a jmp-based user-friendly analysis and presentation … jmp-based user-friendly analysis and...

Consumer test Evaluation Tool

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A JMP-Based User-Friendly Analysis and Presentation System for Consumer Test Evaluation & Interpretation in the Food Industry

Jeff Stagg- Kraft Foods

David Rose - SAS


Consumer Test Evaluation Tool

• A customised software interface that performs data handling / mathematical calculation to make a wider community more efficient / effective in work and increase the use of best practices

• A proprietary, user friendly, software tool created in collaboration between SAS and Kraft Foods for use by Kraft‟s consumer research scientists

• Executes customised JMP scripts for efficient consumer test evaluation

• Designed for world-wide application with a global user base

Description


Speed, quality, cost

• Analysis completed in half the time

• Increasing use of „best‟ practice procedures

• Consistent graphics in management/ client communication

• Global user capability replaces expert coverage from key sites

• More/ broader influence of experts via training/ advice/ coaching

• Software licensing/ deployment & management efficiencies


Benefits

Consumer test Evaluation Tool Consumer test Evaluation Tool

Example of Data Sources

Consumer Test

• Panel of 8-12 expert tasters

• 6+ products

• In-house

• 30+ sensory attributes [Intensity]

• 2-3 replicate sets of determinations

• Managed by Kraft sensory professionals

To analyse results from consumer studies in conjunction with sensory,

chemical, physical, process design data to deliver interpretations of consumer

preferences for product change/ portfolio optimisation decisions

Sensory Test (QDA)

Panel trained to evaluate products objectively

and are expected to be as homogeneous in

their assessments as possible

Consumer responses expected to be very heterogeneous in terms of their preferences.

[no one product expected to satisfy all]

Typical Project Objective

• 200+ consumers

• 6+ products

• Central location

• 20+ attributes [Liking, Intensity, JAR]

• No replication

• Managed by external market research agency

• Demographic, Usage, Attitudinal data


Product Code Product Type

X035 A1 A

X051 A1+3 A

X235 A2 A

X318 A2+3 A

X529 A2 A

X554 A2 A

X609 A3 A

X712 A3 A

X185 B4 B

X234 B5 B

X243 B6 B

X367 B4 B

X383 B6 B

X676 B6 B

X740 B7 B

X862 B6 B

2 beverage types: “A” and “B”

3 flavours of “A” (1 - 3)

4 flavours of “B” (4 - 7)

[ Flavours A ≠ B]

The product set is from a Category Appraisal Study on two different types of

beverage: multiple flavours

Case Study Product Set


Case Study Objective

• Obtain overall liking scores for products within the beverages category in a single study

• Determine if there are significant differences in the overall liking scores between:

• two key beverage types

• multiple flavours

• Identify consumer groups with different type/flavour preferences

• Determine differences in product usage / occasions in consumption for flavours and beverage type

• Identify potential opportunities for Quality Improvement (QI) or New Product Development (NPD) that would help shape strategic development


Case Study Workflow

Data validation

Create product colour profiles

Key attribute assessment

Cluster analysis

‘Drivers of liking’

Consumer Testing

[data collation]

Consumer Test Report

[data interpretation]

Distributions of variables are presented and

range properties assigned

Products are assigned colours for graphical output

Multi colour profile sets are possible that can

represent a property (origin, brand) or rank order of a

key measure

Graphical representation of data – histogram with range

test; star, scatter, stacked bar & distribution charts

Review of solutions : cluster method & number of

clusters- using dendrogram, 3D preference map and

product cluster scores

PCA map (external data) with key attribute overlay (liking)

& optional response surface model, single attribute

regression using scatter plot format


Data Scaler helps validate labelling assignment to data and variable

ranges versus consumer test questionnaire

Data validation


A central feature of the application makes available to users the means to

switch colour schemes at any point in the analysis, allowing them to

investigate different features of the data.

Each colour scheme constitutes a profile.

Multiple Colour Profiles

Overall Liking

Fisher's LSD (a=0.05); LSD=0.16; N=796

1 2 3 4 5 6 7 8 9

X185 - B4 6.33

X367 - B4 6.46

X234 - B5 6.58

X740 - B7 6.61

X383 - B6 6.73

X035 - A1 6.74

X051 - A1+3 6.76

X243 - B6 6.77

X862 - B6 6.80

X609 - A3 7.10

X554 - A2 7.17

X318 - A2+3 7.22

X712 - A3 7.35

X676 - B6 7.38

X529 - A2 7.52

X235 - A2 7.54

Overall Liking

Fisher's LSD (a=0.05); LSD=0.16; N=796

1 2 3 4 5 6 7 8 9

X185 - B4 6.33

X367 - B4 6.46

X234 - B5 6.58

X740 - B7 6.61

X383 - B6 6.73

X035 - A1 6.74

X051 - A1+3 6.76

X243 - B6 6.77

X862 - B6 6.80

X609 - A3 7.10

X554 - A2 7.17

X318 - A2+3 7.22

X712 - A3 7.35

X676 - B6 7.38

X529 - A2 7.52

X235 - A2 7.54

Profile to highlight differences in beverage

type (purple for “A” , grey for “B”). Profile to highlight extent of overall liking,

irrespective of product type.


