the ideal Profile method - OP&P Product Research

OP&P Product Research
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pieter@opp.nl

Consumer Guided Product
Development
The Ideal Profile Methodology
SPISE 2007 SYMPOSIUM
HoChiMinh-City
July 26-27
July 2007
OP&P Product Research
Pieter Punter

introduction
• the Food Industry constantly strives for product development and
product 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
In reality product is:
not a success success
Management
thinks:
product is
not a
success
success
Refrain from what
you shouldn't do
Type-1 error:
erroneous investment
Type-2 error:
missed opportunity
Do what you should
not refrain from
• 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
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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 j
bi is the relative importance of deviations on attribute i for consumer’s overall
liking
Xij is the consumer perception of product j on attribute i
Ii 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
1,50
dimensie 2 (21%)
1,00
0,50
0,00
-0,50
-1,00
-1,50
product 17
product 8
pepper
oregano
garlic onion thyme
product 2
basil amtveg
product 14
sweet
herby product 10
oilyapp product 18anisfe product 11
pepperi pepper hot
product 5 thicken tomskins product 16
stale
product 3
tompanc
thickness
grainy
product 7
product 13
product 6 cheese glossy
product 4
pureed
frenchon brown color
separate
coatmouth
sour
tomato redcolor
coatpasta
haydry smooth
tomsoap
metalic
product 9
product 12
-2,00
product 15
product 1
-2,50
-2,50 -2,00 -1,50 -1,00 -0,50 0,00 0,50 1,00 1,50 2,00
dimensie 1 (36%)
• 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 liked
or ideal products are obtained
by statistical methods (for instance Prefmap)
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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 assessment
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 perceptions,
this results in a perceptual map
• these assessments are purely
analytical without hedonic/evaluative
influences
• next, consumers 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
measure
conventional
JAR
Ideal Profile
Aj
overall liking
measured
measured
measured
bi
attribute importance
calculated
calculated
calculated
Xij
attribute perception
measured
n.a.
measured
Ii
attribute ideal point
calculated
n.a.
measured
|X-I| attribute deviation
calculated 1
measured
calculated 2
• 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
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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
• the ideal
(black line) is
averaged over
all 5 products
• Liking rating
Current:: 5,7
New-V1: 6,6
New-V2: 6,4
Current5155 New-V1-5155 New-V2-5155 Ideal5155
fresh aftertaste
length aftert
intensity aftert
sour aftertaste
astringent aftert
bitter aftertaste
odour intensity sour odour
sweet odour
40
fresh odour
natural odour
yoghurt odour
taste intensity
smooth mouthfeel
sweet taste
slimy mouthf
sour taste
fattiness
full taste
watery taste
creamy taste
firmness
thick mouthf
off taste
stale taste
astringent taste
bitter taste
mild taste
fresh taste
natural taste
yoghurt taste
artificial taste
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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 β’s
• the PCA extracted seven factors from the 32 intensity attributes (67% VAF):
F1 Bitter/astringent
F2 Fresh/creamy
F3 Odour aspects
F4 Thickness
F5 Aftertaste
F6 Slimy/fat
F7 Sweet/not-sour
• 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):
fresh/creamy
sweet/not-sour
thickness
slimy
aftertaste
bitter/astringent
-0,60 -0,40 -0,20 0,00 0,20 0,40 0,60
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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 to
overall liking by attribute
• for each attribute within a factor, the contribution to overall liking is computed
using the formula:
j
# factors
∑
k=
1
effect _ attribute = factorloading * beta
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_attribute j
” 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)
jk
k
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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
the relative effect
on liking (left axis,
yellow bars) when
the specific
attributes reach
ideal levels (right
axis, marked lines)
20%
10%
0%
-10%
sour taste
Current (liking 5,7)
sweet odour
full taste
yoghurt taste
artificial taste
stale taste
difference from ideal (red=too much, green=too little)
fresh taste
fresh aftertaste
off taste
creamy taste
sweet taste
astringent aftert
bitter taste
astringent taste
mild taste
natural taste
bitter aftertaste
30
20
10
0
-10
-20
-20%
-30
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30
best liked New-V1 and least liked Private label
20
10
0
22
• New-V1 is rated 6,6
and Private label 5,0
• the Private label
lacks thickness, mildness,
natural taste
and freshness
• it is too watery,
astringent and bitter
20%
10%
0%
-10%
New-V1 (liking 6,6) difference from ideal (red=too much, green=too little)
off taste
artificial
taste
yoghurt
taste
full taste
astringent
aftert
sweet
taste
stale taste
bitter taste
creamy
taste
fresh
aftertaste
fresh taste
bitter
aftertaste
mild taste
natural
taste
astringent
taste
-20%
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-10
-20
-30
30
20
10
0
20%
Private label (5,0) difference from ideal (red=too much, green=too little)
10%
0%
stale taste
watery taste
off taste
sweet taste
artificial
taste
yoghurt
taste
fresh taste
astringent
aftert
astringent
taste
fresh
aftertaste
bitter taste
bitter
aftertaste
thick
appearance
firmness
thick mouthf
mild taste
natural taste
full taste
creamy
taste
-10%
-20%
-10
-20
-30

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?
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