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SightX Survey Research Guide: MaxDiff vs Conjoint Analysis


SightX Survey Research Guide: MaxDiff vs Conjoint Analysis

Per SightX

  • Customer Satisfaction Surveys (CSAT).
  • Data analytics
  • Survey analysis
  • Advanced statistical techniques
  • Survey research
  • Survey software
  • Joint analysis
  • Agile quantitative research

If you’ve worked in the insight field for a long time, you’re probably familiar with conjoint and maxdiff analysis. They are widely used in market research, most often for testing products and messages.

And while both can help you better understand what consumers value most about your product or service, each has a unique approach and output that can be beneficial depending on your use case.

So today we’re going to explore what each of these experiments can help you achieve, how they’re similar, how they’re different, and what you should choose for your specific use case!

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Before we dive in, let’s cover a few terms we’ll be using in this article:

Attributes– Also known as product or service features. These are the aspects of your offer that will be evaluated in a maxdiff or conjoint analysis. For example, the attributes of a prepackaged cold beer might be price, taste, package sustainability, or caffeine content.

An image of a cold drink bottle with callouts on the attributes, which are the aspects that will be evaluated in maxdiff or concatenated analysis

Levels– These are simply options that apply to each attribute you will be testing. Continuing the example above, the levels of our flavor attribute can be plain, vanilla, or hazelnut.

Profile or card– This is a hypothetical product (or service) offer generated during a joint experiment. It’s the total package of your attributes with random levels displayed. Below is a sample profile for our cold drink:

A sample profile for a hypothetical product that was generated during a joint experiment

What are Conjoint & MaxDiff Analysis?

Joint analysis

In market research, conjoint analysis is a technique used to measure the value of your product features– individually and in a package.

It does this by giving respondents a choice between 2-3 profiles, each with the same attributes but with random levels. Using our cold beer; a conjoint analysis question examining cost, packaging sustainability, taste, and caffeine content would look something like this:

A sample survey to help measure the value of different product features during a collaborative experiment

MaxDiff analysis

On the other hand, MaxDiff analysis allows respondents to quantify their preferences by rating your product’s attributes as least or most important to them. A sample MaxDiff question exploring similar attributes of our cold brew might look something like this:

    Sample questionnaire during maxdiff analysis to help quantify preferences for different product attributes

How are MaxDiff and Conjoint similar? And how are they different?

Similarities

Both Conjoint and MaxDiff analysis force respondents to make trade-offs, simulating a real-world purchase decision. While each experiment has its own way of doing this, both will give you better insight into which attributes are most important to your audience.

differences

MaxDiff’s output will show you the order of your attributes from best to worst. This gives you a quick and easy way to understand which individual features are most valuable to consumers and which are least important.

Sample MaxDiff Analysis Chart

On the other hand, the result of the joint analysis will not only show you the importance of each attribute, but also the popularity of each level within your attributes. Ultimately, this will help you better understand the optimal package to capture the most market share.

Sample graph from conjoint analysis.

So the next time you’re trying to evaluate which methodology is right for your use case, just think about your goals:

IF you want to better understand the individual attributes of your product, we suggest MaxDiff.

Alternatively…

IF you are interested in finding the best overall package of features for your product, we suggest a match.

And if you need a little guidance to get started with any of these techniques, see our blog on conjoint analysis.

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