Sensometrics 2010 Repères

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Sensometrics 2010 Repères

  1. 1. Bayesian Networks A complete framework to understand consumer perceptions and their links with external data Fabien CRAIGNOU, Repères Carole JEGOU, Kraft R&D
  2. 2. Context & Objectives 2  PRODUCT TESTING in France.  20 market products tested in the confectionary sector  Sequential monadic procedure  N= 200 respondents  Overall liking, JAR questions  SENSORY PANEL  40 significant sensory attributes  Covering aroma, texture, flavour, aftertaste and after sensation.  ANALYTICAL MEASURES  20 key variables  GAIN UNDERSTANDING OF CONSUMER PERCEPTIONS AND DRIVERS OF LIKING, AND PROVIDE THE R&D WITH GUIDELINES FOR PRODUCT DEVELOPMENT. Sensometrics meeting – Rotterdam – July 2010
  3. 3. Data processing workflow: 3-step analysis 3 STEP 1 : UNDERSTANDING STEP 2 : SIMPLIFYING CONSUMER PERCEPTIONS SENSORY & TECHNICAL INFORMATION « internal » model consumer attributes Identification of main (JAR scales) dimensions STEP 3 : INFLUENCE OF TECHNICAL DIMENSIONS ON CONSUMER PERCEPTIONS « external » model sensory attributes, analytical data Sensometrics meeting – Rotterdam – July 2010
  4. 4. Data processing workflow: 3-step analysis 4 STEP 1 : UNDERSTANDING CONSUMER PERCEPTIONS « internal » model consumer attributes (JAR scales) Sensometrics meeting – Rotterdam – July 2010
  5. 5. Understanding consumer perceptions STEP 1 5 AUTOMATIC LEARNING - discovering STRUCTURE and PARAMETERS Colour JAR 41% Mutual information Taste of X JAR 36% OVERALL Texture 1 JAR LIKING 46% 27% 25% 42% 49% 54% Texture 2 JAR 32% Consistency JAR 14% Sweetness JAR Aftertaste JAR HEURISTIC SEARCH ALGORITHM TO TEST DIFFERENT STRUCTURES QUALITY OF THE POSSIBLE NETWORKS IS ASSESSED BY A SCORE TAKING INTO ACCOUNT The fit of the model to the data The complexity of the structure CROSS VALIDATION IS USED TO ENSURE ROBUSTNESS Sensometrics meeting – Rotterdam – July 2010
  6. 6. Understanding consumer perceptions STEP 1 6 OUTPUT – relative weights in overall liking Sweetness 24% Relative Weights in overall Liking (Mutual information Aftertaste 22% normalized to sum 100%) Texture 20% Colour 18% Taste of X 16% ALL DRIVERS ARE VERY CLOSE IN TERMS OF IMPACT ON TASTE LIKING (contrary to other markets where 1 or 2 drivers prevail). OVERALL LIKING requires good performances on…  … TASTE dimensions (aftertaste, sweetness, taste of…)  … TEXTURE perception  …COLOUR perception Sensometrics meeting – Rotterdam – July 2010
  7. 7. Understanding consumer perceptions STEP 1 7 OUTPUT – detailed impact of intensity balance Impact of sweetness balance Impact texture balance Probability that Liking >= 8 Probability that liking >= 8 80% 80% 60% 57% 60% 40% 40% 40% too light too light 20% sweetness 20% meltiness 11% JAR 5% JAR 5% Intensity 2% Intensity 0% too strong 0% too strong Impact of Aftertaste balance Impact of colour intensity balance Probability that Liking >= 8 Probability that Liking >= 8 80% 80% 60% 60% 43% 43% 40% 40% too light too light 20% aftertaste 20% 13% colour 4% JAR JAR 1% Intensity 3% balance 0% too strong 0% too dark Sensometrics meeting – Rotterdam – July 2010
  8. 8. Data processing workflow: 3-step analysis 8 STEP 1 : UNDERSTANDING STEP 2 : SIMPLIFYING CONSUMER PERCEPTIONS SENSORY & TECHNICAL INFORMATION « internal » model consumer attributes Identification of main (JAR scales) dimensions Sensometrics meeting – Rotterdam – July 2010
