Demo questionnaires
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Previews and links
(click to run the demo)
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Selectable Options and In-place Calibration
in CBC - Choice Based Conjoint
An
illustrative example
A product can often have many optional features. Which of them
are the most effective can be obtained from a CBC with attribute
levels composed of selectable options. The advantage of this
approach compared to MXD -
Maximum Difference Scaling is possibility to estimate relations
(interactions) of the options to the (class of) products accepted
by the respondents.
A CBC task can be completed with an in-place calibration
question. In contrast to the common calibration run with a fixed
set of profiles independent from the conjoint exercise, the
in-place calibration relies on the profiles acceptable for the
respondent. The systematic bias due to the fixed calibration set
is avoided.
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Pricing and promotion CBC study
A shortened
field example
The design allows for the standard pricing, promotion pricing
and free-bees effects to be merged into a single simulation model.
To avoid the bias in estimation of part-worths due to impulsive
behavior of customers in short-term promotional campaigns, an
approach of using two CBC exercises, one without and the other
with promotions applied, has been used.
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Reduced MXD - Maximum Difference Scaling
A simple
alternative to Common Scale Discrete Choice Analysis
There are 18 possible options selectable in a telecommunication
tariff to be tested. The options are made of 6 offers (factors)
each on 3 quantitative levels. Similar to a conjoint exercise,
only one of the levels is shown in a choice tasks. The levels have
a natural order of attractiveness (cheaper is better) which is
used as soft constraints in the estimation of the options
utilities.
Several final choices are completed with a calibration question
so that potential of the options could be estimated.
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Extended MXD - Maximum Difference Scaling
Use of an
additional question and calibration
The items tested in MXD are often required to be viewed from a
higher number of aspects leading to different types of
preferences. If item descriptions are complicated, as is common in
medicinal, pharmaceutical, financial, insurance, telecommunication
and other products, a lot of interviewing time can be saved, and
fatigue from interviewing alleviate, by grouping several
preference choices in a grid.
Consecutive questions of the same response format are known to
yield less reliable responses than presenting them alone or in a
series of topically related questions. This can be rectified by
ranking of the items selected in the MXD block. Calibration
questions can be asked in course of the ranking.
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SCE - Sequential Choice Exercise
A two-stage
technical solution
Ranking of items from a fixed set is a common way of obtaining
preferences but quantification of the results is not as free of
problems as one would wish. Bayesian estimation with restrictions
derived from the properties of
permutohedron allows to obtain estimates of multinomial
utilities with low bias and reasonable scaling. Ranking of the
least preferred items can often be omitted if there is little
interest in them as the latest choices provide only a negligible
amount of information.
Thanks to its simplicity the method can be used with items in
card format in face-to face (P&P, CAPI) interviewing. The data
can be seamlessly merged with data from other discrete choice
based method(s). The following usage is common:
- Product concept test
- CBS - Choice Based Sampling
- Calibration of DCM utilities (e.g. from CBC, ACA, MXD, etc.)
- Full-profile "card" conjoint
- Estimation of product market potential in a competitive
arrangement (a pre-launch test)
- Anywhere a quantitative evaluation of ranks is supposed to be
informative.
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Screening of Preferences
A method
for a large number of items
Numbers of items, options or products available on the current
markets are usually very high. Individual customers are often
inclined to prefer only a fraction of them. It is well known that
a simple evaluation of items each being presented in an isolation
does not give sufficiently reliable estimates. If just a screening
of the market behavior is required, usage of the complete armory
of a full-fledged DCM method for a single (cf. MXD - Maximum Difference Scaling) or
two-attribute (brand-price CBC) study is often superfluous and,
unfortunately, a bit clumsy when the number of items is large.
With the mentioned caveats on mind the two approaches have been
combined. The resulting method is much simpler and quicker than a
conjoint approach, and gives estimates far better than the
traditional one-by-one evaluation. The evaluation scale drift
often observed during an interview is avoided.
The method was originally developed as a replacement of a
self-standing evaluation of conjoint attributes with many levels
(a.k.a. self-explicated conjoint), and a simplification of
calibration of a relatively large number of profiles (cf. SCE - Sequential Choice Exercise). It is
feasible with a general survey interviewing software.
