This information is useful for people who want to understand a couple times 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. I’m particularly fond of Adaptive Choice-Based Conjoint (but that’s a topic for another post). 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 I often get calls from companies thinking it would be a good idea to do a conjoint project.
In this post, I’ll show two common times when it is NOT appropriate to use conjoint analysis.
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:
1. Your product features are already locked in – you just want to test prices. If your product is fully baked, you don’t want to use conjoint. Conjoint is all about looking at the inter-relationship between various levels of product attributes and price.
If your product is locked in as a 10 license product with 1 TB of storage and other features set, conjoint is not for you.
So, how can you test price on your fully baked product?
Note: each of these methods deserves its own post, but here’s a taste.
- 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.
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.
The Newton-Miller-Smith variant of van Westendorp:
- Add purchase probability follow-up questions based on the inexpensive and expensive answers. Build consideration curves.
2. Your attributes don’t vary (don’t 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, package, price, etc. Instead, you want to understand the importance of, or preference for, a number of features/attributes 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 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 on the Outsource Research website.
Maximum Difference Scaling (MaxDiff)
- Force respondents to make trade-offs between (usually) 4 of your items at a time. They 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.
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 feature set is already locked and you just want to test prices, or 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.