consumer driven product development for export markets
TRANSCRIPT
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Consumer Driven Product Development For Export Markets
Chris Findlay February 18, 2015 Alberta Agriculture/CIFST
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• Sensory Science as a Tool • Sensory Maps provide guidance for NPD • Case Studies
– Red Wine – Cabernet Sauvignon – Whole Grain Bread – Asian Noodles -‐ CIGI
Overview
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Consumer Driven PD
The success of products developed for export markets increases greatly when developers have specific consumer-‐driven sensory targets to work with.
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The Scallop Story
SoU as BuVer, Tough as Rubber It’s all in the cooking
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Good fun! Poor science!
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The Beef Story
Tenderness and Cooking
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• The Coke-‐Pepsi Challenge – Difference Tests
• Product profiles – Descrip]ve Analysis using trained panels
• Consumer Tes]ng – Product liking with larger groups
What do we really do in Sensory?
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Compusense Panel
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The Infamous Beer
Accelerated Batch Fermenta]on (25 days to 15 days) Accurate Balanced Fermenta]on
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PC
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PC 1
Product Liking Clusters
X2
B1
G8
G1
G4 X1
G2
Products that are found close together are in the same liking cluster
CLUSTER 1
CLUSTER 2
Strawberry Jam Consumer Liking Clusters
Chunks of Fruit
Smooth and Sweet
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• Product taste tests were once the staple of product development or quality control departments.
• The ques]ons were simple and the results were basic.
• Sensory science has moved forward from then through the applica]on of basic science to build a robust understanding of sensory evalua]on, consumer response and consumer choice.
Start off simple…
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• Discrimina]on – Difference Tests
• Formula]on Changes • Process Improvements • Mul]-‐plant produc]on • Ingredient Subs]tu]on
Product Matching
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• Analy]cal sensory profiles of products that are both accurate and precise.
• A library of the sensory proper]es of prototypes
• Reliable measures of sensory shelf life.
Calibrated Descrip]ve Analysis
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The Ballot
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The Response
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Immediate Feedback
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FCM Introduced in 2006
|
2000’s |
|
1990’s
2010’s
40 hours
20 hours
6 hours
The Effec]veness of FCM Training
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What we know about products • There is no product that is “universally” liked, even water.
• Op]miza]on of products is essen]al to achieve efficiency and market success
• To op]mize, you must have a clear target. • Unless you segment your consumers based upon their sensory preference you will not have a clear target.
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What we know about consumers • There is no such person as an “average” consumer.
• There is no product that is “universally” liked, even water.
• Tradi]onal demographics are no indicator of consumer preference
• Consumers Lie!
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• Cluster consumer based upon their liking of products and their behaviour.
• Consumer-‐driven product development; crea]ng new products based upon sensory design, targeted on the desires and needs of specific consumer groups.
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Beyond Tradi]onal Segments
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Finding the right respondents
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Data collection at stores
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Data collection at stores
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Data collection at stores
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Make sure they are the real consumers!
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• Sensory and consumer research has taken advantage of the progress in compu]ng and communica]ons to be able to take its tests to consumer, wherever they are and to permit sensory laboratories to collaborate on a global scale.
