combining choice based conjoint and neuropricing · price point 1 recalculate the scenario record...
TRANSCRIPT
Combining Choice Based
Conjoint and NeuroPricing®
Maximilian Rausch and Peter Kurz
KANTAR TNS – Applied Marketing Science – Munich
Prof. Dr. Kai-Markus Müller and Nathalie Liegel
The Neuromarketing Labs – Stuttgart
HFU Business School – Villingen-Schwenningen
1 NeuroPricing® 3
2 Choice Modeling 8
3 Best of both worlds 12
4 Case Study 18
5 Findings 23
Content
2 Combining Choice Based Conjoint and NeuroPricing®
NeuroPricing®
1
NeuroPricing® measures unbiased willingness-to-pay
4
Starbucks used to underprice their coffee: The best price-product fit is at 2.40 €
bra
in a
ctiv
ity
price [€]
1.90 €
Low price-product fit
High price-product fit
0.10 €
2.40 €
9.90 €
2.40 €
Feel-good price Disbelief – Quality?
Shock – Profiteering!
At the time of testing Starbucks sold the respective coffee for € 1.80
5
In order to determine perceived value, we measure a match-mismatch signal using EEG – these signals are well known from basic research
6
Basic neuroscience research: Participants are exposed to a series of words.
The brain determines match-mismatch within a few hundred milliseconds.
1
Based on such basic research we have explored EEG signals, which are ideal for pricing research. Our neuroscience team
has discovered that… …the brain determines price match-
mismatch within a similar time window as words!
The averaged values of the brain signals of all participants result in the feel-good
price curve. The feel-good price corresponds to the average perceived value of the product.
2 3
cow
car
cow
milk
Brain responds within 400 ms
with match signal
Brain responds within 400 ms with mismatch
signal
9,90 €
2,40 €
Match
Mismatch
Basic research study design Applied research study design, NeuroPricing® Empirically validated NeuroPricing® Model
1
2
bra
in a
ctiv
ity
1.90 €
High price-product fit
Low price-product fit
0.10 €
2.40 €
9.90 €
Feel-good price
Price in €
What does a NeuroPricing® study look like in practice?
7
https://www.youtube.com/watch?v=E__wJjFik7o
NeuroPricing® identifies unbiased value perception for products and services
Price in €
Targ
et g
rou
p`s
dem
and
in %
5 10 15 20 25 30 35 0
20
40
60
80
100
13,95 €
15,95 €
17,95 €
19,95 €
21,95 €
23,95 €
Trust validated market models
Identify the feel-good-price The brain does not lie
Predicting demand in advance boosts revenue and profits
Understand the perceived value Brain scans measure price perception
without biases
8
From feel-good-price to demand by means of example data: At a price of 17,95 €, a market reaches 91 %
9
Willingness to pay in €
5 10 15 20 25 30 35 40 45
0,7
0,6
0,5
0,4
0,3
0,1
0,2 9 % 91 %
Part
of
the
targ
et g
rou
p in
%
All columns sum up to an exclusive 17,95 € result 9 %. All columns sum up from inclusive 17,95 € result 91 %.
dem
and in
%
price in €
NeuroPricing® is field validated Example showcase: Optimizing price architecture for PepsiCo Lay's Chips in Turkey
PepsiCo field test in large Turkish test regions: ± 3 % correctly predicted!
What will happen if FritoLay increases their price by 0.25
Turkish Lira?
NeuroPricing® including modeling of demand, revenue
and profit curve
"We believe, future pricing questions should be answered using NeuroPricing® because the results are significantly more accurate than those of alternative research methods" Vildan E., Revenue Strategy Manager Turkey, PepsiCo-FritoLay
10
Choice Modeling
2
Academic background
Choice modelling …
Combining Choice Based Conjoint and NeuroPricing®
… is well
established in
physics,
transportational
research,
econometrics and
market research.
… looks back on
more then 40 years
of academic
research. … is based on the
Random Utility
Theory … is close to the
real live decision
and is validated in
many empirical
studies
… Daniel McFadden
received the Nobel
Memorial Prize in
Economic Sciences
for his contribution in
analyzing discrete
choice“.
9
Choice Modeling
Combining Choice Based Conjoint and NeuroPricing® 10
Choice Modeling
Combining Choice Based Conjoint and NeuroPricing®
Benefit of Product X at price 2
Utility sums
Benefit of Product Y at price 2
Benefit of …
Linear model
yi = + 1* xi + 1* x1i + … +
Preference shares
Simulation model
pi = eu i / (e
u i + eu
j + eu k )
Hierarchical Bayes Part worth utilities
Product A
Price point 1 of prod. A
Price point 2 of prod. A
…
Product B
Price point 1 of prod. B
Interview choice tasks
Market shares
Calibration model
based on external data input
11
Best of both worlds
3
Best of both worlds
Combining Choice Based Conjoint and NeuroPricing®
NeuroPricing® Discrete choice modeling
Detailed price curves for the tested products derived from EEG measurement +
Suited for a selected set of products and rather for a small sample -
Choice models tend to underestimate price elasticities especially for low priced products. -
Extending the detailed NeuroPricing® results to a broader scope based on the choice model +
Deep Dive Scalability
13
Combining both worlds
Combining Choice Based Conjoint and NeuroPricing®
Product 1 0,08 € etc.
product 2 0,10 €
product 1 0,08 €
Testing 1 to 3 products using EEG brain scans. More price points are used compared to the discrete choice model.
