1. Key Features and Results
Benefits

2. XLSTAT-MX functions

3. 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.

4. Preference Mapping (PREFMAP)
10 commercial samples of potato chips
99 consumers  satisfaction from 1 to 30
Consumers are segmented into 9 clusters

5. Preference Mapping (PREFMAP)

6. Generalized Procrustes Analysis (GPA)
GPA is pretreatment used to reduce the scale effects and to obtain a consensual configuration.

7. Generalized Procrustes Analysis (GPA)
GPA compares the proximity between the terms that are used by different experts to describe products.

8. 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.

9. 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)

10. Multiple Factor Analysis (MFA)
MFA groups the information on one chart

11. Multiple Factor Analysis (MFA)
MFA groups the information on one chart

12. Multiple Factor Analysis (MFA)
Wine 13 is in the direction of the two quality variables and is therefore the wine of preference.

13. Multiple Factor Analysis (MFA)
The olfactory criteria are often increasing the distance between the wines.

14. 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

15. 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

16. Mean of Liking for JAR – Mean of Liking for too little and too much
Penalty analysis
Mean of Liking for JAR – Mean of Liking for too little and too much

17. 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

18. Semantic differential charts
1 yoghurt
5 experts
6 attributes:
Color
Fruitiness
Sweetness
Unctuousness
Taste
Smell

19. Semantic differential charts

20. 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

21. 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

22. TURF analysis

23. 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:
Product
Judge
Session
Judge*Product
All computations are based on the analysis of variance (ANOVA) model.

24. 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

25. Product characterization

26. 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. 

27. 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 D-efficiency

28. DOE for sensory data analysis
60 judges
8 products
Saturation: 3 products / judge

29. DOE for sensory data analysis

30. DOE for sensory data analysis

31. Let XLSTAT-MX be part of your product development strategy.