The central notion in all DCM-related models and methods is choice probability and its relationship to utility.
A choice of an item from a set of items has some probability. The actual choice is a realization of this probability. The probability model used for interpretation of the choices is the RUT - Random Utility Theory (Louis Leon Thurstone, 1927). Particular behavioral models that are essence of choice modeling, differ only in the considered conditions and assumptions. In market research (MR), a model is usually anticipated before the actual experiment is run. The same is true for experimental design and estimation method that correspond to the model. There is seldom enough time to test several models, designs and methods, discriminate between them and find the best one. A study is planned to match the assumed model. This requires a thorough knowledge of the problem.
A well designed choice-based exercise complies with the four requirements of a good questionnaire:
Experimental methods of market research (MR) based on DCM rely on a
choice from a set of items, i.e. a simulated action conditional to the set
of stimuli presented. The stimuli may span from the real up to completely
fictional ones. The most common sets are made of products, product
profiles, attributes, aspects, properties, statements, etc.
A choice is a natural manifestation of human behavior. It is much less culturally or socially dependent than a metric (evaluation-based) statement expressing a preference, attitude, or value. Especially when differences between alternatives are small, choice methods are likely to be more sensitive and provide more reliable information than ratings. In addition, consistency checks are available that facilitate the identification of respondents who do not have well-defined preferences. Experimental conditions can be adjusted to mimic the real purchase conditions. The concepts of interest, their attributes, and the presence or absence of competing concepts can be considered jointly, and the preferences analyzed.
The use of choice experiments is an alternative to asking direct questions. In some cases, however, results obtained from a standard way of interviewing can be transformed to a format equivalent to that available from a choice experiment and analyzed accordingly. The reverse transformation, usually as a part of the data interpretation in form of a simulation of particular events or their equilibrium, is also possible.
The common problem met in interpreting an experiment is that the experimental data do not reflect what they were expected to reflect. Psychology states, in the PEBSE model, the five important treats influencing human behavior:
One can learn a lot about personality and expectations from a research
interview, but only very little about the influence of the environment or
situation. The behavior in an experiment and conclusions derived from it
cannot reflect the market behavior in its completeness. The results are
always subject to the experimental conditions and the range of collected
pieces of information.
A DCM behavioral model should satisfy constraints inherent to the given type of items and the conditions under which the choice is made so that the estimated model reflects the observed behavior. The model should allow for the following constraints common in MR:
Choice-based modeling in marketing research draws on the achievements in understanding human behavior and advances in numerical methods. A behavioral model lacking a feasible way to reliably estimate its parameters would be useless. The following theoretical foundations are essential.
The probabilistic approach provides marketing researcher with a powerful tool for analyzing and predicting consumer choice behavior. Market (revealed) and/or experimental (stated) research data can be combined and analyzed. This does not mean all problems can be solved with a single model, and with the same ease. Many acute problems still show a stubborn resistance.
postulates (Luce, 1958) assign a choice probability to each
alternative in a choice set made of uncorrelated items. In the case of
such an unstructured choice set, the postulates lead to the well-known
logit formula, also known as BTL - Bradley-Terry-Luce model. Another
consequence of the postulates is the IIA - Independence from Irrelevant
Alternatives axiom that deserves a short note.
The outcome of IIA is that the choice probability ratio for any two items
in any choice set, independently from the presence or absence of other
items in the set, is constant for an individual. The other items are
therefore called irrelevant. Preference shares in a simulation for an
individual are usually taken equal to the conditional choice probabilities
given the choice set, and independent from other individuals. This removes
part of the IIA irrelevancy of items between respondents. However, it is
still present within the simulation for individual respondents. The false
irrelevancy of the items is hidden behind the share averages that do not
have constant ratios due to varying preferences of individuals.
Some products, irrespective of their different brands or other extrinsic
properties, are close substitutes. They are often found among CPG/FMCG
products and are understood as indistinguishable commodities. When present
in a real choice set they are taken as a bulk alternative and the
decision-maker selects one of them by random. In a simulation, if only one
of such products is present in the set, it gets some share. If an
equivalent product is added, the sum of the simulated shares for the two
products is nearly twice as high as for a single product. In reality, the
sum might be only slightly higher than that of a single product share.
This result is due to the IIA assumption that both products are irrelevant
alternatives. This unfavorable property is often explained as a "red/blue
If the estimation of the behavioral model parameters is based on ratings
of stimuli used in the experiment, the method is called metric. If we were
able to obtain reliable (sufficiently accurate and precise) and
unequivocally interpretable metric data, as it is in some other
experimental sciences, no doubt the metric approach would be invincible.
As MR deals with a great deal of randomness and uncertainty, a
probabilistic approach offers by itself. Either of the approaches has its
The term conjoint is believed to be an acronym for "considered jointly".
It is assigned to a rich group of methods allowing extraction of
quantitative effects of the stimuli aspects influencing the respondent's
responses in the experiment. The sets of mutually exclusive aspects are
known as factors in the standard analysis of variance and are called
attributes in MR. A stimulus is composed of aspects each coming from a
single attribute. The most frequent stimuli are products or services.
