如何快速掌握各种市场研究方法

FIG. 2. SCHEME FOR BOOTSTRAPPING PENALTY ANALYSIS WITH
Collinearities between variables are being ignored. Does not allow the application of preference mapping
concepts since it is not a regression method
Penalty
2
1.8
Too Sour
1.6
Too Little
1.4
Flavor
1.2 1
0.8
Too little heat
0.6
0.4
0.2
0
0.2
0.25
Too Little Herb
0.3 % of Consumers
Too Little Garlic
0.35
0.4
S
Penalty
Penalty Analysis
Bootstrap Penalty Analysis
Alternatives to distributions and penalty analysis
Jean-Francois Meullenet and Rui Xiong
Introduction
Hedonic and JAR (just-about-right) scales are widely used together to provide directional information for product reformulation or optimization
Like extremely
Hedonic scale
JAR scale
Like very much
Much tom Much
Like moderately
Too much
Like slightly
Just about right
Neither like nor dislike
Too little
Dislike slightly
S
Penalty Analysis
The major limitations of penalty analysis are:
The fact that categories below and above JAR level are collapsed (i.e. because n is often not large enough within a single category)
S importance to determine which attributes should be
modified.
Objective
Apply bootstrap (and Jacknife) resampling to penalty calculations to allow statistical testing (h0: YjarYTLorTM=0) of the penalties or mean drops.
Diagnostic results
100% 80% 60% 40% 20% 0%
Much Too Strong Too Strong JAR Too Weak Much Too Weak
S
Introduction
Simple graphical method for assessing the cost associated with having an attribute not at its optimum level
50
Much Too
Weak
40
Too Weak
30
JAR
20 Too Strong
10
0
Much Too Strong
Liking score for this group, Y<
Liking score for this group, Yjar
Liking score for this group, Y>
2
1.8
Too Sour
1.6
Too Little
1.4
Flavor
1.2 1
Too little
0.8
heatቤተ መጻሕፍቲ ባይዱ
0.6
0.4
0.2 0
0.2
0.25
Too Little Herb
0.3 % of Consumers
Too Little Garlic
0.35
0.4
Are penalties significantly different from 0? This is of
Much too little
Dislike moderately
Dislike very much Dislike extremely
S
Introduction
Results from diagnostic attributes are not always actionable
What is the percentage of consumers required on the too little or too much side to consider an attribute to be at a inappropriate level?
If an attribute is not at its optimal level, does that have an impact on product liking?
S
Percent Responses
color chocolate
nutty banana sweetness firmness
Consumers are split is 3 groups (TL, JAR, TM)
Penalties not calculated if proportion of consumers is less than 20%
Penalty=Yjar-Y<or>
Frequency
Banana
A graphical technique, understandable to managers
Ignoring correlations among attributes
Not a regression method
Liking
Sensory Level
S
Penalty Analysis