Exploi]ng digital technology
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Web-Based Data Collection
Global Platforms Consistent methods Sophisticated tests Central management SaaS Management
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Web-Based Data Collection
The world has changed:
• DA now performed at home
• HUT tests done on the web • Smart phones allow tests to be done online anywhere
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• Consumer segmenta]on is important to understand liking
• Consumer-‐driven product development works
• Large consumer tests are expensive
• Large consumer tests take ]me and resources
Consumer Category Tests
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• The Risks – Fa]gue – Carry-‐over effect – Boredom. – Consumers behaving like experts – Resources
• The Remedies – Tes]ng at a single event – Incomplete Block Designs
• The Challenges – Missing data – Valida]on
Considera]ons for Large Studies
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The Effect of Order and Day on Consumer Liking 12 White Wines, 115 Consumers, CBD 12:12 over 3 Days
0
10
20
30
40
50
60
70
1 2 3 All
Day 1 Day 2 Day 3 Combined
Posi]ons 1st 2nd 3rd 4th
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Sta]s]cal Challenge • A valid approach to segmenta]on of consumer BIB data • Using a combina]on of sensory best prac]ce, experimental
design and advanced sta]s]cal analysis
Sensory Informed Design Method Development
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Cabernet Sauvignon Study
• A study of 12 Cabernet Sauvignon wines was conducted using over 600 recruited consumers and tested for liking
• Consumers sampled 3 of the 12 wines in a BIB design
• Data was analyzed for liking clusters with missing data replaced with consumer’s individual mean
• Four liking clusters successfully demonstrated different sensory liking profiles
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3.00 4.00 5.00 6.00 7.00 8.00 9.00
W1
W2
W3
W4
W5
W6
W7
W8
W9
W10
W11
W12
Cabernet Sauvignon Mean Liking
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3.00 4.00 5.00 6.00 7.00 8.00 9.00
W1 W2 W3 W4 W5 W6 W7 W8 W9
W10 W11 W12
3.00 4.00 5.00 6.00 7.00 8.00 9.00
W1 W2 W3 W4 W5 W6 W7 W8 W9
W10 W11 W12
3.00 4.00 5.00 6.00 7.00 8.00 9.00
W1 W2 W3 W4 W5 W6 W7 W8 W9
W10 W11 W12
3.00 4.00 5.00 6.00 7.00 8.00 9.00
W1 W2 W3 W4 W5 W6 W7 W8 W9
W10 W11 W12
Cluster 1 – 28% Cluster 2 – 23%
Cluster 3 –32% Cluster 4 – 17%
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Cabernet Sauvignon Study: Results
• Findings demonstrated that although the method was not robust, the approach gave useful and ac]onable results
• A research program was ini]ated to develop a systema]c approach to building designs using sensory informa]on to ensure contrast
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Is there a Preference? • To state a true preference a consumer must be
able to see a real difference • Otherwise it’s just a guess • Consequently we must present the consumer
with truly different samples
Sensory Design
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Let’s consider a sensory space
Can we find logical contrasts to test?
Factor Scores plot : dimension 1 versus 2
-2.58 2.58
-2.58
2.58
Tobacco
Smoke
Asparagus
Coffee
FloralVanilla
Green
WoodyPepper
EucalyptusFruityRaisin
Leather
Sweet
Sour
BitterAstringent
W1
W2
W3
W4W5
W6W7
W8
W9
W10
W11W12
Factor Scores plot : dimension 3 versus 4
-2.58 2.58
-2.58
2.58
TobaccoSmoke
AsparagusCoffeeFloralVanilla
GreenWoodyPepper
Eucalyptus
FruityRaisin
Leather
Sweet
Sour
BitterAstringent
W1
W2W3
W4
W5
W6
W7
W8
W9W10
W11
W12
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Quadrangles and Triangles Factor Scores plot : dimension 1 versus 2
-2.58 2.58
-2.58
2.58
Tobacco
Smoke
Asparagus
Coffee
FloralVanilla
Green
WoodyPepper
EucalyptusFruityRaisin
Leather
Sweet
Sour
BitterAstringent
W1
W2
W3
W4W5
W6W7
W8
W9
W10
W11W12
Factor Scores plot : dimension 3 versus 4
-2.58 2.58
-2.58
2.58
TobaccoSmoke
AsparagusCoffeeFloralVanilla
GreenWoodyPepper
Eucalyptus
FruityRaisin
Leather
Sweet
Sour
BitterAstringent
W1
W2W3
W4
W5
W6
W7
W8
W9W10
W11
W12
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Whole Grain Bread Study
In a 2012 study of whole grain breads, 570 consumers 16 samples using an improved SID of 16:6, with nested designs of 16:3 and 16:4
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GPA of 16 Whole Grain Breads 55 Sensory Attributes
-1.10 1.10
-0.50
0.50
Variance Accounted for: DIM1 – 66% DIM2 – 9% DIM3 – 7% DIM4 – 4%
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3 4 5 6 7 8 9
Overall Liking All 570 Category Consumers
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7.1
6.8
6.6
6.5
6.4
6.2
5.9
5.9
5.8
5.8
5.7
5.5
5.5
5.2
5.0
7.3
J LOVE IT!
L HATE IT!
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2 3 4 5 6 7 8 9
Cluster 1 Overall Liking 25.8 % 147 Consumers
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7.4
6.7
6.7
6.6
6.5
6.1
5.8
6.0
4.9
5.6
4.9
4.4
4.5
4.4
3.2
7.8
J LOVE IT!
