New Products Management - CHAPTER 6 ANALYTICAL ATTRIBUTE APPROACHES: INTRODUCTION AND PERCEPTUAL MAPPING potx

 Basic idea: products are made up of attributes -- a future product change must involve one or more of these attributes.. Gap Analysis Determinant gap map produced from managerial in

CHAPTER 6

ANALYTICAL ATTRIBUTE APPROACHES:

INTRODUCTION AND PERCEPTUAL

MAPPING

McGraw-Hill/Irwin Copyright ©2006 The McGraw-Hill Companies, Inc All right reserved

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

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

R es

po nd

en ts

R es

po nd

en ts

1 2

.

700

A Data Cube

Figure 6.3

Rate each brand you are familiar with on each of the

Disagree Agree

1 Attractive design 1 2 3 4 5

3 Comfortable to wear 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

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

1.5

2

2.5

3

3.5

4

4.5

5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Snake Plot of Perceptions

(Three Brands)

Aqualine

Islands

Sunflare

Attributes

Ratings

Figure 6.5

Data Reduction Using Multivariate Analysis

 Factor Analysis

smaller number of factors, each containing a set

of attributes that “hang together”

 Cluster Analysis

smaller number of clusters based on their benefits sought, as revealed by their “ideal brand”

Factor Eigenvalue Percent Variance

Explained

0 5 10

15

20

25

30

35

40

45

The Scree

Selecting the Number of Factors

Figure 6.6

Attribute Factor 1

“Fashion”

Factor 2

“Comfort”

Factor Loading Matrix

Figure 6.7

Attribute Factor 1

“Fashion”

Factor 2

“Comfort”

5 I feel good when I wear it -0.028 0.201

7 Looks like a designer label 0.138 -0.020

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

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

Aqualin e

Islands

Splash

Molokai

Sunflare Gap 1

Gap 2

Fashion

The AR Perceptual Map

Figure 6.9

Aqualine Islands Sunflare Molokai Splash

Dissimilarity Matrix

Figure 6.10

Islands

Splash

Molokai

Sunflare

C om fo rt

Fashion

The OS Perceptual Map

Figure 6.11

AR Methods OS Methods

Input Required

Analytic Procedures Commonly Used

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

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

Failures of Gap Analysis

(nuances ignored)

interrelationships overlooked; also creations

requiring a conceptual leap

data are gathered and analyzed

mathematical calculations?