Histogram plot of average overall liking scores with range test enables

fast/ simple interpretation of product performance in consumer test

Key measures assessment

All Test Persons

Overall Liking

Products „connected‟ by same bar are statistical parity for significance test applied


Colour coding by order of overall liking enables easier visual interpretation

=> Most liked products score well for taste, appearance and consistency



By changing the profile, we can immediately compare performance for

beverage type



A spider chart enables a large number of attributes to be compared simultaneously

=> Sourness intensity relates most strongly with overall liking, lower level preferred


Significant difference between products when band widths do not overlap


All Test Persons

Liking score distribution charts illustrate „satisfaction profile‟ for each product

No single product completely satisfies all consumers: with approximately 35% of

ratings for most liked products scoring less than 8.0, could there be two or more

groups of consumers with different patterns of liking?

=> Cluster Analysis



We can perform a cluster analysis on the respondents‟ data to identify groups

of like-minded individuals who score the products in similar ways:

• The dendrogram on the left shows how the

consumer study respondents cluster.

• Each “twig” on the left of the diagram represents

one respondent. The sooner these “twigs” join

up into branches, the more similar those

respondents are in terms of their scoring

patterns.

• Determining the actual number of clusters is

often somewhat subjective. In this example

there could be several clusters – but for

illustration purposes we have assumed that

there are just two: the “red” respondents and the

“green” respondents.

• We can show the same data in another format

which allows us to interpret the clusters more

easily…

Cluster Analysis


By presenting the scores within a Principal Components style of display with

the same colour-codes, it is possible to interpret the scores more easily:

Chart showing PC1 vs PC2 for 796 respondentsGenerally prefer “A” products to “B” products

Generally prefer “B” products to “A” products

High

Scorers

Low

Scorers

Cluster Analysis


We can also view the plot in three dimensions, and view it from different

angles. We can also incorporate the products into the plot to show which

ones are preferred by which groups of consumers:

Cluster Analysis


Should a „satisfactory‟ clustering solution be identified, products ratings

for each cluster group can be generated and compared PCA Plot of Scores (2 Clusters) Crosstabs (2 Clusters) Product Names & CodesDendrogram (2 Clusters)

Cluster 1 (66%) Cluster 2 (34%)

Overall Liking

Overall Liking

Larger cluster strongly prefer beverage type A

Smaller cluster like beverage type equally, their response driven by flavour – optimal levels of 6 & 2

Cluster 1 (66%)

Cluster 2 (34%)

Cluster Analysis


Clu

ste

r 1 (

66%

) Clu

ste

r 2 (

34%

) The clusters have different relationships with the 1st principal component, which

has a greater correlation with the data in both clusters than the 2nd or the 3rd

Drivers of liking


Drivers of liking

Combining range test with sensory data PCA helps to identify potential

„drivers of liking‟

Principle Components Map with Cluster 1 Liking Overlay Cluster 1 consumer overall liking ratings

Simple Conclusion : expert sensory attributes - viscous, flavour 2 & astringent are key descriptors that describe differences between beverage type hence product liking scores for consumers in Cluster 1 (66%)


Fitting a response surface to cluster 1 overall liking mean scores identifies

potential QI / NPD insights for developing Beverage type A

Drivers of liking

Key toOverlay

123456789

X035 - A1 (7.00)

X051 - A1+3 (6.62)

X185 - B4 (6.08)

X234 - B5 (6.39)

X235 - A2 (7.57)

X243 - B6 (6.69)X318 - A2+3 (7.41)

X367 - B4 (6.22)

X383 - B6 (6.64)

X529 - A2 (7.66)

X554 - A2 (7.38)

X609 - A3 (7.40)

X676 - B6 (7.26)

X712 - A3 (7.51)

X740 - B7 (6.58)

X862 - B6 (6.56)

Overall impact

Fl avour3a

Fl avour6a

Fl avour6b

Fl avour2

Fl avour4

Fl avour7

Fl avour8

Fl avour9

Cook_Processed

Medicinal _Chemi cal

Perfumey_Soapy

Sweet Sour

Bi tter

Astri ngent

Vi scous

Sour aftertaste

Bi tter aftertaste

Astri ngent aftertaste

1

2

2

1 Optimal product („me too‟ X529) on boundary of optimal cluster 1 sensory area

Optimal product central to optimal cluster 1 sensory area

Consumer test Evaluation Tool Consumer test Evaluation Tool

• User-specified product colour profiles (interactive switching)

• Full names or label option for chart output (interactive switching)

• Customised scripts for Kraft Foods analysis best practices

(example: identifying clustering solution)

• Business presentation chart formats

(interactive customisation – properties & layout)

• File/chart handling (combining and storage)

• Access to full JMP software functionality (DOE, regression, partitioning etc)

Analysis performed in half the time

=> A saving 3-5 man days per study

Efficiency Benefit


• Quality Improvement / New Product Development insights are generated from the co-analysis of objective and subjective data relating to a range of products, allowing for the possibility that different consumers have different preferences.

• The JMP application developed for Kraft Foods runs a series of customised scripts that perform this analysis efficiently, follows a best practice approach and delivers report-ready graphics and statistics at every step.

• The application allows researchers to spend substantially less time processing their data, and correspondingly more time considering what it means.

Summary

Consumer Test Evaluation Tool

a jmp-based user-friendly analysis and presentation … jmp-based user-friendly analysis and...

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