OP&P Product ResearchBurgemeester Reigerstraat 893581 KP Utrecht, The Netherlands+31-30 2516772www.opp.nlpieter@opp.nl

Consumer Guided Product Development

The Ideal Profile Methodology

July 2007OP&P Product Research

Pieter Punter

SPISE 2007 SYMPOSIUMHoChiMinh-City

July 26-27

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introduction

• the Food Industry constantly strives for product development andproduct optimization (NPD in short) in order

• to stay ahead of the competition• to prevent boredom and keep consumers satisfied• to deliver food products to the market place that are optimally aligned with consumer preferences

• there are many different procedures, differing in complexity, costs and time; they range from:

• complex, integrated procedures like QFD to• we do it ourselves (since we know best what is best)

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product innovation: the basics

• refrain from what you shouldn't do, and do what you shouldn't refrain from (don’t miss opportunities!)

source: Ellen van Kleef and Hans van Trijp, 2004

In reality product is:

not a success success

not asuccess

success

Managementthinks: product is

Refrain from whatyou shouldn't do

Do what you should not refrain from

Type-1 error:erroneous investment

Type-2 error:missed opportunity

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the underlying assumption in NPD

• all methodologies applied for product optimization share a common, underlying assumption:

• there is an ideal product which maximizes liking• the closer the match between the actual product and this ideal, the higher the liking

the quest is for this ideal product (of course, there can be different ideals for different subsets of consumers)

for instance, the ideal

bicycle for bumpy

roads

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the model

• consumer disliking is a weighted linear combination of the attribute level deviations from the ideal product

• where:Aj is the averaged consumer liking judgment for product jbi is the relative importance of deviations on attribute i for consumer’s overall

liking Xij is the consumer perception of product j on attribute iIi is the ideal level of attribute i that would generate maximum liking

• there are different approaches to estimate the deviations from ideal

reference: Engel, Blackwell & Miniard, 1995

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the conventional, sensory approach

• a sensory panel rates a number of products on sensory attributes and defines the perceptual product space

• target consumers rate the same products on liking• this result is projected in the product space

• from these two data sets, the coordinates of the best likedor ideal products are obtained by statistical methods (for instance Prefmap)

product 13

product 5

product 2

tomsoap

product 9metalic

product 15

product 1

product 12

sourtomato redcolor

separatepureed

tompanc

thicken tomskinsthicknessproduct 6

oilyapp

product 16

smoothhaydry

coatmouthcoatpasta

product 3product 4

product 14

frenchoncheese

grainy

brown colorglossy

stale

product 7

oregano

hot

pepper

herby product 10

pepperianisfe product 11

sweetgarlic

basil

product 8 product 17

product 18pepper

onionamtveg

thyme

-2,50

-2,00

-1,50

-1,00

-0,50

0,00

0,50

1,00

1,50

-2,50 -2,00 -1,50 -1,00 -0,50 0,00 0,50 1,00 1,50 2,00

dimensie 1 (36%)

dim

ens

ie 2

(21

%)

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the JAR approach from applied consumer research

• market researchers also use methods to derive the ideal product attribute levels• they go directly to the consumer and use consumer input derived from the Just About Right (JAR) methodology

• with the JAR methodology:

• consumers rate the products on several intensity attributes and indicate for each attribute whether it is too weak, too strong or “just about right”

• this is a combined judgment (estimate the intensity, check it against your ideal and decide how far away from is it from that ideal), the outcome is the deviation from ideal

• they also rate their liking of the products

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comparison of JAR and conventional

JAR• consumers give a direct assess-ment of the attribute deviations from ideal and of their appreciation• the assessment of deviations from the ideal introduces a hedonic/-evaluative tone (bitterness is too weak or just about right)

• although the methodology is simple and straightforward, it only indicates the direction of the change but does not tell how much change is needed

• there is only an implicit ideal

Conventional• a sensory panel gives absolute analytical judgements of their percep-tions, this results in a perceptual map

