings by customers)
*
A Determinant Gap Map
Figure 6.2
1 2 3 .... Options .... X Ideal
1
2
.
.
.
.
.
.
.
15
Attributes
Respondents
1
2
.
.
700
.
A Data Cube
Figure 6.3
Rate each brand you are familiar with on each of the following: Disagree Agree
1. Attractive design 1..2..3..4..5
2. Stylish 1..2..3..4..5
3. Comfortable to wear 1..2..3..4..5
4. Fashionable 1..2..3..4..5
5. I feel good when I wear it 1..2..3..4..5
6. Is ideal for swimming 1..2..3..4..5
7. Looks like a designer label 1..2..3..4..5
8. Easy to swim in 1..2..3..4..5
9. In style 1..2..3..4..5
10. Great appearance 1..2..3..4..5
11. Comfortable to swim in 1..2..3..4..5
12. This is a desirable label 1..2..3..4..5
13. Gives me the look I like 1..2..3..4..5
14. I like the colors it comes in 1..2..3..4..5
15. Is functional for swimming 1..2..3..4..5
Obtaining Customer Perceptions
Figure 6.4
Snake Plot of Perceptions
(Three Brands)
Aqualine
Islands
Sunflare
Attributes
Ratings
Figure 6.5
*
Figure 6-5
Snake Plot of Brand Ratings
Data Reduction Using Multivariate Analysis
- Factor Analysis
- Reduces the original number of attributes to a smaller number of factors, each containing a set of attributes that “hang together”
- Cluster Analysis
- Reduces the original number of respondents to a smaller number of clusters based on their benefits sought, as revealed by their “ideal brand”
*
No. of Factors
Percent Variance
Explained
The Scree
Selecting the Number
of Factors
Figure 6.6
Factor
Eigenvalue
Percent Variance Explained
1
6.04
40.3
2
3.34
22.3
3
0.88
5.9
4
0.74
4.9
5
0.62
4.2
6
0.54
3.6
7
0.52
3.5
8
0.44
3.0
9
0.40
2.7
Factor Loading Matrix
Figure 6.7
Attribute
Factor 1 -- “Fashion”
Factor 2 -- “Comfort”
1. Attractive design
.796
.061
2. Stylish
.791
.029
3. Comfortable to wear
.108
.782
4. Fashionable
.803
.077
5. I feel good when I wear it
.039
.729
6. Is ideal for swimming
.102
.833
7. Looks like a designer label
.754
.059
8. Easy to swim in
.093
.793
9. In style
.762
.123
10. Great appearance
.758
.208
11. Comfortable to swim in
.043
.756
12. This is a desirable label
.807
.082
13. Gives me the look I like
.810
.055
14. I like the colors it comes in
.800
.061
15. Is functional for swimming
.106
.798
Sample calculation of factor scores: From the snake plot, the mean ratings of Aqualine on Attributes
1 through 15 are 2.15, 2.40, 3.48, …, 3.77. Multiply each of these mean ratings by the corresponding
coefficient in the factor score coefficient matrix to get Aqualine’s factor scores. For example, on
Factor 1, Aqualine’s score is (2.15 x 0.145) + (2.40 x 0.146) + (3.48 x -0.018) + … + (3.77 x -0.019)
= 2.48. Similarly, its score on Factor 2 can be calculated as 4.36. All other brands’ factor scores are
calculated the same way.
Factor Scores Matrix
Figure 6.8
Attribute
Factor 1 -- “Fashion”
Factor 2 -- “Comfort”
1. Attractive design
0.145
-0.022
2. Stylish
0.146
-0.030
3. Comfortable to wear
-0.018
0.213
4. Fashionable
0.146
-0.017
5. I feel good when I wear it
-0.028
0.201
6. Is ideal for swimming
-0.021
0.227
7. Looks like a designer label
0.138
-0.020
8. Easy to swim in
0.131
0.216
9. In style
-0.021
-0.003
10. Great appearance
0.146
0.021
11. Comfortable to swim in
-0.029
0.208
12. This is a desirable label
0.146
-0.016
13. Gives me the look I like
0.148
-0.024
14. I like the colors it comes in
0.146
-0.022
15. Is functional for swimming
-0.019
0.217
The AR Perceptual Map
Figure 6.9
Aqualine
Islands
Splash
Molokai
Sunflare
Gap 1
Gap 2
Fashion
Comfort
Dissimilarity Matrix
Figure 6.10
Aqualine
Islands
Sunflare
Molokai
Splash
Aqualine
X
3
9
5
7
Islands
X
8
3
4
Sunflare
X
5
7
Molokai
X
6
Splash
X
The OS Perceptual Map
Figure 6.11
Aqualine
Islands
Splash
Molokai
Sunflare
Comfort
Fashion
Source: Adapted from Robert J. Dolan, Managing the New Product Development Process: Cases and Notes
(Reading, MA: Addison-Wesley, 1993), p. 102.
