Methods for Pricing Research

Survey research on price sensitivity helps market professionals
on decision making at each stage of a new product development: concept
analysis, development and launch.

Do you have a new concept to be tested?
Are you developing a new product?
Did your competitors changed
their prices?
Is your brand going to launch
a new product in the market?
Survey research on price sensitivity can really help
you out on understanding consumer preferences.

This presentation compares six methods of pricing research: their
ability to recover information about customer values, how they are
executed, how can results be interpreted and their limitations.
Concept test
/concept evaluation
Conjoint Analysis
PSM BPTO
Brand Price Trade Off
Monadic scenarios
Discrete Choice Modelling
(Choice Based Conjoint)

Concept test
/concept evaluation
Conjoint Analysis
PSM BPTO
Monadic scenarios
Methods for Pricing Research

PSM
Price Sensitivity Meter
This method was introduced in 1970’s by Peter van Westendorp, a Dutch economist. It is
based on the premise that there is a range of prices bounded by a maximum that a
consumer is prepared to spend and a minimum below which credibility is in doubt.
In this method respondents must ask to four price-related questions:
 At what price would you consider the product to be so expensive that you
would not consider buying it? Too expensive
 At what price would you consider the product to be priced so low that you
would feel the quality couldn’t be very good? Too inexpensive
 At what price would you consider the product starting to get expensive, so
that it is not out of the question, but you would have to give some thought to
buying it? Expensive
 At what price would you consider the product to be a bargain – a great buy for
the money Inexpensive

0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 € 2 € 3 € 4 € 5 € 6 € 7 € 8 €
Too Inexpensive
Inexpensive
Expensive
Too Expensive
The cumulative percentages are plotted and the four key intersections are interpreted. It
allow us to predict the optimum price point (OPP) and the range of acceptable prices:
These distributions are usually display in a chart, as shown below.
Range of Acceptable Prices
PMC – point of marginal cheapness
PME – point of marginal expensiveness
IPP – Indifference price point
OPP – optimal price point
PSM
OPP
IPP
Range of Acceptable Prices

PSM approach has some limitations:
 data is dubious as it relies heavily on consumer’s price awareness in a market,
which means that respondent’s ability to answer these questions is dependent upon
their having a good reference price. For this reason, in a large sense, PSM becomes a
test of price rather than a measure of price sensitivity.
 do not provide any insight into the customer values that drive a purchase
decision.
 it is misleading because it combines buying intention with price sensitivity.
PSM

Methods for Pricing Research
Concept test
/concept evaluation
Conjoint Analysis
PSM BPTO
Monadic scenarios

0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Definitely
would not
purchase
Probably
would not
purchase
Might or
might not
purchase
Probably
would
purchase
Definitely
would
purchase
5 €
10 €
15 €
Concept test
/concept evaluation
In this method respondents are presented with a product concept and asked how likely
they would be to purchase this product at a specific price.
How likely would you be to purchase this box of chocolates if it cost 10€?
Definitely would purchase, Probably would purchase, Might or might not purchase,
Probably would not purchase, Definitely would not purchase

Concept test
/concept evaluation
The Concept test method has some limitations:
 provides no competitive information: respondents do evaluations without any
information about other products that might be available in the market. They are
not able to do trade-offs between features and price to determine their preferred
product, which means they do not model real-world decisions.
 relies on price awareness: respondents base their answers on their awareness of
the current pricing in the category.
 is inefficient when evaluating numerous product specifications: each
respondent should only evaluate one concept.
 relies on aggregate-level analysis: this approach will make respondent
heterogeneity difficult to detect and measure.
 cannot indicate what a better or ideal price might be, but only if the proposed
price is acceptable.

Conjoint Analysis
In conjoint analysis each respondent is exposed to many concepts. Respondents are
asked to make hypothetical trade-offs between products, as buyers are forced to do
when actually shopping.
In each product the combination of features shown together (including price)
changes, so that a large number of product features can be evaluated. Impossible
combinations (e.g., an expensive attribute and a low price) are eliminated.
Example 1:
Considering the purchase of a tablet. Below are 8
options for this tablet. They differ in 3 basic
benefits (weight, the length of the battery life,
and the price). Rank order these 8 options from 1-
8 by assigning 8 to your most preferred option
and 1 to your least preferred option (it is very
important that you assign 8 to your most
preferred option.)
OPTION WEIGHT
BATTERY
LIFE
PRICE RANKING
1 300 gr 6 hours 500€
2 500 gr 12 hours 300€
3 500 gr 6 hours 500€
4 300 gr 12 hours 500€
5 300 gr 6 hours 300€
6 500 gr 12 hours 500€
7 500 gr 6 hours 300€
8 300 gr 12 hours 300€

