Combining Choice Based Conjoint and Neuropricing · price point 1 Recalculate the scenario Record...

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

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


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


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


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.


Step 1 – none buyers


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)


Step 2 – adapting the demand curve


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.


Step 2 – deriving the correction factors


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.


Step 3 – using correction factors to adapt all demand curves


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


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 · price point 1 Recalculate the scenario Record...

Documents

Transcript of Combining Choice Based Conjoint and Neuropricing · price point 1 Recalculate the scenario Record...