XLSTAT-MX functions

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XLSTAT-MX functions
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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.

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Preference Mapping (PREFMAP)

• 10 commercial samples of potato chips
• 99 consumers ? satisfaction from 1 to 30
• Consumers are segmented into 9 clusters

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Preference Mapping (PREFMAP)
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Generalized Procrustes Analysis (GPA)

• GPA is pretreatment used to reduce the scale
  effects and to obtain a consensual configuration.
  

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Generalized Procrustes Analysis (GPA)

• GPA compares the proximity between the terms that
  are used by different experts to describe
  products.

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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.

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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)

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Multiple Factor Analysis (MFA)

• MFA groups the information on one chart

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Multiple Factor Analysis (MFA)

• MFA groups the information on one chart

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Multiple Factor Analysis (MFA)

• Wine 13 is in the direction of the two quality
  variables and is therefore the wine of
  preference.

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Multiple Factor Analysis (MFA)

• The olfactory criteria are often increasing the
  distance between the wines.

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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

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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

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Penalty analysis
Mean of Liking for JAR Mean of Liking for too
little and too much
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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

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Semantic differential charts

• 1 yoghurt
• 5 experts
• 6 attributes
• Color
• Fruitiness
• Sweetness
• Unctuousness
• Taste
• Smell

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Semantic differential charts
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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

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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

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TURF analysis
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Product characterization

• Find which descriptors are discriminating well a
  set of products and which the most important
  characteristics of each product are.

• All computations are based on the analysis of
  variance (ANOVA) model.

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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

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Product characterization
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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. 

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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

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DOE for sensory data analysis

• 60 judges
• 8 products
• Saturation 3 products / judge

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DOE for sensory data analysis
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DOE for sensory data analysis
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Let XLSTAT-MX be part of your product development
strategy.
info_at_xlstat.com