Conjoint Analysis
Example 2:
Considering two TV alternatives.
Which would you prefer?
or
1 2 3 4 5 6 7 8 9
Strongly prefer
product on left
Strongly prefer
product on right
Example 3:
Extremely clear
picture quality
$300
Clear
picture quality
$200
Please distribute 100 points
between the following attributes
according to the influence each
has on your decision to purchase
Points
Price
Bonus Airline Miles
Extra Leg Room
Preferred Security/Screening
On-Time Departure /Arrival
Assigned Seating
Total 100
Each respondent provides information on his or her trade-offs so that the utility of each
product characteristic (including price) can be estimated for each respondent. This
individual-level estimation allows the identification of individual differences that can
lead to a market segmentation scheme and can be used to help predict acceptance of
products by different individuals in a heterogeneous market.

The Conjoint Analysis method has some limitations:
 requires the respondent to state a preference among a set of configured
products, but it does not ask if the respondent considers the configured
product well enough to buy it.
 the respondent task of providing a rating is still not as realistic as choosing
a product, like consumers actually do when shopping.
Conjoint Analysis

BPTO
BPTO is a simplified form of conjoint analysis in which respondents choose a brand
from a series of price scenarios. This approach is the simplest method for assessing the
relative impact/value of the brand as it only uses two variables: brand and price.
Several brands are shown at once and the customer chooses the preferred option. Price
are adjusted and the customer chooses again. This method allows answering to these
questions:
 What are the maximum acceptable prices for a product?
 What differences of monetary values are perceived by consumers between the
products being presented?
 What market shares can a product achieve at certain price levels if it competes
with other products?
BPTO is most used in consumer type markets where there is little to choose functionally
between products. For other type of products, such as services, industry and technical,
more sophisticated techniques are needed.

BPTO
Example:
Limitations
 in most markets it results in exaggerated price elasticity.
Imagine you are choosing a bottle (2 litres) of soft drink from a shop. If the drinks were
priced as follows which would you choose?
Choose a product then click on ‘Select’ to see some new prices and to choose again.
Brand Price Choose…
7 Up 1.20 
Coca Cola 1.20 
Fanta 1.20 
Pepsi 1.20 
Sprite 1.20 
None of these 1.20 

Monadic scenarios
This approach asks respondents to make a realistic
purchase from a shelf display, price marked as in the real
world. Respondents are not prompted to look at price in
any overt way.
The effects of different price scenarios are evaluated by
creating different displays and obtaining a choice from a
different sample of people.
Monadic scenarios are used to generate an accurate price
elasticity and have been validated across a wide variety of
fmcg (fast moving consumer goods) markets.
This method can be combined with other methods such
BPTO.
Limitations
 the cost, as every extra scenario we want to test means an extra sample of people.

In Discrete Choice Modeling a set of products with different features and prices is
presented to respondent and he/she has to pick one. In this regard, it is based on a
more realistic task that consumers perform every day: the task of choosing a product
from among a group of competitors.
The scenarios can be varied across participants, with different choice sets or different
prices and data is aggregated later. The utility of the products relative to price can be
measured then.
The results from discrete choice modeling are quite similar to those from conjoint as
both approaches allow what-if simulations.
Example:
Which TV would you purchase?
Brand A
81 cm
Wi-Fi, 3D
€500
Brand B
106 cm
No Wi-Fi, 3D
€600
Brand C
101 cm
Wi-Fi, 3D
€700
NONE
If these were my only
alternatives I would not
purchase anything

Contact us
Adress: Rua Carlos Mardel, N.º 94, 1º Esquerdo, 1900 – 125, Lisboa, Portugal
Telephone: +351 218 496 151
E-mail: sonia.casneuf@em-estudosdemercado.pt
Website: www.em-estudosdemercado.pt
E.M. - ESTUDOS DE MERCADO E SONDAGENS DE OPINIÃO
Count on us!
Together we make the difference.
Do you want to know more about research on price sensitivity?
We can help you decide the most suitable method for you.
Contact us, we are at you disposal!

Methods for Pricing Research

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