• these assessments are purely analytical without hedonic/evaluative influences

• next, consumer s rate the same products on overall liking and the liking ratings are linked to the perceptual map• the methodology is time consuming: the product space is constructed from a (trained) sensory panel and liking must be obtained from a representative consumer panel

• ideals are calculated through regression analysis

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the third way: a combined approach

• the combined approach (the Ideal Profile Method) lets consumers rate the perceived attribute intensities and the ideal intensities separately• this results in a (consumer defined) sensory and ideal profile and in consumer acceptance data for the same products

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the Ideal Profile Method: principles

• consumers profile the products on 20-30 sensory attributes and 6-10 acceptance aspects • for each attribute, they rate the perceived intensity and the preferred or ideal intensity (this is done for each product)

• this results in a perceived and ideal profile for each product

• the underlying sensory dimensions are extracted from the intensity ratings by means of PCA with varimax rotation on the total data (products and subjects)

• this results in a smaller number of combined attributes or dimensions

• regression of overall liking on the factor scores shows the regression weights (relative importance of the different dimensions for liking), these are used to calculate the effects of optimization

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comparing the three approaches

• the Conventional, JAR and Ideal Profile method differ in the way the different measures are obtained

• the results show a substantial equivalence and a high degree of convergent validity

reference:

The quest for the ideal product: comparing different methods and approaches;

van Trijp, Punter, Mickartz and Kruithof, FQP, 2007, 18, 729-741

calculated2measuredcalculated1|X-I| attribute deviation

measuredn.a.calculatedIi attribute ideal point

measuredn.a.measuredXij attribute perception

calculatedcalculatedcalculatedbi attribute importance

measuredmeasuredmeasuredAj overall liking

Ideal ProfileJARconventionalmeasure

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a practical example: yoghourt

• five different yoghourts have been evaluated:

• the current formulation

• two new varieties

• a private label variety• a competitor

• a total of 85 consumers participated (65% females; between 20-60 years old, all users of plain yoghourt)

• they rated each yoghourt on 32 aspects, both for perceived and ideal intensity; on six acceptance aspects and on overall liking• the products have been presented in a sequential monadic test, presentation order was balanced, they received 100 ml of each variety

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step 1: spiders

• the first step is to make spider plots of the average intensity ratings for each product and of the ideal ratings• since the ideals do not differ significantly from each other over products, the average ideal is taken• this will point out on which attributes the products differ from ideal, but it does not tell us how important this deviation is

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the sensory and ideal profiles for current and new

Current5155 New-V1-5155 New-V2-5155 Ideal5155

odour intensitysour odoursweet odour

fresh odour

natural odour

yoghurt odour

taste intensity

sweet taste

sour taste

full taste

creamy taste

mild taste

fresh taste

natural taste

yoghurt tasteartificial tastebitter tasteastringent taste

stale taste

off taste

thick mouthf

firmness

watery taste

fattiness

slimy mouthf

smooth mouthfeel

intensity aftert

length aftert

fresh aftertaste

bitter aftertaste

astringent aftertsour aftertaste

40

• the ideal (black line) is averaged over all 5 products• Liking rating Current:: 5,7 New-V1: 6,6 New-V2: 6,4

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the sensory and ideal profiles for current and others

• Liking rating Current:: 5,7 PL: 5,0 Comp: 6,1

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drivers of liking

• next, the importance of the deviations for liking has to be estimated• step 1: the underlying perceptual dimensions are computed (PCA with varimax rotation on the sensory attributes)• step 2: regression of overall liking on the factor scores to estimate the regression weights (β)

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the underlying perceptual dimensions and ββββ’’’’ssss• the PCA extracted seven factors from the 32 intensity attributes (67% VAF):

• regression analysis of the liking ratings on the factor scores revealed the following drivers of liking (r=0,73; blue=positive and red=negative driver):

F6 Slimy/fatF3 Odour aspects

F5 AftertasteF2 Fresh/creamy

F7 Sweet/not-sourF4 ThicknessF1 Bitter/astringent

-0,60 -0,40 -0,20 0,00 0,20 0,40 0,60

bitter/astringent

aftertaste

slimy

thickness

sweet/not-sour

fresh/creamy

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computation of the contributions per attribute

