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How to use best-worst scaling to make better decisions

Use best-worst scaling, or MaxDiff analysis, to learn what’s important to your customers.

Best-worst scaling, also known as MaxDiff (maximum difference) analysis, is useful when gauging preferences on messaging, brand names, product features, and more. It’s easy to use and offers clear insights into what customers truly like—and dislike. Let’s take a look at best-worst scaling and how you can use it to make better decisions for your business.

Best-worst scaling is a type of survey research conducted to understand the relative importance of attributes such as product features, packaging, messaging, etc., to your target market. By identifying what consumers really want most, you can maximize your business investments of time and capital on efforts that will appeal to your target market.

Best-worst scaling asks respondents to choose among several options at once—selecting only the best and worst options. This type of survey question allows you to collect the information you are seeking quickly and definitively. There’s no guesswork about what respondents mean when they choose a score near the middle of the range as in Likert scales or rating scale questions. Your respondents simply choose the most and least important options to them. MaxDiff questions can be asked in a single survey or as part of a longer questionnaire.

Two terms are frequently used in best-worst scaling:

  1. Attribute: a single property, item, or feature for measurement
  2. Set: a group of attributes displayed to participants

How to conduct your best-worst scaling study: 

  1. Once you’ve determined the attributes you’re testing, you’ll generate your experimental design
  2. Create multiple versions (usually 6 or more) of best-worst scaling questions to test your chosen attributes, creating sets using randomization, item balance, paired balance, or connectivity.
    1. Each attribute should appear at least three times
    2. Each attribute should appear the same number of times
    3. Each attribute should appear with the other attributes the same number of times (e.g. each attribute appears with each other attribute twice)
  3. Distribute your survey to your target market using SurveyMonkey Audience or your own contact list. 
  4. Collect and analyze results. 
  5. Present your findings to stakeholders with key findings and recommendations.

Best-worst scaling is very similar to conjoint analysis for determining respondents’ preferences for various items or features. However, MaxDiff analysis is easier to use and less refined than conjoint analysis. 

Here are examples of the two methods for comparison:

Conjoint analysis studies mimic shopping trips, where participants review products, features, attributes, and prices to make purchase decisions. The analysis is complex and considers multiple factors.

For example, you could ask “If you were in the market for a new smartphone, which of the following would be most appealing to you?” Respondents would compare brand, price, storage and more. 

In best-worst scale survey questions, respondents are asked to choose the least and most important factors within the answer options. Data analysis is faster and cleaner.

For example, you could ask, “If you were in the market for a new smartphone, please indicate the feature that would be the most important in your purchase decision and which feature would be the least important.” You would then list various features like camera, display, face ID, and more–and respondents would decide which feature was most and least important. 

Most ImportantLeast Important
◻️Selfie camera◻️
◻️Oversize display◻️
◻️Stereo sound◻️
◻️Face ID◻️
◻️Battery life◻️
◻️Multiple color options◻️
◻️Price◻️

As you can see in the examples, both methods are seeking what customers prioritize when purchasing a smartphone. The best-worst model data will reveal the most preferred feature and the least preferred feature. Conjoint analysis shows how much each feature influences the final purchasing decision. 

Often, these methods are used together to create a more detailed picture of what customers want and are willing to pay for.

In addition to the ease of creation and data analysis, best-worst scaling has several advantages.

Best-worst scaling questions are easy for respondents to answer. They are, in effect, simulating real-world behaviors in making choices and trade-offs, eliminating options that they don’t feel strongly about. Answering several of these questions makes the strength and importance of each choice known.

By forgoing a ranking scale, best-worst scaling avoids biases from cultural differences—some cultures have associations with different numbers—or perceptions of ratings. Respondents may also have list order bias, where they indicate that all features are equally important. When bias occurs, the business may waste resources on improvements or features that aren’t truly important to customers.

MaxDiff survey data is well suited for creating statistical models. These models make it possible to quantify preferences and understand what your target market values.

Determining what features are deemed best and worst by your customers provides you with actionable insights for product features and improvements, new products, and obsolete product features. Use this data to give your customers what they truly want and value. This will result in increased customer satisfaction overall.

As with any process, there are some downsides to best-worst scaling.

Depending upon the number of attributes you are testing, it may take more time to take your survey. Shorter surveys tend to have higher response rates. 

MaxDiff analyses measure respondents’ preferences based on the attributes presented in relation to each other. This does not account for whether the attributes offered on the survey are good or bad based on an absolute perspective.

Best-worst scaling is typically used to optimize features, not the product as a whole. For example, your MaxDiff survey may show a definite preference for one feature, but in reality, customers may not be willing to pay a higher price for the feature. To dig deeper into your results, perform a conjoint analysis to test the product as a whole.

Is the level of preference indicated for an attribute conditional upon the alternatives it is compared to? This is the context effect. For example, if you’re conducting a best-worst scaling smartphone study and your attributes are Samsung, Nokia, Google, Sony, and Apple, respondents are probably thinking about Google in terms of hardware. But if you’re doing a study that includes Google and Yahoo in its attributes, people will more likely think of Google in terms of its search engine.

It’s important to be aware of the context effect even though it is impractical to try to avoid attributes because of this possibility.

Standard rating questions can have user scale bias, scale meaning bias, and lack of discrimination between answer choices. MaxDiff surveys eliminate the issue of bias because participants are asked to choose their preference, not rank or rate.

As we mentioned in our earlier description, best-worst scaling is closely related to conjoint analysis. Conjoint analysis is used to determine how different features would affect a purchase decision, while MaxDiff asks what features are most important to them. For a clear picture of customer overall preference, use best-worst and conjoint analysis together to combine and compare the results.

There are many uses for best-worst scaling. Primarily, it is used to identify preferences among a list of attributes in a set. It’s a fast and easy way to obtain important information, especially if you have a small marketing budget. Best-worst scaling is a very effective method for determining what your target market really wants.

Best-worst scaling surveys are valuable tools in product development of upgrades. If your product sales are flagging and you’ve decided you need to update the product, a MaxDiff survey can help. Rather than deciding internally what features you think your customers want, present your features (attributes) in sets and find out what is truly important to them.

Is the name you’re considering for your product appealing to your target market? Perform best-worst scaling and analysis to obtain information about which names are preferred by customers.

To take name testing further, use best-worst scaling to find out how the names you’re considering for your new product fit with your target market’s perception of your brand.