L HATE IT!
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3 4 5 6 7 8 9
Cluster 2 Overall Liking 45.3 % 258 Consumers
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7.9
7.8
7.7
7.5
7.3
7.0
6.6
6.9
6.3
6.5
6.0
5.9
5.9
5.7
5.6
8.0
J LOVE IT!
L HATE IT!
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3 4 5 6 7 8 9
Cluster 3 Overall Liking 28.9 % 165 Consumers
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6.9
6.2
6.1
5.9
5.7
5.5
5.3
5.5
4.6
4.9
4.6
4.5
4.5
4.5
3.7
7.6
J LOVE IT!
L HATE IT!
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• Improves the efficiency of large studies • Can be used for any product category • Improves the quality of data collected • Delivers actionable consumer clusters • Saves resources
Conclusions about Sensory Informed Design
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Combining highly efficient methods can reduce costs
without compromise
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How is it that we never have enough ]me to do the job right,
but always enough ]me to do it over?
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1. Provide an effec]ve strategy for category assessments
2. Reduce large numbers of possible test products
3. Understand the product sensory space
Objec]ves
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4. Design highly efficient consumer studies
5. Combined methods
6. Deliver reliable and robust outcomes
Objec]ves
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1. Projec]ve Mapping (Napping)
2. Calibrated Descrip]ve Analysis (FCM)
3. Sensory Informed Design
The Methods
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4. E-‐M Imputa]on of Missing Data
5. Cluster Analysis on Consumer Liking
6. Correla]on of Sensory and Consumer Data
The Methods
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Projec]ve Mapping Whole grain breads 50 products to a set of 16
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GPA of 16 Whole Grain Breads 55 Sensory AVributes
-1.10 1.10
-0.50
0.50
Variance Accounted for: DIM1 – 66% DIM2 – 9% DIM3 – 7% DIM4 – 4%
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Sensory Informed Balanced Incomplete Block Design (SID)
Sample sets that • maximize sensory contrast • ensure consumer liking • results reflect consumer sensory preference
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SID Procedure
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SID Consumer Studies
Whole Grain Bread Nested design 16 present 3 16 present 4 570 consumers 6 products for liking
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Conclusions
• Projec]ve Mapping
– efficient
– selects a representa]ve sensory product set.
• FCM trained DA panel
– less than half the ]me
– greater precision.
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Conclusions
• SID consumer research
– Eliminates fa]gue or boredom
– provides a solid basis for consumer segmenta]on.
• EM Imputa]on
– realis]c values for the missing data
– integrates with clustering to iden]fy liking segments.
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Pusng it all together
Projec]ve Mapping
Calibrated Descrip]ve Analysis
SID-‐based Consumer Research
Clustering and
Correla]on
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Asian Noodles Descrip:ve Study
March 2014
Canadian Interna:onal Grains Ins:tute
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Objec:ve
• To compare the sensory profiles of two types of instant noodle product, Deep Fried and Steam Dried, against benchmark products that are popular in the target market, China.
• To use the sensory differences to guide product improvement in the noodle prototypes being developed at CIGI.
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Deep Fried Instant Noodles
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Table 1. Deep Fried Test and Benchmark Products Product Name Category Best Before Date UPC
Deep Fried Instant Noodles 5% Low Protein Pea Flour Prototype N/A N/A Deep Fried Instant Noodles 5% High Protein Pea Flour Prototype N/A N/A Deep Fried Instant Noodles 15% Low Protein Pea Flour Prototype N/A N/A Deep Fried Instant Noodles 15% High Protein Pea Flour Prototype N/A N/A Master Kong Instant Noodle Benchmark May 29, 2014 6900873059010 Doll Instant Noodle Benchmark November 5, 2014 079551730445
Products
Test products were provided by Canadian Interna5onal Grains Ins5tute. Benchmark products were procured by Compusense from local Asian grocery stores. Tes5ng was conducted at the Compusense Research Facility in Guelph, ON the week of March 17, 2014
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Deep Fried Benchmark #1: Master Kong (康师傅)
• It is a product of China. • Based on our research this is a long exis]ng and popular brand of instant noodles that owns the most of market share in China.
