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Alternatives to conjoint analysis

Alternatives to conjoint analysis for trade-off estimation Conjoint analysis is a widely established market research technique for understanding how people value the component elements that make up a product or service - the attributes and levels. However, in certain circumstances, for instance where there are lots of attributes to consider, or where bundles are being built, it may be better to consider alternatives such as MaxDiff, Configurators, Simalto or a range of other more bespoke designs and choice tasks.

Note that we would consider options such as Discrete Choice Modelling (DCM), stated preference research and elements like shop-display tests for pricing as 'flavours' of conjoint analysis rather than purely distinct.


The most common adjunct to conjoint analysis is the use of MaxDiff (also known as best-worst analysis). MaxDiff is a popular alternative to conjoint analysis because it is easy to set up, with a simple pick-from-a-list approach and only considered benefits one-at-a-time which makes design easier, while allowing for many more benefits or features to be compared, in contrast to normal conjoint limits (50-100 items is common).

Respondents are asked to pick the best and worst items from a short list - usually of 4 or 5 items. They are then shown another list and the process is repeated. This enables the elements of the list to be allocated a utility score (and rank) in a similar way to conjoint analysis.

One difference is that the items are not shown in combination as part of a product profile but are valued individually relative to each other, and thus value highpoints and gaps can be identified. MaxDiff is typically analysed using a hierarchical bayes (HB) method with the effect that data is scaled like a conjoint study. This allows for comparative preferences to be understood, without going to the full choice-modelling between products of conjoint analysis.


Menu-based conjoint (MBC) and configurators

Menu-based conjoint is the formal name of a set of conjoint software from Sawtooth, but it can also be used to describe more bespoke configurator type approaches to collecting information about choice-making.

With menu-based approaches, respondents are given the task of choosing or creating products via a configuration menu where individual items might have their own price, or the respondent can choose from bundles or menus (a hamburger meal is typically given as an example). By observing choices with different price points, standard choice-based analysis tools can be used to ascertain customer value and willingness to pay.

In more bespoke examples, a dedicated configurator can be used, where respondents choose features and levels to build up their most preferred products, in a similar way to the Dell PC-configurator. By asking respondents how they would change their choices in different scenarios - eg an increasing price, the configurator can be used to uncover choice preferences. With careful design and analysis allows both the valuation and price sensitivity around individual items can be assessed.



Simalto (simultaneous multi attribute level trade-offs) is originally a paper-based method of getting individuals to make trade-offs on a trade-off grid where there are lots of attributes and levels such as in a service review. A trade-off grid looks like the attributes and levels laid out on a single grid and respondents are asked to use the grid to rate performance and to indicate which areas are priorities for change. Being detailed, the process of completing a Simalto service grid often generates a wealth of additional comment and detail making Simalto an effective prompting tool for depth interviews. Simalto can also be combined with point allocation tasks to indicate the value of improvements. Where these points are allocated dynamically by the computer they can result in something similar to the configurator model. These types of approach can be more rewarding for the respondent as they are less repetitive and more information is given about the reasons why detailed decisions are being taken.


Simple ranking

Ranking questions also force individuals to trade-off between alternatives and ranking is one method of collecting data from a simple full-profile conjoint analysis. Ranking is traditional cumbersome with more than about 8 or 10 items so tasks can be split or rotated to simplify the task. The use of online surveys allows ranking to be done more easily - eg a click-to-rank, drag-and-drop or by asking respondents to make selections in roughly rank order and monitoring mouse clicks.


Advanced ranking (eg anchored ranks, forced difference ratings)

One problem with plain ranking is that the step-size between items is equal. An alternative is an anchored ranking - that is to take a ranking then anchor a point (eg the top is worth 100 points) and get the respondent to allocate points to items ranked below - so indicating relative step-size. An alternate version which is made possible with sliders with long scales (eg to 100) is to disallow a rating at the same point - so no two features can score the same. This forces small differences between ratings (eg 92 to 93) which then gives both a rating and a unique rank.


Dynamic budget allocation

In some ways conjoint provides too much information in that it's objective is to provide a valuation for all the levels in all of the attributes. In general we are most interested in the items that are most valuable. One way of building this is to offer the levels at fixed 'point' values and then give respondents a point budget, then get them to optimise the product within the given budget. The point values are then adjusted and the respondent repeats the task essentially discarding the items not chosen, while focusing on the items that are of most value.


E-commerce mock ups with search and filter tools

An extension to conjoint is to consider not a smaller set of items, but a bigger set of items and to use e-commerce or product aggregator mock-up with search and filter tools to allow the respondent to search for their optimum product choices.

Combined with ranking and experimental design principles, and adding textual noise to alleviate the repetition of standard conjoint analysis, these mock-ups are designed to look like familiar online app or shopping environment. The mock-up allows the respondent to explore the choice set in a broader and more natural way, to which tasks and journeys can be assessed (eg pick the top three products you would buy). The use of searches and filters is in itself a choice and by providing a richer more natural interface it becomes possible to mimic typical online search and choice behaviour, and then make something directly comparable to real e-commerce purchasing.

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