Perceptual Mapping and Opportunity Identification. Dr. Chris Findlay Compusense Inc.
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1 Perceptual Mapping and Opportunity Identification Dr. Chris Findlay Compusense Inc.
2 What are we trying to accomplish?
3 Outline Sensory experience of consumers Descriptive Analysis What is a Perceptual Map? Some examples Finding holes in the map Opportunity identification A real world example Wieners and losers
4 Sensory Experience of Consumers Consumers judge food on Appearance Aroma Flavor Texture After Experience Remember there is no such thing as an Average Consumer.
5 DESCRIPTIVE ANALYSIS DEFINITION: any method to describe and quantify the sensory characteristics of stimuli by a panel of trained assessors. ASTM Standards 2002, E253 02
6 DESCRIPTIVE ANALYSIS PROCEDURE Identification of the key sensory attributes. Development of trained panel. Development of the ballot. Measurement of the attributes. Analysis and interpretations of results.
7 The Sensory Order Best practices in Descriptive Analysis Identify the attribute Rank its intensity Scale the intensity Feedback Calibration Assure that results are historically reproducible
8 A Perceptual Map Visualization of the sensory properties of a group of products HARDNESS 4 CHEWINESS Multidimensional presentation 2 CHOC CHIPS CHOCOLATE FLAVOR Simplification 3 Many different methods using factor analysis (PCA, GPA) 1 Sweetness
9 Overall Goals Learning how the products in a class are perceived with respect to strengths weaknesses and similarities Learning what potential buyers want Learning how to produce or modify a product to optimize its appeal Lawless & Heymann, 1998
10 Desired Qualities in Perceptual Mapping Goodness of fit (low stress) Reliability: blind duplicates plot together Similar pairs (batches) plot nearby Dimensionality: model has a few dimensions can be plotted Interpretation: Map should make sense Validity: map should relate to descriptive attributes Should relate to consumer preferences Payoff: Map should suggest new hypotheses or may confirm previous hypotheses Cost efficiency: data collection is rapid and simple Lawless & Heymann, 1998
11 A Study of Beef Striploins Are there sensory differences? By country of origin US, Canada, NZ and Australia By grade and age of animal 12 products, 12 panelist, 3 replicates 6 sensory attributes
12 Multi-Country Study of Beef striploin by animal age and grade 8 Tender 6 Juicy 4 Chewy 2 Moist 0 CND USA <14 >21-28 A SELECT NZ Australia >14-21 >28 AAA CHOICE Chew Time
13 by Country Juicy NZ Tender CND USA Moist Chew Time Chewy Australia
14 By Age <14 Juicy Tender >21-28 Chew Time Chewy Moist >14-21 >28
15 by Grade AAA Juicy Tender A SELECT Chew Time Moist Chewy CHOICE
16 Marinated Rainbow trout Improve yield and consumer acceptance How much marinade to use? Control, Atlantic and Pacific Salmon with 5 prototype products 12 panelists, 3 replicates, 6 attributes
17 GPA of Marinated Trout Control 15 Firmness 9.5 Atlantic 10 Salt Juiciness Fish flavor Overall flavor Pacific Flakiness Where s the opportunity?
18 Category assessment - Ham Evaluation of the sensory attributes of a wide range of commercial product. 15 products 10 trained panelists 3 replicates 22 attributes (much more complex)
19 Ham Data Juiciness Fiber Awareness Chewiness Cohesiveness Astringency Pork Fat Pork Flavor Phenolic Wood Smoke Sourness Sweetness Saltiness Firmness Surface Shine Sweet Aromatic Sulfur H15 H14 H13 H12 H11 H10 H9 H8 H7 H6 H5 H4 H3 H2 H1
20 HAM - PCA H3 Juiciness H15 Sulfur Cohesiveness H12 H11 Sweetness Pork Fat Phenolic H6 Pork H10Flavor H14 Sour H1 Surface Shine H4 Wood Smoke H5 Fiber Awareness Firmness H7 H2 H13 Chewiness Sweet Aromatic H9 H8 Salt Astringency Where s the hole?
21 A Real-World Case Study A category assessment of 14 wieners Descriptive analysis by 10 panelists 3 replicates 23 attributes Followed by a consumer study
22 Wiener Attributes Mottled appearance Color Smoke aroma Smoke flavor Green herbs Garlic flavor Pepper Afterburn/Heat Beef flavor Pork flavor Poultry flavor Sweet Salt Sour Firm skin Skin chew Springiness Firmness Cohesiveness Coarseness Particles Juiciness Residual oiliness ** Highlighted samples P<0.05
23 Summary Wiener Data Residual oiliness Juiciness Springiness Skin chew Firm skin Sour Pork flavor After burn/heat Pepper Garlic flavor Green herbs Smoke flavor Color P14 P13 P12 P11 P10 P9 P8 P7 P6 P5 P4 P3 P2 P1 Attribute
24 Means of Wiener Data 8 P1 P2 6 P3 P4 P5 P6 4 P7 P8 P9 P10 2 P11 P12 P13 0 Color Green herbs Pepper Pork flavor Firm skin Springiness Residual oiliness Smoke flavor Garlic flavor Heat Sour Skin chew Juiciness P14
25 Single Attribute Contrasts Juiciness Garlic flavor P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14 0 P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12 P13 P14
26 Wiener GPA 0.98 P1 P3 P6 P2 P13 P8 P4 P P P11 P12 P7 P10 P9-0.98
27 GPA Plot for Wieners P1 P3 P2 Pork Skin chew P6 Smoke Garlic Pepper Sour Heat Juiciness P13 P8 P4 P5 P14 Firm skin P12 Residual oiliness Springiness Green herbs Color P10 P7 P11 P9
28 Preference Mapping and Consumer Target Products Tom Carr Carr Consulting
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