• The product represents generic deep fried instant noodle.
• We have already sourced and purchased enough for this project.
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Deep Fried Benchmark #2: Doll (公仔面)
• It is a product of Hong Kong.
• The product represents generic deep fried instant noodle.
• This is certainly a popular brand in Hong Kong and Southern China.
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Product Means and ANOVA
ATributes p value LSD Fried 15% High Fried 5% High Fried 15% Low Fried 5% Low Master Kong Doll
Overall Colour 0.00 3.5 64.0 a 48.1 c 58.3 b 47.5 c 57.9 b 57.4 b
Surface Shine 0.00 2.8 55.2 b 55.5 b 51.9 c 57.2 b 64.1 a 61.6 a
Overall Aroma 0.00 1.5 30.5 b 29.3 b 29.2 b 29.5 b 32.1 a 29.5 b
Starch Aroma 0.25 -‐ 17.5 17.0 17.6 17.2 18.3 17.3
Green Aroma 0.03 1.3 1.4 ab 2.1 a 2.6 a 1.8 ab 2.7 a 0.7 b
Overall Wheat Aroma 0.36 -‐ 18.4 17.1 17.3 17.8 17.5 17.9
Oil Aroma 0.99 -‐ 11.3 11.1 10.8 10.9 10.9 11.1
Surface S]ckiness (hands) 0.00 2.3 28.6 cd 29.7 c 27.2 d 29.7 c 38.3 a 33.1 b
Elas]city 0.01 3.7 35.3 b 35.2 b 33.3 b 36.5 ab 34.6 b 39.9 a
Surface Roughness 0.50 -‐ 22.9 22.4 21.7 21.7 21.4 22.7
Surface S]ckiness (mouth) 0.00 2.6 30.5 cd 32.6 bc 28.6 d 32.0 c 35.8 a 34.8 ab
Firmness 0.00 1.7 42.2 a 40.3 b 41.4 ab 40.4 b 33.8 d 38.1 c
Chewiness 0.00 2.1 34.0 a 32.3 ab 32.6 ab 32.2 ab 24.9 c 30.6 b
Cohesiveness 0.00 1.6 23.8 a 21.9 b 22.7 ab 22.5 ab 19.7 c 21.4 b
Residue 0.02 1.4 19.2 ab 18.3 bc 19.1 ab 18.3 bc 17.3 c 19.7 a
Significant at p=0.05 Highest Score Lowest Score Scale: Unstructured Scale from 0 to 100
Table 2(a). Deep Fried Category Product Means and ANOVA
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Product Means and ANOVA
ATributes p value LSD Fried 15% High Fried 5% High Fried 15% Low Fried 5% Low Master Kong Doll
Overall Flavour 0.02 1.6 28.2 b 28.0 b 28.2 b 28.4 b 30.4 a 29.5 ab
Starch Flavour 0.64 -‐ 17.0 17.1 16.4 17.5 17.1 16.9
Green Flavour 0.01 0.9 1.1 b 1.1 b 1.6 ab 1.1 b 2.4 a 1.0 b
Overall Wheat Flavour 0.12 -‐ 18.2 17.1 17.7 17.8 16.6 18.1
Oil Flavour 0.09 -‐ 13.3 13.4 13.2 13.4 13.6 14.8
Sal]ness 0.00 1.5 20.3 b 19.2 b 20.0 b 20.3 b 21.8 a 22.0 a
Sourness 0.57 -‐ 6.8 6.9 7.1 7.0 7.5 7.2
Sweetness 0.01 1.2 15.3 b 15.2 b 15.4 b 15.4 b 16.8 a 16.7 a
BiVerness 0.53 -‐ 8.3 8.4 9.0 8.9 9.1 9.0
Overall AUertaste 0.46 -‐ 20.7 19.9 21.0 20.4 20.8 20.6
Starch AUertaste 0.29 -‐ 12.8 13.6 13.4 13.4 13.9 12.8
Green AUertaste 0.01 0.7 0.7 b 1.0 b 0.8 b 0.9 b 1.8 a 0.6 b
Wheat AUertaste 0.43 -‐ 13.5 13.1 13.1 12.6 12.6 13.2
Sal]ness AUertaste 0.00 1.2 12.8 c 13.0 c 13.5 bc 12.8 c 14.5 ab 14.7 a
Significant at p=0.05 Highest Score Lowest Score Scale: Unstructured Scale from 0 to 100
Table 2(b). Deep Fried Category Product Means and ANOVA (con%nued)
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Principal Component Analysis (Covariance) of Fried Instant Noodles (PC1 versus PC2)
Fried 5% Low
Master Kong Doll
Overall Colour
Surface Shine
Firmness Chewiness
Cohesiveness
-‐25
25
-‐25 25 Surface S]ckiness (mouth)
Fried 15% High
Surface S]ckiness (hand)
Fried 5% High
Fried 15% Low
Variance Accounted for: PC1 – 54% PC2 – 35% PC3 – 8% PC4 – 2%
PC1
PC2
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Differen:al Profiles of Significant ATributes
The charts are created by subtrac]ng the profile of one product versus another product of interest to highlight the aVributes that are different.
