Getting Started: What is CBC Analysis?

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CBC is used for conducting "Choice-Based Conjoint Analysis" studies over the web, in "CAPI" (Computer-Aided Personal Interview) interviewing mode where the device is not necessarily connected to the internet, or via paper-and-pencil questionnaires.  CBC studies are used for learning about respondents' preferences for the combinations of features that make up products or services.  CBC analysis can help with (among other things) product design, line extensions, pricing research, and market segmentation.  There are also numerous opportunities for using CBC in modeling economics and healthcare choices.  

 

The market simulators that result from CBC analysis enable managers to test numerous product formulations and competitive scenarios.  Market acceptance/competitiveness, price sensitivity, and cannibalization are just some of the issues that market simulators based on CBC data can probe.

 

The main characteristic distinguishing choice-based conjoint analysis from earlier types of conjoint analysis is that the respondent expresses preferences by choosing concepts (products) from sets of concepts, rather than by rating or ranking them.  Over the last two decades, choice-based conjoint has become the most widely used conjoint-related technique.

 


The Choice Question

 

A CBC question is often referred to as a "task."  A set of products (concepts) is displayed on the screen and the respondent chooses among the concepts.  For example:

 

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This is a basic display for illustration.  The user has a great deal of control over the fonts, colors, and layout.  Graphics may also be used.  Realistic-looking "store shelf displays" can be developed with scores of product graphics displayed if using the Advanced Design Module for CBC.

 

The attributes that make up each concept are carefully chosen so that the independent effect of each attribute level upon a product concepts' likelihood of choice can be estimated.  CBC software automatically designs the concept combinations based on a few user-defined inputs.

 

Two important decisions to make when constructing choice tasks are 1) how many concepts to present per task, and 2) how many total tasks to ask. A typical study might include about ten choice tasks, with about three to six product concepts per task.  The composition of the choice tasks usually will vary between respondents.  Each respondent receives one version of a larger pool of efficient designs.  Because respondents are randomly selected to receive a different version of the overall design, we refer to these designs as "random" designs.  However, the term random can be misleading, as the designs themselves are carefully selected using a computer algorithm that ensures that each separate version has level balance and near orthogonality.

 

Sometimes some "holdout" choice tasks are added to the CBC questionnaire.  These are not initially used for estimating the preferences (part-worth utilities) for the respondents, but are used to check the internal validity of the estimated utilities.  We refer to these as "holdout" tasks or "fixed" choice tasks.  They are "fixed" in the sense that the product combinations within these tasks are shown in exactly the same way to all respondents.  

 


The Role of CBC

 

Choice-based conjoint analysis has attracted much interest over the last three decades in the marketing research field.  There are many reasons for its rise to dominance among the conjoint-related methods:

   

Researchers tend to favor it because the task of choosing a preferred concept is similar to what buyers actually do in the marketplace.  Choosing a preferred product from a group of products is a simple and natural task that anyone can understand.  An option is also available for asking respondents to choose the least preferred concept within that task, in addition to the most preferred.

 

 

Choice-based conjoint analysis lets the researcher include a "None" option for respondents, such as "I wouldn't choose any of these."  By selecting that option, respondents who do not like any of the options can express their lack of interest.  Comparing "None" usage across groups of respondents can reveal segments that are relatively more or less likely to purchase product concepts.

 

 

Most conjoint analysis studies use "main effects only" assumptions.  Choice-based conjoint analysis typically involves leveraging the data across respondents, making it feasible to quantify interactions. This capability is enhanced by the (controlled) random designs used by the CBC System, which, given a large enough sample, permit study of all interactions, rather than just those expected to be of interest when the study was designed.  It should be noted, however, that using HB estimation for estimating individual-level part-worths significantly reduces the need to model additional interaction terms (in other words, the interactions observed in aggregate models are usually due to unrecognized heterogeneity).

 

 

It is possible in choice-based conjoint analysis to have "product-specific" (alternative-specific) attributes.  For example, in studying transportation we might consider walking shoes and bicycles.  The attributes describing shoes are different from those describing bicycles, and yet one might want to learn how much improvement in walking shoes would be required to switch a respondent from cycling to walking.  CBC's Advanced Design Module permits alternative-specific designs.

 

Choice-based conjoint analysis does have a disadvantage, however: it is an inefficient way to elicit preferences.  Each concept is described using many attributes and each choice set contains several concepts.  Therefore, the respondent may have be faced with a lot of information before giving each answer.  Moreover, the response data are sparse.  With standard discrete choice, the choice does not indicate how much more preferred that option is relative to the remaining options, or the relative preferences for the not-chosen alternatives.

 

For those reasons, in the early days of CBC research, choice-based conjoint studies were usually not used to estimate the values that individual respondents attached to attribute levels, as had been done with traditional ratings-based conjoint methods.  Instead, data from groups of respondents were aggregated for analysis.  This has been done either by combining all respondents or by studying subsets defined by specific market segments.  Researchers estimated "part-worth utility values" for each group of respondents that summarize the choices made by those individuals.  And, as in other conjoint methods, the part-worth values could be used to simulate and predict respondent reactions to product concepts that may not have actually appeared in the choice tasks (questions).

 

Aggregating respondents in CBC analysis assumed respondent homogeneity, which was not always appropriate or desirable.  Developments since the mid-1990s have recognized segment-based or even respondent-by-respondent differences for CBC analysis.  Latent Class analysis (as offered in the CBC Latent Class Segmentation Module) can simultaneously find relatively homogeneous segments and estimate their unique part-worth functions.  

 

Computationally-intensive Bayesian estimation (built into the Lighthouse interface, and also available in Sawtooth Software's standalone CBC/HB System) has permitted estimating individual-level part-worths from choice data.  HB generally leads to more accurate predictions and greater flexibility during analysis.  It is generally considered a gold standard today, though other sophisticated part-worth estimation methods often produce very similar quality results.

 

If you are relatively new to CBC research, we highly recommend that you visit our Technical Papers library at http://www.sawtoothsoftware.com/support/technical-papers.  We recommend you download and read:

 

The CBC Technical Paper

An Overview and Comparison of Design Strategies for Choice-Based Conjoint Analysis

The Benefits of Accounting for Respondent Heterogeneity in Choice Modeling

Special Features of CBC Software for Packaged Goods and Beverage Research

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