  9. 9. Simplifying sensory/analytical information STEP 2 9 Handling sensory and analytical variables Sensory and Analytical variables have been discretized into 3 levels using a K-Means procedure, in order to adapt each discretization to the distribution of the variable. CHECKING CORRESPONDENCE WITH GROUPINGS BASED ON THE SENSORY PANEL Falvour Attribute 1 Sensory Score Flavour Attribute 1 Product 1 29.55 a Product 2 26.75 a Product 3 25.94 ab Product 4 25.75 ab Product 5 22.38 b c Product 6 21.98 b c d Product 7 21.73 b c d Product 8 21.36 c d Product 9 21.33 c d Product 10 21.11 c d Product 11 21.02 c d Product 12 20.31 c d Product 13 19.91 c d Product 14 19.20 c d e Product 15 19.15 c d e Product 16 18.58 c d e Product 17 18.57 c d e Product 18 17.95 c d e Product 19 17.25 d e Product 20 14.94 e f Number of observations Sensometrics meeting – Rotterdam – July 2010
  10. 10. Simplifying sensory/analytical information STEP 2 10 Identifying main dimensions Unsupervised learning Hierarchical Clustering based on KL divergence Aroma 1 Aroma 1 IMPORTANCE OF CROSS-VALIDATION Mouthfeel 3 Mouthfeel 3 Descriptor 8 Descriptor 8 Descriptor 11 Descriptor 11 Descriptor 9 Descriptor 12 Descriptor 9 Descriptor 12 Descriptor 13 Descriptor 13 Mouthfeel 1 Mouthfeel 1 Descriptor 10 Descriptor 15 Descriptor 15 Descriptor 10 Descriptor 14 Descriptor 14 Descriptor 6 Descriptor 7 Descriptor 6 Descriptor 7 Flavour 4 Flavour 4 Descriptor 16 Descriptor 16 Descriptor 5 Flavour 3 Flavour 3 Descriptor 5 Descriptor 18 Descriptor 18 Descriptor 33 Descriptor 34 Descriptor 33 Descriptor 34 Descriptor 17 Descriptor 19 Descriptor 17 Descriptor 19 Descriptor 37 Descriptor 35 Descriptor 4 Descriptor 37 Descriptor 35 Descriptor 4 Descriptor 36 Descriptor 36 Descriptor 3 Descriptor 3 Descriptor 2 Descriptor 29 Descriptor 1 Descriptor 2 Descriptor 29 Descriptor 36 Descriptor 1 Descriptor 20 Descriptor 36 Descriptor 30 Descriptor 20 Descriptor 30 Mouthfeel 2 Mouthfeel 2 Descriptor 31 Descriptor 21 Descriptor 26 Descriptor 31 Descriptor 21 Descriptor 26 Descriptor 32 Descriptor 23 Descriptor 22 Descriptor 27 Descriptor 32 Descriptor 23 Descriptor 22 Descriptor 27 Descriptor 28 Flavour 2 Flavour 2 Descriptor 28 After sensation 1 Descriptor 24 After sensation 1 Descriptor 24 Aroma 2 Aroma 2 Descriptor 25 Descriptor 25 Flavour 1 Flavour 1 Sensometrics meeting – Rotterdam – July 2010
  11. 11. Data processing workflow: 3-step analysis 11 STEP 1 : UNDERSTANDING STEP 2 : SIMPLIFYING CONSUMER PERCEPTIONS SENSORY & TECHNICAL INFORMATION « internal » model consumer attributes Identification of main (JAR scales) dimensions STEP 3 : INFLUENCE OF TECHNICAL DIMENSIONS ON CONSUMER PERCEPTIONS « external » model sensory attributes, analytical data Sensometrics meeting – Rotterdam – July 2010
  12. 12. Sensory expectations of consumers STEP 3 12 Key issues of the modeling workflow Consumer attributes Sensory Dimensions In order to let the search algorithm c1 s1 Consumer 1 cn sk focus on the links between Consumer Data and Sensory Data… Product 1 Constant for 1 product Consumer 200 FIXING the arcs between consumer dimensions (already discovered) FORBID the arcs between sensory dimensions (links are too obvious: for each product => 200 times the same sensory variables) Consumer 1 Product 20 Consumer 200 Sensometrics meeting – Rotterdam – July 2010
  13. 13. Sensory expectations of consumers STEP 3 13 Structural model Colour JAR Taste of X JAR OVERALL LIKING Texture 1 JAR Flavour 4 Flavour 4 Texture 2 JAR Consistency JAR Aftertaste JAR Sweetness JAR Flavour 2 Flavour 3 Flavour 3 Flavour 2 Mouthfeel 3 Mouthfeel 3 Mouthfeel 1 Mouthfeel 1 After After sensation 1 sensation 1 Aroma 2 Mouthfeel 2 Mouthfeel 2 Aroma 2 Flavour 1 Flavour 1 Aroma 1 Aroma 1 Sensometrics meeting – Rotterdam – July 2010
