Conjoint for Every Project? Not So Fast!

This information is useful for people who want to understand when it is not appropriate to use conjoint analysis.

Conjoint analysis is a gold standard technique for measuring feature preference, particularly in relationship to price.  We’re particularly impressed with the results from Adaptive Choice-Based Conjoint.  There’s a lot of buzz around conjoint as a tool to help product managers choose features that will help their products better compete in the marketplace, so we often get calls from companies thinking it would be a good idea to do a conjoint project.

Below, we describe when it is NOT appropriate to use conjoint analysis.

Defining Attributes and Levels

An “attribute” is something like brand, number of licenses, amount of storage, color, package size, etc.  A “level” is the degree of an attribute.  For example, brand A, B or C; 5, 10, or 20 licenses; 1, 2 or 3 TB of storage; blue, red, or black color; 12 ounce, 18 ounce, or 24 ounce package.

When NOT to Use Conjoint:

Your attributes don’t vary (have levels) – you’re just testing preference/importance of a number of items.  You are not looking at the inter-relationship of various levels of brand, size, quality, durability, price, etc.  Instead, you want to understand the importance of or preference for a number of features/attributes (without looking at their levels), or that each have a single (constant, not varying) level.  Perhaps you want to test the general importance of brand vs size vs quality, etc.  Or, you may want to understand how the importance of the specific, fixed features that make up your product (e.g., is having 10 licenses more important than having 1 TB of storage or the other features that make up your product?).

So, how can you test preference/importance of these features?

Note: A full description of MaxDiff can be found in other articles and videos on our website.

Maximum Difference Scaling (MaxDiff)

  • Force respondents to make trade-offs between (usually) 3-5 of your items at a time. Respondents indicate which item is most and least preferred (important, etc.).  The output yields all the items on a 100-point scale, where you can truly say that a given item is “twice” as preferred as another item with half its value.

Note: MaxDiff can be used to help reduce the number of attributes that you carry forward into a conjoint.  For example, if your product has a lot of potential features to test, it would be wise to reduce the number that you bring into conjoint, so that the respondent is not overwhelmed.  MaxDiff can show you the most important attributes, which can then be further explored in the conjoint.

Your product features are already locked in – you just want to test prices.  If your product is fully baked, in order to use conjoint, you would have to use a specific type described below.

If your product is locked in as a 10 license product with 1 TB of storage and other features set, you need to think about conjoint in a different way than usual.

So, how can you test price on your fully baked product?

Note: each of the non-conjoint methods described below has issues.  A further discussion can be found in other articles on our website

Monadic designs:

  • Break your respondent sample into groups that each see a single price associated with the product and ask their likelihood to purchase. Plot the probabilities against the prices.  OK to use if you have sufficient sample size so that you can randomize sample groups.

The  van Westendorp Price Sensitivity Meter:

  • Ask “too inexpensive”, “inexpensive”, “expensive”, and “too expensive” questions. Plot the data to obtain lower and upper bands and optimal price point.  Not recommended.

The Newton-Miller-Smith variant of van Westendorp:

  • Add purchase probability follow-up questions based on the inexpensive and expensive answers. Build consideration curves.  Better than straight VW if you have purchase probability baselines.

Price-Only Conjoint:

  • Allows showing different prices for different brands (or products), allows different price utilities for different brands/products, and avoids systematic underestimation of price effects. Think of each product’s price as a separate “attribute” in the conjoint sense.  Can fractionalize the design so each respondent does not need to do the whole design.  Usually modeled at aggregate level, but using Hierarchical Bayes is better.  Issue – cannot simulate with products deleted from or added to the basic set we designed around.  Pretty face valid that we are testing price, but experience shows that price sensitivity is realistic.


Conjoint analysis is a powerful technique that can help you configure your feature-price mix to create a product that will be most preferred by your market.  However, if your attributes don’t have any variation (levels) to them, then conjoint is not for you and you’ll need other techniques to solve your research problem.

For more information about conjoint, MaxDiff and other ways we can help, contact us at