All conjoint methods have common features described in Conjoint
Method Overview. The most straightforward application of DCM is CBC - Choice Based
There is a special case of a DCM design with just a single attribute. In contrast to conjoint, the levels of the attribute can be mutually exclusive. The goal is to differentiate between the levels and determine their preferences. A choice-based version is known as MXD - Maximum Difference Scaling method, shortly MaxDiff. An indisputable advantage of MaxDiff is possibility to test a large number of items. Various flavors of so-called Bandit MaxDiff have been suggested.
The SCE - Sequential Choice Exercise is a faster alternative to MaxDiff. It is based on the ranking of a full or reduced set of items and is suitable for about 16 items at most.
A common task in MR is an overall evaluation of product concepts with fixed properties. The interest is often in the competitive potential of the concepts. As a standard method, the concepts are randomized and presented to respondents who rate them. The ratings averaged over respondents make the basis for a metric evaluation and interpretation.
Both MXD or SCE methods mentioned above can be used in a concept test. The obtained preferences provide additional discrimination between concepts that are rated equally. To estimate the competitive potential of the concepts in terms of purchase intentions, calibration of the obtained preferences is applied.
A metric-based merging of conjoint utility blocks via several common attributes known as "bridging" often leads to disappointing results. The problem is in different scaling factors in the metric blocks that can be adjusted only with a serious distortion of preferences between the items. In contrast, a merging of choice data can profit from the inherent flexibility of the between-blocks scaling.
Several DCM blocks in a survey can be designed to contain some of the features common to the blocks. The invaluable gain from using the DCM approach is a possibility of a seamless merger of the data from several DCM blocks in a common model.
Merging of several blocks of choice data (rather than parameters estimated before the merging), each having different measurement "sensitivities", is possible by implicit rescaling (compression or expansion) of parameters for each of the blocks by the estimation procedure so that the parameters for all the blocks match. The scaling factors of the choice data blocks automatically adapt one to another. A disadvantage is a potential distortion of some estimates if the constraints between the blocks are not strong enough. As prevention, both the design of the blocks and estimation procedure requires certain restrictions to be imposed. A merging of a SCE - Sequential Choice Exercise or a MXD - Maximum Difference Scaling of fixed product profiles designed in a managerial way, possibly used as a filter in CBS - Choice Based Sampling, and of several class-based CBC - Choice-Based Conjoint blocks of pseudo-randomly modified products, are typical examples. These procedures are the essence of the CBCT - Choice-Based Concept Test and CSDCA - Common Scale Discrete Choice Analysis mentioned previously.
The problem of matching DCM outcomes with aggregated metric data, e.g. with the market data (revealed preferences), does not have a straightforward solution. The same is true for optional features of a product. Nevertheless, if one can assume independence between the core product properties and those of available options, the probability character of choices allows for deriving a model founded on conditional probabilities (a path model).
Estimation of the competitive potential of a core product and its
selectable options is possible with MBC
- Menu Based Choice. The method relies on the choice of a core
product in a standard CBC, and, conditionally, in a modified volumetric
conjoint method that allows for modeling saturation of needs of the
Choice-based methods are very efficient in the interpretation and
prediction of the events made with high involvement. Results for
low-involvement events are often less reliable but still clearly superior
to metric methods. The reason is simple. In a classic metric-based method,
an item is usually rated no more than once. In a choice-based method, it
can be present in many sets and, thus, be evaluated many times under
different conditions and contexts.
Individual-based estimates are very sensitive to the design of choice
sets which concerns namely orthogonality and balance of the sets. The
number of freedoms of the estimated system should always be higher than
the number of estimated parameters. The hierarchical
Bayes or other methods that bank on aggregate sample properties and
allow estimation of a higher number of individual parameters than the
number of degrees of freedom per individual, cannot correct the design
deficiency. Especially the bias of estimates may be unpredictable.
A frequent problem encountered in DCM is varying and often unchecked (or
even inestimable) the precision of estimates. The levels of items that
have been selected least, e.g. those least attractive in CBC
- Choice-Based Conjoint, have the lowest precision for a simple
reason - little data is available. Fortunately, this does not introduce a
big problem in a simulation. Such a product will have only a small share.
However, the simulation may be useless when a low part-worth level is
combined with a high part-worth level. The problem lies directly in the conjoint additive
kernel. Due to the error propagation, a sum of part-worths has
always lower precision than any part-worth alone. When a low part-worth is
"overcome" by a high one the computed item utility may be completely
An unduly attractive item has always an unwanted effect. When such an item is present in a choice task the information is lost for all items in the given choice set. Inappropriately attractive items or the combinations of levels making them so have always a strongly detrimental effect on the estimates and should be strictly avoided. The selection and format of items for a concept test or a MaxDiff study are fully in hands of the project designer. In a conjoint study, the problems can be efficiently tackled using a design based on product classes. Use of prohibited combinations of attribute levels should be avoided whenever and wherever possible.
Deployment of robust design and estimation methods in conjoint studies is important. Commercially available programs (e.g. from Sawtooth Software, Inc.) have parametric options that help to keep tight reins on the design and computation processes.