  14. 14. Sensory expectations of consumers STEP 3 14 OUTPUT: importance of sensory dimensions on SWEETNESS perception * Flavour 3 Bitterness Mutual Information : non-linear measure of impact. * After sensation 1 Astringent aftersensation Total sample Cluster 1 Cluster 2 * Flavour 2 Sourness Aroma 2 Cocao aroma Flavour 1 Fruity Flavour 4 Chocolate flavour Aroma 1 Sweet aroma % Mutual 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 information Sensometrics meeting – Rotterdam – July 2010
  15. 15. Sensory expectations of consumers STEP 3 15 OUTPUT: expected sensory levels (top-6 descriptors) Probability that sweetness is perceived as JAR by consumers FLAVOUR 3 - descriptor 1 FLAVOUR 3 - descriptor 5 FLAVOUR 3 - descriptor 7 70% 70% 70% 60% 60% 60% 62% 58% 62% 60% 56% 56% 50% 48% 50% 48% 50% 48% 40% 40% 40% 30% 30% 30% 20% 20% 20% 11 - 18 18 - 23 23 - 28 12 - 19 19 - 22 22 - 29 8 - 10 10 - 14 > 20 AFTERSENSATION 1 FLAVOUR 2 -descriptor 2 AFTERSENSATION 1 descriptor 8 descriptor 12 70% 70% 70% 60% 63% 60% 60% 59% 60% 59% 60% 59% 50% 50% 50% 47% 46% 47% 40% 40% 40% 30% 30% 30% 20% 20% 20% 17 - 22 22 - 24 24 - 26 6 - 10 10 - 12 12 - 18 7 - 13 13 - 17 17 - 23 Significant positive impact Significant negative impact Sensometrics meeting – Rotterdam – July 2010
  16. 16. Sensory expectations of consumers STEP 3 16 Preference Clusters OUTPUT: expected sensory levels (top-6 descriptors) Cluster 1 Cluster 2 Following 2 of the preference segments identified by KRAFT FLAVOUR 3 - descriptor 1 FLAVOUR 3 - descriptor 5 FLAVOUR 3 - descriptor 7 70% 70% 70% 63% 63% 63% 65% 64% 65% 60% 60% 60% 58% 58% 61% 61% 58% 53% 57% 50% 50% 50% 48% 48% 40% 40% 40% 38% 38% 38% 30% 30% 30% 20% 20% 20% 11 - 18 18 - 23 23 - 28 12 - 19 19 - 22 22 - 29 8 - 10 10 - 14 > 20 AFTERSENSATION 1 FLAVOUR 2 -descriptor 2 AFTERSENSATION 1 descriptor 8 descriptor 12 70% 70% 70% 64% 63% 63% 65% 65% 60% 60% 60% 60% 62% 58% 59% 53% 58% 53% 50% 54% 50% 51% 50% 54% 40% 40% 40% 41% 41% 41% 30% 30% 30% 20% 20% 20% 17 - 22 22 - 24 24 - 26 6 - 10 10 - 12 12 - 18 7 - 13 13 - 17 17 - 23 Significant positive impact Significant negative impact Sensometrics meeting – Rotterdam – July 2010
  17. 17. Sensory expectations of consumers STEP 3 17 OUTPUT: Summary of expected sensory levels Main sample Cluster 1 KEY DESCRIPTORS Cluster 2 Descriptor 1 Descriptor 1 Descriptor 2 Descriptor 2 Monitoring… Descriptor 3 Descriptor 3 SWEETNESS AFTERTASTE Descriptor 4 Descriptor 4 TASTE OF X Descriptor 5 Descriptor 5 Descriptor 6 Descriptor 6 Descriptor 7 Descriptor 7 MOUTHFEEL Descriptor 8 Descriptor 8 Sensometrics meeting – Rotterdam – July 2010
  18. 18. SUMMARY – ADDED VALUE / LIMITS on the methodology 18  STRUCURAL LEARNING possible to discover the links between variables enables to have a good overview of all consumer perceptions simultaneously  NON-LINEAR RELATIONS very important when dealing with links between consumer & sensory  USE OF CROSS-VALIDATION to enhance confidence into the models  Discretizing sensory/analytical variables: tough (not natural?) job, need to check correspondence with sensory panel significant differences.  Non-linear relations: exercise caution, as sometimes weird relations can be discovered (U-shape like relation for example) => needs to be cleaned.  Causality ? Sensometrics meeting – Rotterdam – July 2010
  19. 19. PLS PATH MODELLING – BAYESIAN NETWORKS 19 SOME IMPORTANT DIFFERENCES TO REMEMBER BAYESIAN PLS PATH MODELLING Discover the structure Impose the structure Latent variables: entirely Latent variables dependent on the constructed to explain explanatory variables TARGET Many observations Few observations Sensometrics meeting – Rotterdam – July 2010

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