Comparing AR and OS Methods
Figure 6.12
|
AR Methods |
OS Methods |
|
Input Required |
|
|
Brand ratings on specific attributes |
Overall similarity ratings |
|
Attributes must be pre-specified |
Respondent uses own judgment of similarity |
|
Analytic Procedures Commonly Used |
|
|
Factor analysis; multiple discriminant analysis |
Multidimensional scaling (MDS) |
|
Graphical Output |
|
|
Shows product positions on axes Axes interpretable as underlying dimensions (factors) |
Shows product positions relative to each other Axes obtained through follow-up analysis or must be interpreted by the researcher |
|
Where Used |
|
|
Situations where attributes are easily articulated or visualized |
Situations where it may be difficult for the respondent to articulate or visualize attributes |
Failures of Gap Analysis
- Input comes from questions on how brands differ (nuances ignored)
- Brands considered as sets of attributes; totalities, interrelationships overlooked; also creations requiring a conceptual leap
- Analysis and mapping may be history by the time data are gathered and analyzed
- Acceptance of findings by persons turned off by mathematical calculations?
*
1
1.5
2
2.5
3
3.5
4
4.5
5
123456789101112131415
Factor
Eigenvalue
Percent Variance
Explained
1
6.04
40.3
2
3.34
22.3
3
0.88
5.9
4
0.74
4.9
5
0.62
4.2
6
0.54
3.6
7
0.52
3.5
8
0.44
3.0
9
0.40
2.7
0
5
10
15
20
25
30
35
40
45
123456789
Attribute
Factor 1 --
“Fashion”
Factor 2 --
“Comfort”
1. Attractive design
.796
.061
2. Stylish
.791
.029
3. Comfortable to wear
.108
.782
4. Fashionable
.803
.077
5. I feel good when I wear it
.039
.729
6. Is ideal for swimming
.102
.833
7. Looks like a designer label
.754
.059
8. Easy to swim in
.093
.793
9. In style
.762
.123
10. Great appearance
.758
.208
11. Comfortable to swim in
.043
.756
12. This is a desirable label
.807
.082
13. Gives me the look I like
.810
.055
14. I like the colors it comes in
.800
.061
15. Is functional for swimming
.106
.798
Attribute
Factor 1 --
“Fashion”
Factor 2 --
“Comfort”
1. Attractive design
0.145
-0.022
2. Stylish
0.146
-0.030
3. Comfortable to wear
-0.018
0.213
4. Fashionable
0.146
-0.017
5. I feel good when I wear it
-0.028
0.201
6. Is ideal for swimming
-0.021
0.227
7. Looks like a designer label
0.138
-0.020
8. Easy to swim in
0.131
0.216
9. In style
-0.021
-0.003
10. Great appearance
0.146
0.021
11. Comfortable to swim in
-0.029
0.208
12. This is a desirable label
0.146
-0.016
13. Gives me the look I like
0.148
-0.024
14. I like the colors it comes in
0.146
-0.022
15. Is functional for swimming
-0.019
0.217
Aqualine
Islands
Sunflare
Molokai
Splash
Aqualine
X
3
9
5
7
Islands
X
8
3
4
Sunflare
X
5
7
Molokai
X
6
Splash
X
AR Methods OS Methods
Input Required
Brand ratings on specific attributes Overall similarity ratings
Attributes must be pre-specified Respondent uses own judgment of similarity
Analytic Procedures Commonly Used
Factor analysis; multiple discriminant a nalysis Multidimensional scaling (MDS)
Graphical Output
Shows product positions on axes
Axes interpretable as underlying dimensions
(factors)
Shows product positions relative to each other
Axes obtained through follow -up analysis or
must be interpreted by the researcher
Where Used
Situations where attributes are easily
articulated or visualized
Situations where it may be difficult for the
respondent to articulate or visualize attributes
