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MaxDiff vs Conjoint Analysis: When to use each method

Learn how these two powerful market research tools can improve your business.

Maximum differential (MaxDiff) and conjoint analyses are frequently used in market research, especially when collecting data for product development. Both MaxDiff and conjoint analysis are particularly useful in evaluating customer preferences for product enhancements, features, or new products. Since they’re both discrete choice exercises, they force respondents to make tradeoffs and indicate preferences between options. 

Both of these methods demonstrate the importance of considering how to ask questions to yield the best, most valuable results for your research. For example, questions must be easy to answer but still provide you with the most accurate representation of the respondent’s sentiment.

Let’s take a look at these unique analysis methods, why you should use them, and some examples of each one.

MaxDiff analysis, also known as Best-Worst Scaling, is an analytic methodology that is used to quantify preferences. A MaxDiff survey asks respondents to review a set of several options and select only the best and worst ones. This eliminates unhelpful responses vaguely ranking all options the same or in a line of non-committal middle answer choices. Instead, by asking only for the best and worst, you can hone in on what respondents consider the absolute most important and least important among the choices provided.

This method is useful in sorting product features, marketing messaging, and other items or attributes by customer preference. Using MaxDiff, you can also test combinations of related features. This helps you to identify what consumers want most from you and your products, so you can maximize your time, energy, and budget on the features your customers want most.

MaxDiff yields quantitative data, but you can also derive qualitative insights because the analysis reveals how important the attributes you’re testing are relative to each other. By attributes, we mean properties, items, or features you want to evaluate. The attributes are arranged in sets—groups of attributes—for survey participants to consider. 

MaxDiff questions can be asked in a dedicated questionnaire or as a part of a larger market research survey.

There are several reasons to use MaxDiff analysis; however, it isn’t appropriate for every type of survey. Keep in mind that MaxDiff is best suited for surveys determining preference for one feature, service, or product over another.

Some of the advantages of MaxDiff analysis include:

  • Reveals how your customers feel about a specific feature of your product
  • Clear, actionable data
  • Powerful discrimination tool
  • Easy for respondents to understand
  • Uses tradeoffs 
  • Eliminates scaling bias
  • Ability to test a large number of attributes
  • Provides ratio data

Choose MaxDiff analysis when you are seeking single-level preference data, specifically to determine the value of two attributes. Multilevel preference data works best with conjoint analysis, which we will discuss shortly.

Your department is in charge of determining the best features to add to your smartphone product. A MaxDiff question you ask survey participants may look like the following:

Please indicate the new smartphone feature that would be most important in your purchase decision and which feature would be least important.

Most ImportantFeatureLeast Important
◻️5G◻️
◻️Flexible screen◻️
◻️Smart camera◻️
◻️Built-in projector◻️
◻️Augmented reality◻️

The next MaxDiff answer set to that question may look something like this:

Most ImportantFeatureLeast Important
◻️Multiple cameras◻️
◻️Unbreakable screen◻️
◻️Smart camera◻️
◻️Warranty◻️
◻️Augmented reality◻️

And the next may look like this:

Most ImportantFeatureLeast Important
◻️5G◻️
◻️Flexible screen◻️
◻️Unbreakable screen◻️
◻️Built-in projector◻️
◻️Warranty◻️

MaxDiff surveys use multiple variables in sets to find out what is truly most important. The variables are then compared to others throughout a series of questions. 

Conjoint analysis, or trade-off analysis, is a form of statistical analysis used to understand how customers value different components or features. This method requires participants to use similar behaviors to the ones used in making purchase decisions while shopping. Participants view sets of features from a group of similar items to make purchase decisions. Data reveals how their preferences are impacted by specific attributes.

Conjoint analysis is based on the principle that any product can be broken down into a set of attributes that impact a consumer’s perceived value of the product. Using this analysis, you can determine what combination of a limited number of attributes is most impactful on a customer’s purchase decision.

Conjoint analysis is valuable in a variety of ways:

  • Useful in identifying tradeoffs consumers make in their evaluation of products and features
  • Provides insights into hidden preferences that may not appear on numerical rating scales
  • Offers the user affirmation of what attributes are most important to consumers
  • Can be used to evaluate brand strength in a particular market
  • Closely resembles customer shopping behaviors

Example of conjoint analysis

Returning to our smartphone company, this time we are using conjoint analysis to evaluate the importance of brand, price, screen, and storage in the purchase decision.

If you were in the market for a new smartphone, which of the following would be most appealing to you?

ModelSamsung Galaxy A50iPhone 12 Pro MaxGoogle Pixel 4a    None
Price$140.99$1399$479I would not choose any
Screen Size6.2 in6.7 in6.2 in
Storage64 GB512 GB128 GB
Selection

The next question set might look like this:

ModelSamsung Galaxy A50iPhone 12 Pro MaxGoogle Pixel 4a    None
Price$140.99$1399$479I would not choose any
ScreenGorilla GlassCeramic ShieldGorilla Glass
PlatformAndroidiOSAndroid
Selection

These questions would be followed by more questions with varied attributes to determine which set is most appealing to respondents.

As you can see, both types of analysis provide insight into customer preference. So, how do you choose the right analysis method to obtain the information you need? 

The MaxDiff model data will reveal the single most preferred attribute and the single least preferred attribute. Conjoint analysis shows how much each attribute influences the final purchasing decision. This means that conjoint analysis is an additive approach (the value of the product is equal to the sum of its parts) and MaxDiff is not.

MaxDiff measures each attribute on the same scale. They can be compared on the same level. Conjoint analysis, being an additive model, is more complex. Comparisons are not direct between attributes but the utility of each attribute within a set.

Take a look at our examples and comparison between MaxDiff and conjoint analyses. Which insights will help you answer your question or work towards your goal?

If you’re planning to launch a new product or update an existing one, a conjoint analysis will provide valuable insights into how sets of attributes are perceived by your potential customers.

If you’re looking for feedback on how your customers perceive certain features or attributes of your current product or service, a MaxDiff analysis may be your best bet.

It all comes down to whether you’re looking for information about a single attribute or multiple attributes in a set and how they affect purchase decisions.

Which type of analysis best meets the needs of your research? Keep in mind that you can start with MaxDiff and then extend your study to include a conjoint analysis to gather both types of information.

As always, SurveyMonkey is here to help you with our extensive market research services. Browse through our selection of market research solutions and start learning about your customer preferences. 

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