NeuroPricing® study - Measuring few products directly in the brain
Deriving the perceived value and the willingness to pay of selected products. Demand curves are modeled based on these.
Discrete Choice model – testing several products
Testing all product combinations of interest using a choice model questionnaire
Estimating the part worth utilities of the attributes and levels. Demand curves for the products are derived from market
simulation
Two studies conducted in
parallel
Perceived value
Part worth utilities
Demand curves for all tested product combinations
Demand curve
etc.
14
Demand curve outside the tested price range is based on
assumptions.
No additional information available on how many
respondents do not purchase the product at all
We use the none rate from the choice model to adapt the
NeuroPricing® demand curve.
Combining both worlds
Step 1 – none buyers
Combining Choice Based Conjoint and NeuroPricing®
Price range tested
Demand curve NeuroPricing®
15
Dem
and
in %
Price in €
Add additional price steps
(NeuroPricing® used more price
steps)
Adjusting the DCM price utilities to
match the NeuroPricing® demand
curve
Deriving the correction factors for
the additional products (not tested in
NeuroPricing® exercise)
Combining both worlds
Step 2 – adapting the demand curve
Combining Choice Based Conjoint and NeuroPricing®
0
20
40
60
80
100
0.02 0.04 0.06 0.08 0.1
Demand curve DCM – product 1
0
20
40
60
80
100
0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1
Demand curve NeuroPricing – product 1 0
20
40
60
80
100
0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1
Adapted demand curve – product 1
16
Adjusting each price part-worth by a constant (identical for
all respondents but unique for each price point).
Target price utility is determined by the demand curve
derived from the EEG.
Respondents with larger scale factor (greater certainty)
are less affected.
This procedure is very similar to the individual-level utility
adjustment suggested by Orme and Johnson (2006)
This process is repeated for all tested products at all tested
price points.
Combining both worlds
Step 2 – deriving the correction factors
Combining Choice Based Conjoint and NeuroPricing®
Add the
differences to
the part-
worths for
price point 1
Recalculate
the scenario
Record new
simulated
shares
Update
difference
Repeat until shares match to the desired precision
EXAMPLE Simulated Target Ratio Difference
Share Price Point 1 0,85 0,8 0,94 -0,39
NONE 0,15 0,2 1,33 0
By applying the derived correction
factor to the other price attributes we
adapt all products in the test.
Combining both worlds
Step 3 – using correction factors to adapt all demand curves
Combining Choice Based Conjoint and NeuroPricing®
0
20
40
60
80
100
0.02 0.04 0.06 0.08 0.1
Demand curve DCM – product X
0
20
40
60
80
100
0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1
Adapted demand curve – product X
17
Case Study
4
The client was interested in the optimal price for their print products.
Sample:
Between 18 and 65 years old
Are taking photos either privately or professionally
Have ordered photo prints before or are interested in doing so.
Sample size: 52
Each respondent conducted the NeuroPricing® exercise and answered the choice model which included a larger set
of products.
Study setup
Study on print products – parallel test using NeuroPricing® and DCM
Combining Choice Based Conjoint and NeuroPricing® 19
NeuroPricing®
Perceived value of the
product is about 11 cents
Choice Model
Recommended price
based on the choice
model would be between
8 and 12 cents.
Combined model
More detailed curve
combining the results of
both methods
Expand correction to all
tested products
Correction factors are
applied to all product
combinations
Selected results
Comparing and combining the results
Combining Choice Based Conjoint and NeuroPricing®
Preis in € 0 0.05 0.1 0.15 0.2 0.25
Nachfr
ag
e i
n %
0
10
20
30
40
50
60
70
80
90
100 0,04 €: 96 %
0,08 €: 78 %
0,12 €: 52 %
0,15 €: 33 %
0,20 €: 8 %
89.80%
69.90%
58.80%
39.20%
29.40%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0.04 0.08 0.12 0.16 0.2
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
22
Findings
5
Both methods work fine within their comfort zone
Looking at the main result (the price to choose) both
methods deliver similar recommendations
The combination of both methods can lift the insights to a
higher level
We get the detailed knowledge on perceived values
from NeuroPricing® and have the scalability of a
discrete choice model.
Next Steps:
Enhance the choice model by adding an additional larger
online sample to the exercise
Fine-tune the model by adding “similarity factor” for the
products to the calibration step
Findings
24
Best of two worlds
Combining Choice Based Conjoint and NeuroPricing®