• the regression weights (β) tell us how much each factor or PCA contributes to liking• the next step is to compute the contribution for the individual attributes

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deviation from ideal and the contribution tooverall liking by attribute

• for each attribute within a factor, the contribution to overall liking is computed using the formula:

in which k is the number of factors and j the number of attributes

• for each product and attribute, the deviation from ideal is computed (delta). • multiplication of delta with the “effect_attributej” gives the amount of change in overall liking for that product if that attribute would be ideal.• next, the relative change is computed (the effect on overall liking when the attribute gets an ideal rating)• the resulting data are plotted for the most important attributes (per attribute the absolute difference from ideal and the relative increment of overall liking if that attribute is rated ideal)

k

factors

kjkj betaingfactorloadattributeeffect * _

#

1∑

=

=

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suggestions for improvement - Current one

• decrease:• sour and bitter (after)taste• astringent (after)taste

• this effects:• off taste, stale and artificial taste

• increase:• sweetness and creaminess• freshness and yoghurt character

• this effects:• natural, mild and full taste

-20%

-10%

0%

10%

20%

sou

r ta

ste

swe

et o

do

ur

full

tast

e

yog

hu

rt ta

ste

art

ifici

al t

ast

e

stal

e ta

ste

fres

h ta

ste

fre

sh a

ftert

aste

off

tast

e

cre

am

y ta

ste

swe

et t

ast

e

ast

ring

en

t afte

rt

bitt

er

tast

e

ast

ring

en

t ta

ste

mild

tast

e

na

tura

l ta

ste

bitt

er

afte

rtas

te

-30

-20

-10

0

10

20

30Current (liking 5,7) difference from ideal (red=too much, green=too little)

the relative effecton liking (left axis, yellow bars) when the specific attributes reach ideal levels (rightaxis, marked lines)

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best liked New-V1 and least liked Private label

• New-V1 is rated 6,6 and Private label 5,0• the Private label lacks thickness, mild-ness, natural taste and freshness

• it is too watery, astringent and bitter

-20%

-10%

0%

10%

20%

off

tast

e

arti

ficia

lta

ste

yog

hurt

tast

e

full

tast

e

ast

ringe

nta

ftert

swe

etta

ste

stal

e ta

ste

bitte

r tas

te

cre

amy

tast

e

fres

haf

tert

aste

fresh

tast

e

bitt

eraf

tert

aste

mild

tast

e

nat

ura

lta

ste

ast

ringe

ntta

ste

-30

-20

-10

0

10

20

30New-V1 (liking 6,6) difference from ideal (red=too much, green=too little)

-20%

-10%

0%

10%

20%

stal

e ta

ste

wat

ery

tast

e

off

tast

e

swee

t tas

te

arti

ficia

lta

ste

yog

hurt

tast

e

fresh

tast

e

ast

ringe

nta

ftert

ast

ringe

ntta

ste

fres

haf

tert

aste

bitte

r tas

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bitt

eraf

tert

aste

thic

kap

pear

anc

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firm

ness

thic

k m

out

hf

mild

tast

e

natu

ral t

ast

e

full

tast

e

cre

amy

tast

e

-30

-20

-10

0

10

20

30Private label (5,0) difference from ideal (red=too much, green=too little)

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summary and conclusions

• the different methodologies for product development or improvement have in common that they want to increase consumer liking by making the difference between perceived and ideal attribute intensities smaller

• they differ in the way they want to achieve this, and range from very time consuming and complex to relatively quick and simple• the Ideal Profile Method is presented as an alternative for the conventional and the JAR method. Instead of using estimated or implicit ideals, the IPM asks consumers explicitly to rate their ideal intensity

• the result of the analysis is a plot which shows for each attribute the deviation from ideal and its the potential relative contribution to liking

• this information can guide product developers in product optimization, but keep in mind to:

….. refrain from what you shouldn't do, and do what you shouldn't refrain from (and don’t miss opportunities!)

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Questions?

• are there any questions?