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Difference from the Doll Benchmark
-‐10.0 -‐8.0 -‐6.0 -‐4.0 -‐2.0 0.0 2.0 4.0 6.0 8.0 10.0
Overall Colour
Surface Shine
Overall Aroma
Green Aroma
Surface S]ckiness (hands)
Elas]city
Surface S]ckiness (mouth)
Firmness
Chewiness
Cohesiveness
Residue
Overall Flavour
Green Flavour
Sal]ness
Sweetness
Green AUertaste
Sal]ness AUertaste
Fried 5% Low
Fried 5% High
Figure 1. 5% Low and 5% High Mean Differences from the Doll Benchmark Product
Greater Than Doll Benchmark
Less Than Doll Benchmark
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Difference from the Doll Benchmark Figure 2. 15% Low and 15% High Mean Differences from the Doll Benchmark Product
-‐10.0 -‐8.0 -‐6.0 -‐4.0 -‐2.0 0.0 2.0 4.0 6.0 8.0 10.0
Overall Colour
Surface Shine
Overall Aroma
Green Aroma
Surface S]ckiness (hands)
Elas]city
Surface S]ckiness (mouth)
Firmness
Chewiness
Cohesiveness
Residue
Overall Flavour
Green Flavour
Sal]ness
Sweetness
Green AUertaste
Sal]ness AUertaste
Fried 15% Low
Fried 15% High
Greater Than Doll Benchmark
Less Than Doll Benchmark
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-‐12.0 -‐8.0 -‐4.0 0.0 4.0 8.0 12.0
Overall Colour
Surface Shine
Overall Aroma
Green Aroma
Surface S]ckiness (hands)
Elas]city
Surface S]ckiness (mouth)
Firmness
Chewiness
Cohesiveness
Residue
Overall Flavour
Green Flavour
Sal]ness
Sweetness
Green AUertaste
Sal]ness AUertaste
Fried 5% Low
Fried 5% High
Difference from the Master Kong Benchmark Figure 3. 5% Low and 5% High Mean Differences from the Master Kong Benchmark Product
Greater Than Master Kong Benchmark
Less Than Master Kong Benchmark
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-‐15.0 -‐10.0 -‐5.0 0.0 5.0 10.0 15.0
Overall Colour
Surface Shine
Overall Aroma
Green Aroma
Surface S]ckiness (hands)
Elas]city
Surface S]ckiness (mouth)
Firmness
Chewiness
Cohesiveness
Residue
Overall Flavour
Green Flavour
Sal]ness
Sweetness
Green AUertaste
Sal]ness AUertaste
Fried 15% Low
Fried 15% High
Greater Than Master Kong Benchmark
Less Than Master Kong Benchmark
Difference from the Master Kong Benchmark Figure 4. 15% Low and 15% High Mean Differences from the Master Kong Benchmark Product
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• Consumer segmenta]on is essen]al to understand liking
• Large consumer tests are expensive
• Large consumer tests take a lot of ]me and resources
• Integrated methods are efficient
Consumer Category Understanding
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Consumer-‐Driven Product Development
WORKS
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• Analy]cal sensory product profiles
• Accuracy and precision
• Reliable measurement
• Advanced sta]s]cs
• Publish innova]ve methods
Science-‐based and Research Focused
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compusense.com +1 519 836 9993 – Worldwide
1 800 367 6666 – Toll free in North America [email protected]
Thank you!