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Discrete Choice Modeling in MR Practice

Welcome to demo questionnaires
designed for DCM - Discrete Choice Modeling

Table of Contents

Please note the live questionnaires accessible from this page have only illustrative purpose. They have been stripped of explanation screens, help pop-up windows, conditionally formulated questions and other important aspects of field questionnaires. The purpose is to show, in a concise way, the core possibilities of the interviewing methods that rely on estimation methods based on RUT - Random Utility Theory and DCM - Discrete choice Modeling. All the questionnaires have been programed using the SSI Web software from Sawtooth Software, Inc.

Demo questionnaires
Previews and links
(click to run the demo)

Selectable Options and In-place Calibration in CBC - Choice Based Conjoint
A technical example

A product can often have 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 the reduced MXD - Maximum Difference Scaling is the possibility to estimate interactions of the options in context to the products accepted by respondents.

A CBC task can be completed with an in-place calibration question. In contrast to a 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 excluded.

CBC with Selectable Options and Calibration

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.

Pricing and Promo CBC

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.

Maximum Difference Scaling

Extended MXD - Maximum Difference Scaling
Use of an additional question and calibration

The items tested in MaxDiff 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 in the same format are known to decrease reliability of responses. This can be rectified by ranking of the items selected in the MXD block. Calibration questions can be asked in course of the ranking as well.

Extended Maximum Difference Scaling

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, reduced 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.
Sequential Choice Exercise

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. reduced 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.

Screening of Preferences

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.

CBC of a Reduced Product Set

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.

CBC of a class-based product set

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.

CBC of installment conditions with parametric attributes

A Parametric CBC of a Loan with the stated Amount and Maturity
A CBC of terms and conditions

Similar to the previous example, profiles in a choice set are generated using varying term parameters of the loan 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 the 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.

CBC with parametric attributes

Common Scale Discrete Choice Analysis (CSDCA)
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 which is not available in conjoint results and often bedevils end-users. In CSDCA, 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:
  1. Priors: Determination of order preferences of attribute levels within attributes (sequential choice and randomized Gabor-Granger exercises are used).
  2. Motivators: Determination of preferences of attribute levels between attributes (Best-Worst Case 2)
  3. Choices: Determination of preferences between profiles (an optional conjoint section)
Common Scale Utilities from CSDCA

Object Image Analysis (OBIMA) 
A DCM method using 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 OBIMA design 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 easier to manage. 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 (scale based) questions but the results seem to better discriminate between objects.
Results from this particular survey are on DCM Blog Cz.

Ranked Grid Analysis (RGA)
A DCM method using SCE - Sequential Choice Exercise

The RGA method has been developed as a substantially faster and simpler variant of the above mentioned OBIMA method. Ranking is carried out in a checkbox grid either in horizontal or vertical dimension. Minimal and maximal number of ranks can be set. The rank data are analyzed by HB-MCMC method.
Sensitivity of discrimination between combinations of levels from both dimensions is perceptibly (about five times) higher than from the standard check-box grid.
The presented demo questionnaire features two examples, one with horizontal and one with vertical ranking of items in a check-box grid.

Concept or Package Test
A DCM method using MXD - Maximum Difference Scaling

The Concept or Package Test is a MaxDiff-based method for a special case when concepts are fixed and described as a set of aspects. The objective is to estimate preferences between the product concepts and their aspects.
The simplest interview arrangement consists of two blocks. One block is a best-choice MaxDiff of concepts or packages, possibly completed with a question on purchase intention. The other block is a best-worst MaxDiff of aspects the concepts are composed of. The obtained choice preferences are combined and rationalized as part-worths of the aspects and utilities of the concepts.
The method can handle more attributes than a CBC.

More detailed information on uses of DCM can be found on pages
Discrete Choice Modeling in MR Practice - Discrete Choice Modeling in Marketing Research Practice