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Flavours or types of conjoint analysis

Flavours or types of conjoint analysis For those new to the subject of conjoint analysis, it is easy to believe that there is only one type or version of conjoint analysis (the one type your agency knows). Or the reverse, and become bewildered by the number of abbreviations and names - eg ACA, CBC, MPC, ACBC, full profile, stated preference, DCE/discrete choice estimation among others.

Most conjoint analysis studies carried out professionally use Choice-based Conjoint (CBC) for pricing and brand value studies. Newer adaptive types of conjoint such as Adaptive Choice-Based Conjoint (ACBC) or menu-based conjoint are used for more complex studies. MaxDiff is a commonly used adjunct or substitute for conjoint analysis with large numbers of items. However, different types or "flavours" can be used, depending on the task at hand, and might incorporate Buy-your-own tasks, configurators or take the basic principles and extend them to create tailored designs for specific markets.

Choice method and estimation approach...

There are three core parts involved in conjoint analysis design. Firstly, the product or service needs to be broken down into component attributes and levels. Then, the decision of what is the best way to present options to customers or consumers (product profiles) combined with the decision of which method should be used to find out which product profiles are most preferred (eg choosing, ranking, rating). And finally the decision of which statistical estimation approach to determine the utility value of each attribute and level to the market (also known as part-worths).

These design decisions depend on the number of attributes to be considered, the contact method (online, face-to-face, phone or by post) and time available for the interview. In addition, the different flavours of conjoint analysis each have differing characteristics in analysis.

Choice Based Conjoint- CBC

CBC is the most common form of conjoint analysis used professionally. Respondents are shown a set of potential products, and asked to choose the one they would select often with an option for None of these. Respondents complete a set of these 'choice tasks', most commonly 8-12 tasks in a set. This keeps the time required for the survey to a minimum.

At analysis stage, all the choices across all the respondents are combined together for estimation. This provides an option for very few choice tasks per respondent, but large sample sizes - even down to one choice per respondent for areas like advertising development. Analysis is either based on Logistic Regression, or more commonly Hierarchical Bayes analysis which allows utilities to be estimated at the respondent level making analysis by segments easier.

Most commonly CBC is based on a full-profile design (each product profile is made up from all the available attributes) and the number of products shown can range from 2 to "many" for instance in shelf-based displays used to test pricing for FMCG/CPG type products where there may be 30 brands to choose from at the same time in a 'virtual' shopping environment.

The limitation on the amount a respondent can absorb at a time, combined with the rapidly increasing number of "full-profile" combinations that come with large designs mean mean that choice-based conjoint is typically limited to 5-7 attributes, in contrast to 25-30 for adaptive and partial profile type conjoint anlaysis.

The main advantages choice-based conjoint gives you are greater robustness of results - particularly for pricing work, combined with shorter and therefore less costly fieldwork. It is also favoured for it's rigour academically. It also enables comparisons with fixed products or fixed tasks enabling you to test new formulations against an existing gold standard or, in the more specialist form of Discrete Choice Modelling (alternate-specific designs), the end model can be tuned to real world data greatly increasing it's predictive power.

The disadvantages are the lower number of attributes that are possible unless you move to more complex bespoke designs using partial profiles, and the lack of directly valued individual level utilities - although techniques such as Hierarchical Bayes (HB) analysis seeks to remedy this by post-hoc simulation of individual level values. However, if you are looking to use conjoint analysis for clustering or segmentation you will need to be aware of the trade-offs needed to get individual level utility scores.

Adaptive Conjoint Analysis - ACA

Adaptive methods have been pioneered by Sawtooth Software as a way of increasing the range and number of attributes for conjoint analysis. Adaptive Conjoint Analysis is Sawtooth's older technique, but has fallen out of favour with the wide use and availability of choice-based conjoint. However it remains an option where there are a large number of attributes to be considered for instance in service design or understanding products with a large number of features such as software.

In design, implementation and calculation ACA is completely different to choice-based conjoint, using a self-explicated section and limited to simple paired comparisons of partial profiles. So whereas CBC has respondents selecting from multiple products described with a full set of attributes, ACA shows descriptions using all two or three of the attributes available and is a pairwise only selection. There is also a relatively long self-explication step that respondents need to complete before coming into the main 'pairs' choice part.

The benefits of ACA are that it allows for a large number of attributes (up to 30) and levels (up to 7 per attribute) to be used. However, ACA does require a computer-based interview and the large number of attributes means that it is common for an ACA interview to last 45 minutes or more. In addition, some of the methods it uses to simplify the task of working out utilities mean that some care is needed in choosing and designing the attributes in order to get reliable results. ACA is not considered reliable for pricing studies as a result, underestimating the impact of price in decision making.

