1. Statistical concept generation techniques
Product and Innovation Management
Topic 6

2. What are these techniques?
1. Determinant gap map
2. Overall similarity (OS) perceptual gap map
3. Attribute ratings (AR) conjoint analysis

3. Determinant gap map Identify the two most determining attributes
The two attributes that are most important and (2)differentiating
Managerial expertise and judgement is used to plot products on a map
Speedy and cost efficient
Discrepancy between managerial perception and customer perception?
Eg： Candy bar/ breakfast cereal 事实上并不健康

4. Determinant gap map – Carbonated drinks example
0.8
Dr Pepper
Tango
0.4
Coke
Pepsi
7-Up
0.0
Diet Tango
不同 左边都是可乐喜好的。右边。。
下面都是
Horizontal-cola flavour
vertical- dietness
Diet Pepsi
-0.4
Diet 7-Up
Diet Coke
-0.8
-1.5
0.0
1.5

5. Overall similarity perceptual gap map
Respondent customers are asked to judge how similar or dissimilar different major products in the same market are, using their own criteria
Likert scale questions are used
Use multidimensional scaling technique to draw the map

6. Overall similarity perceptual gap map – Beer example
2.0
Becks
1.0
Corona
Budweiser
Budvar
Carsberg
Grolsch
0.0
Harp
San Miguel
Horizontal-Initial taste and strength
Vertical-After taste and strength
-1.0
Holsten
Stella Artois
-2.0
-2.0
-1.0
0.0
1.0
2.0

7. Likert-scale questions for overall similarity perceptual gap map
Very dissimilar
Very similar
Becks v Budweiser 1 2 3 4 5 6 7
Budweiser v Carlsberg
Carlsberg v Corona ……
Becks v Stella Artois

8. Example of data output for one respondent customer
Becks
Budvar
Budweiser
Carlsberg
Corona
Grolsch
Harp
Holsten
San Miguel
Stella Artois 5 6 7 4 2 3 1

9. Attribute ratings conjoint analysis
A product to be developed is represented by a bundle of attributes. ppt22
All possible combinations of all of the determinant attributes
Visualization of each possible combination through a card or a static picture of the prototype
Identification of which combinations would be most preferred by customers
Conjoint analysis
Attributes （characteristics）
3 attributes
Colour - red,black,blue
I have 3 levels of this colour attribute

10. How to decide on the attributes for conjoint analysis?
1. Features
2. Functions
3. Benefits
New toothpaste

11. An introductory example – let’s generate a new coffee product concept (1st attribute)
Utility value

12. An introductory example – let’s generate a new coffee product concept (2nd attribute)
Utility value

13. An introductory example – let’s generate a new coffee product concept (3rd attribute)
Utility value

14. Detailed work example – prepared salsa
Three determinant attributes: Spiciness, Thickness, Colour
Spiciness: Mild, Medium-hot, Extra-hot 3
Thickness: Regular, Thick, Extra-thick 3
Colour: Green, Red 2
How many different types of salsa that can be considered?
3x3x2=18
3x3x2=18
Conjoint analysis
Attributes （characteristics）
3 attributes
Colour - red,black,blue
I have 3 levels of this colour attribute

15. Preference ranking by one of the respondents
Spiciness
Thickness
Colour
Actual ranking
Predicted ranking
Mild
Regular
Red 4
Green 3
Medium-hot 10 6 8
Extra-hot 15 16
Thick 2 1 5 13 11
Extra-thick 7 9 14 12 17 18
用100个人去得到 最后17这个result
Regression analysis
Y=a+bx

16. Analyse preference ranking data
Use ‘monotone analysis of variance’ to identify and quantify common patterns within the rank order data
Estimate the utilities of each level of each attribute for each respondent
-> (1) Can tell which levels of the determinant attributes are preferred
-> (2) Can tell which attributes are treated as more important than others 1.

17. Which levels of the spiciness attribute are preferred?
-1.774
0.105
=3.441
1.667

18. Which levels of the thickness attribute are preferred?
-1.074
0.913
=1.987
0.161

19. Which levels of the colour attribute are preferred?
0.161
-0.161
Colour – =0.322
=5.75

20. Which attributes are treated as more important than others?
59.8%
34.6%
Spiceness=3.441/5.75
Thickness=1.987/5.75
Colour=0.322/5.75
5.6%

21. Two issues on the applicability of attributes rating conjoint analysis
1. How well does the attribute ratings conjoint analysis model incorporating utility scores predict actual customer preference patterns?
2. What if we have many more attributes and/or levels?
把备注的全部乘起来。 432 combinations respondent
Prepared salsa
Spiceness – 3
Thickness – 3
Colour – 2
Type of container – glass VS plastic – 2
Size of container – family VS individual – 2
Type of ingredients – organic VS non-organic 2
Brand name – 3 different brand name. 3

22. Four hurdles to be crossed for effective conjoint analysis
1. Can the product be specified as a bundle of attributes?
2. How can our company identify the determinant attributes of the product?
3. Are the respondents familiar with the product and its determinant attributes?
4. Can our company act on the conjoint analysis results?
2.How to identify the 3 to 4 attributes out of 10 potential attributes?
Determinant contribute
Important
Differentiating
3. New to the world products
Radical VS incremental innovations 4.

23. Application of virtual prototypes in conjoint analysis
Enabled by improvements in virtual reality computer and video technology
Respondents are brought into a virtual buying environment that simulates the information typically available in a realistic buying environment
By using surrogate travel technology, respondents ‘walk around’ a dealer showroom and look at virtual, computer-generated prototypes
By using video monitor and laser videodisk player, respondents see advertisements, read relevant information, hear statements from salespeople, and word-of-mouth comments from customers

24. Question time!
1. What are the key differences among the three statistical concept generation techniques taught today?
What are the main/major differences
2. What is the major limitation of the three techniques?
Statistical techniques (topic 6)– incremental innovations
Non-statistical techniques (topic 5) – both redical and incremental innovations
3. Which of the three techniques is better?
Conjoint analysis
A gap in the map doesn’t ..

25. Q1
criteria
data
Determinant gap map
managers
Cheaper/fast/High risk
Overall similarity perceptual gap map (ppt6)
consumers
Conjoint analysis
Most detail
specific