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Brand-Price CBC with a Reduced Product Set
A method
for a respondent-based selection of brands
Data from a brand-price CBC with a large number of brands often
bear a frustrating level of noise. This is mostly due to the
requirement to chose from among many items a respondent is only
little, if at all, interested in. A solution is to reduce the set
so that it contains brands considered by the respondent as
possible choices, and of course, the brands of interest in the
test.
As the amount of information obtained from a reduced set is
lower, it has to be compensated with a larger sample. This is
especially true when a small number of brands dominate the market,
and the study is aimed at some new or less frequently bought ones.
The starting selection block of an interview is carried out as a
SCE - Sequential Choice
Exercise. If the SCE choices are completed with questions about
purchase intention a market potential estimation is possible.
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A Class-based Design of a Brand-Price CBC
A balanced
presentation of brands
The producer of First snacks was considering an introduction of
smaller packages containing 1 or 3 packets (servings) for a
presumably significant segment of light users. The problem was to
decide if this would increase reach and/or share with or without
replacement of some of the current 4 piece packages. The major
competing brands Second and Third were available only in packages
of 4.
The 3 package sizes and 3 flavors for the First brand products
gave 9 products compared to 3 products of each other brand. This
would lead to a gross selection bias in the standard CBC design. A
compromise solution was found in 5 item choice sets composed of 2
First products and at most 2 products of any of the competing
brands in a choice set using a class-based design. This allowed to
obtain data for estimation of availability and cannibalization
effects among all the tested products. Another class-based design
fully balancing brands in 9-item choice sets shown in the second
part of the questionnaire is an example of an improper design due
to different availability, substitution and cannibalization
effects among the brands.
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A Parametric CBC of an Installment
Sales Method
A CBC on an
Evoked Set
Most conjoint exercises deal with products whose properties are
composed of attribute levels directly shown to respondents. The
goal of a parametric study is to determine effects of some
parameters on (acceptability of) the tested objectives. Parameter
values may or may not be shown to respondents.This is typical for
financial products such as accounts, transactions, loans,
mortgages, installment sales, etc. The parameters can be interest
rate, up-front payment percentage, length of a contracted period,
values in various fee formulas, etc. Parameters may be, and often
are, mutually interdependent and/or constrained. The shown values
are computed using the tested parameters.
In this example, installment sales parameters, namely percentage
of an upfront payment and of price surcharge, are tested on an
evoked set of cellular phone handsets. The set is determined as
the three top choices from a pool.
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A Parametric CBC of a Loan with Fixed
Amount and Maturity
A CBC of terms and conditions
Similar to the previous example, profiles in a choice set are
generated by varying terms parameters of the product provision.
The loan amount and maturity period are stated directly by a
respondent. The ranges of tested parameters (namely monthly fees
and interest rates) are conditional to the stated amount and
maturity of the loan and to presence of the free-of-charge early
repayment.
The CBC task is followed by a block of calibration questions
realized as a simple SCE - Sequential Choice Exercise.
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A Common Scale Discrete Choice Analysis
A hybrid method with Best-Worst Case 2 [Louviere
et al., 1995]
as a core
The method allows for comparison of level part-worths between
attributes. This is not available in conjoint results which often
bedevils end-users. Attribute levels are put on the same ratio
scale, as is shown for HDTV sets in the picture at the right
(taken from a web
published paper). The method has been enhanced with
estimation of perceptual thresholds.
A simple hypothetical product was chosen for the demo. The exercise
is composed of three basic sections:
- Priors: Determination of order preferences of
attribute levels within attributes (SCE - Sequential
Choice Exercise)
- Motivators: Determination of preferences of
attribute levels between attributes (Best-Worst Case
2)
- Choices: Determination of preferences between
profiles (an optional conjoint section)
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Object Image Discrete Choice Analysis
A DCM method based on SCE - Sequential Choice Exercise
The method has been developed to achieve a better discrimination
power between objects based on their aspects than is available from
scale-based questions. Aspects for an object are ranked rather than
evaluated. Ranking is used also for objects in respect to their
aspects.
The method is based on the idea published by McCullough
(2013) by replacing the proposed MaxDiff and Q-Sort methods with SCE
exercises that are faster and easy manageable. Obtaining perception
thresholds (anchoring) is inherent to the method without using any
additional questions. An early experience shows an interviewing time
is about 50% longer than using evaluation questions but the results
seem to better discriminate between objects.
Results from this particular survey are on DCM
Blog Cz.
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