Technically ACA is known as a hybrid technique as it contains elements of 'self-explication' followed by the trade-off tasks themselves. ACA itself is produced by Sawtooth Software and can be conducted face-to-face or on-line. Telephone use of ACA is difficult and paper-base questionnaires are not possible.

Adaptive Choice-based Conjoint (ACBC)

ACBC is another Sawtooth approach to the challenge of larger conjoint designs, but this time based on choice-based design principles and so more robust for tasks such as pricing. The design follows a series of stages starting with a Build-your-own section, then a screening section to identify an initial set of attributes and levels that a respondent will use in their decision making. These are then taken into choice tasks similar to CBC for ultimate estimation and selection.

The result is more complex, and slightly more time-consuming than a straight CBC study, but one that allows for more complex attribute sets to be used and that feels less repetitive and more intelligent to respondents as it learns from each stage.

Full-profile - or student conjoint

Full-profile is the original form of conjoint and is still in use, though predominantly in the US or for student type learning projects it would appear. Like choice-based conjoint this uses a more limited number of attributes to describe the product or service, but sufficient cards or treatments are shown to one respondent to enable individual level utilities to be calculated. A fractional factorial design is used to specify a fixed set of profiles that need to be shown for analysis. The difficulty is that this does limit the number of attributes quite severely. It's likely to be used with 3-4 attributes, but even at this amount, it might involve ranking or rating of 16-24 product profile cards. These old school studies are still popular for simple, non-computer-based projects and are most common for students learning about conjoint for the first time. This is also the main form implemented by non-specialist online survey software, or offered by SPSS in their conjoint module.

Discrete Choice Analysis/Discrete Choice Modelling

A more advanced form of choice-based is Discrete Choice Analysis (also known as "stated preference research" or an alternative specific choice-based design). DCA studies are particularly popular for transportation studies looking at modal choice - the preference between a train, car and airline for instance. The main difference from CBC is the inclusion of continuous variables such as price and time. This allows the ability to examine the varying costs of the ticket with varying times taken to travel and so to establish the value of time for the journey. This enables transport economists to make statements like "2cm extra leg room is worth 10 minutes longer journey time or £40 extra fare" or "an extra train every 15 minutes would encourage x% of car drivers to switch to the train".

Shelf-based designs for CPG pricing and category management

A particularly specialist form of DCM and CBC comes in the form of shelf-based designs which show a range of products (SKUs) at different prices. The aim being to understand price elasticities within a particular category. These types of pricing method are very commonly used within FMCG/CPG pricing research and can be calibrated against retail audit pricing data.

Menu-based designs, configurators and e-commerce mock ups

At the most advanced level for conjoint analysis are bespoke designs that incorporate selection menus, build-your-own options, configurators and on to full e-commerce store mock ups with filter and sort tools.

Our Cxoice Survey Technologies were originally created in order to carry out conjoint analysis tasks where traditional forms of conjoint analysis are lacking or where current designs can seem too difficult from a respondent point-of-view. We have a full range of options for conjoint or trade-off task creation, or can incorporate off-the-shelf methods like those of Sawtooth Software in our Cxoice Surveys.

Extensions to conjoint analysis include emotional association tasks,repertoire purchasing (where someone is buying a bundle of products across a range of uses), volumetric measurement and improved choice displays such as using textual noise, hotcold selectors, click-to-rank, sliders or more interactive elements to encourage a fuller participation in the decision making process.

MaxDiff and non-conjoint alternatives

Moving away from pure conjoint designs, many businesses now use MaxDiff to identify core customer preferences without going all the way to a conjoint design. Instead of creating and choosing from whole products, the focus is on a list of features to identify which are most strongly preferred using some of the principles of conjoint analysis. In a typical MaxDiff exercise, the list of features is split into blocks of 4 or 5, and for each block respondents indicate both the most preferred and least preferred feature. Analysis provides a ranked list of features according to respondent preferences.

Deciding which format of conjoint analysis to use

We often find that the choice of the type of conjoint analysis to use depends on a number factors. It's often difficult for someone new to the subject to visualise all the options. The biggest determining factor is the number of attributes, and in cases where there are 6-8 attributes, there can be several options and approaches. We often produce several versions and presentations for illustration to help clients see and understand which form and presentation to use, without compromising the final statistical quality of the survey.

See also:

For help and advice on carrying out conjoint analysis research and which type of conjoint to use contact us at

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