Key
Features
and Results
Benefits
XLSTAT-MX functions
Preference Mapping (PREFMAP)

Build decision making maps to:
• Improve or develop products
• Position products in comparison with competitors’
products
• Reach a target market

Preference mapping = a powerful tool to
optimize product acceptability.

XLSTAT-MX offers several regression
models to project complementary data on
the objects maps:
• Vector model,
• Circular ideal point model,
• Elliptical ideal point model,
• Quadratic ideal point model.
Preference Mapping (PREFMAP)



10 commercial samples of potato chips
99 consumers  satisfaction from 1 to
30
Consumers are segmented into 9
clusters
Preference Mapping (PREFMAP)
Generalized Procrustes Analysis (GPA)

GPA is pretreatment used to reduce the
scale effects and to obtain a consensual
configuration.
Generalized Procrustes Analysis (GPA)

GPA compares the proximity between
the terms that are used by different
experts to describe products.
Multiple Factor Analysis (MFA)

MFA is a generalization of PCA
(Principal Component Analysis) and
MCA (Multiple Correspondence
Analysis).

MFA makes it possible to:
• Analyze several tables of variables
simultaneously,
• Obtain results that allow studying the
relationship between the observations,
the variables and tables.
Multiple Factor Analysis (MFA)

36 experts have graded 21 wines
analysed on several criteria:
• Olfactory (5 variables)
• Visual (3 variables)
• Taste (9 variables)
• Quality (2 variables)
Multiple Factor Analysis (MFA)

MFA groups the information on one chart
Multiple Factor Analysis (MFA)

MFA groups the information on one chart
Multiple Factor Analysis (MFA)

Wine 13 is in the direction
of the two quality variables
and is therefore the wine of
preference.
Multiple Factor Analysis (MFA)

The olfactory criteria are
often increasing the
distance between the
wines.
Penalty analysis

Identify potential directions for the
improvement of products, on the basis of
surveys performed on consumers or
experts.

Two types of data are used:
• Preference data (or liking scores) for a
product or for a characteristic of a product
• Data collected on a JAR (Just About
Right) scale
Penalty analysis
A type of potato chips is evaluated:
 By 150 consumers
 On a JAR scale (1 to 5) for 4 attributes:
• Saltiness,
• Sweetness,
• Acidity,
• Crunchiness.
 And on an overall liking (1 to 10) score
scale
Penalty analysis
Mean of Liking for JAR – Mean of Liking for too little
and too much
Semantic differential charts

The semantic differential method is a
visualization method to plot the
differences between individuals'
connotations for a given word.

This method can be used for:
• Analyzing experts’ agreement on the
perceptions of a product described by a
series of criteria on similar scales
• Analyzing customer satisfaction surveys
and segmentation
• Profiling products
Semantic differential charts

1 yoghurt

5 experts

6 attributes:
• Color
• Fruitiness
• Sweetness
• Unctuousness
• Taste
• Smell
Semantic differential charts
TURF analysis

TURF = Total Unduplicated Reach and
Frequency method

Highlight a line of products from a
complete range of products in order to
have the highest market share.

XLSTAT offers three algorithms to find
the best combination of products
TURF analysis


27 possible dishes
185 customers

"Would you buy this
product?" (1: No, not
at all to 5: Yes, quite
sure).

The goal is to obtain
a product line of 5
dishes maximizing
the reach
TURF analysis
Product characterization

Find which descriptors are discriminating
well a set of products and which the most
important characteristics of each product
are.

Check the influence on the scores of
attributes of:
• Session
• Product
• Judge

• Judge*Product
All computations are based on the
analysis of variance (ANOVA) model.
Product characterization

29 assessors

6 chocolate drinks

14 characteristics:
• Cocoa and milk taste and flavor
• Other flavors: Vanilla, Caramel
• Tastes: bitterness, astringency,
acidity, sweetness
• Texture: granular, crunchy, sticky,
melting
Product characterization
DOE for sensory data analysis

Designing an experiment is a
fundamental step to ensure that the
collected data will be statistically usable
in the best possible way.
DOE for sensory data analysis

Prepare a sensory evaluation where
judges (experts and/or consumers)
evaluate a set of products taking into
account:
• Number of judges to involve
• Maximum number of products that a judge
can evaluate during each session
• Which products will be evaluated by each of
the consumers in each session, and in what
order (carry-over)


Complete plans or incomplete block
designs, balanced or not.
Search optimal designs with A- or Defficiency
DOE for sensory data analysis



60 judges
8 products
Saturation: 3 products / judge
DOE for sensory data analysis
DOE for sensory data analysis
Let XLSTAT-MX be part of your product development
strategy.
info@xlstat.com