While performing market research, it is critical to collect as much information on customer preferences as possible. One typical approach is to have respondents rate a list of topics from most important to least essential. Yet, another popular technique is MaxDiff analysis, which provides more sophisticated insights into consumer decision-making. In this post, we will define MaxDiff question type, provide examples of how it differs from ranking, and explain why it is an excellent tool for market research.
MaxDiff (also known as Maximum Difference Scaling) is a rating/preference question type, in which respondents are asked to rank attributes according to their importance (for example, most and least significant, most and least appealing, etc.).
Participants are asked to pick the best and worst objects from a list of possibilities in a sequence of questions. Following that, participants selected the “best” and “worst” alternatives from each set, resulting in a score for each item depending on how frequently it was chosen. Researchers can analyse the frequency with which each item is picked as the best or worst option to determine which items people consider to be most significant.
How do you define an attribute? Attributes are the properties of the object, product, brand, service, or advertisement that you are comparing.
How do you define a set? A set of attributes is formed by randomly selecting attributes.
When to use MaxDiff
This type of question can be used as an alternative to standard rating scale results that could lead to the impression that everything matters equally. With MaxDiff, respondents are forced to make choices between options. This results in ratings that show the relative importance of the items being rated based on the choices they make.
Example: A respondent evaluated the following factors as driving customers to purchase a new smartphone:
- Operating System vs Price
- Operating System vs Design
- Operating System vs Performance
- Price vs Design
- Price vs Performance
- Design vs Performance
Respondents who say Operating System is best and Performance is worst inform us of the outcome of five of the six comparisons. We cannot infer a paired comparison between Design and Performance, because we do not know how the respondent compares these two support channels. You can see that the data produced by this question is better than that of a standard ranking question. The tendency of people is much stronger to judge items at extremes than it is to discriminate between items in the middle of the spectrum.
MaxDiff use cases
As a market researcher, incorporating MaxDiff into your surveys may assist you in gaining a deeper knowledge of what is most important to your target audience and making more educated decisions. Let’s look at some real-world instances of how MaxDiff may help you with your surveys:
Product Feature Prioritization
If you want to create a new smartphone app but are unsure which features would be most relevant to your target audience. You may use MaxDiff to show respondents a collection of characteristics and ask them to select the most and least essential ones. You may find the most critical features that your consumers want and prioritize their development by analysing the results.
Brand perception research
Brand perception research is critical for every company that wishes to remain competitive in the market. You may use MaxDiff to identify which brand traits are most essential to your target demographic and how your brand compares to rivals. You may, for example, provide respondents with a list of attributes such as “innovative,” “trustworthy,” and “affordable” and ask them to evaluate the relevance of each. By analyzing the data, you may identify your brand’s strengths and weaknesses and develop strategies to improve it.
Employee Engagement Research
For example, you might provide a list of benefits to employees and ask them to rate them in order of priority. You may find the benefits most important to your employees and prioritize their implementation by studying the data. This can lead to increased employee happiness, which can improve overall company performance.
Two reasons why researchers are using MaxDiff
MaxDiff is very helpful when dealing with complicated or multi-dimensional difficulties that regular rating scales cannot address. For example, if you want to know which aspects of a product are most essential to clients, MaxDiff can help you determine how to rank them. Similarly, MaxDiff can help you determine what inspires customers to make a purchase.
MaxDiff is intended to address two common problems with standard rating scales:
- Poor discrimination between alternatives
- “Yeah-saying” bias
Respondents rank numerous alternatives equally when using standard rating scales, resulting in poor distinction. In addition, some participants provide more positive ratings than others, a phenomenon known as the “yeah-saying” bias. Let’s dive into how using MaxDiff question types in market research can solve the previously mentioned problems with rating scales.
The problem of Poor discrimination between alternatives
Poor discrimination between alternatives is a common problem researchers face when conducting surveys. Multiple options are often rated at a similar level, resulting in a lack of differentiation. As all options are perceived as equally important, it can be difficult to choose.
Respondents tend to give high ratings to multiple alternatives, resulting in poor discrimination between alternatives. This is called “ceiling effects.” Participants do this for a variety of reasons, such as appearing agreeable or avoiding appearing negative. Alternatively, some respondents may simply have a more positive outlook on life, which leads them to give higher ratings.
Example of “ceiling effect” in questions with traditional rating scales
Market researchers face problems discriminating between alternatives. Making informed decisions based on survey data becomes difficult if respondents cannot differentiate between options.
Often traditional rating scales are not able to discriminate between alternatives accurately, and the use of MaxDiff is the right way to overcome this problem. By making respondents choose between the most and least important options, clear differentiation is achieved. MaxDiff also avoids the “ceiling effect” of traditional rating scales.
Customer satisfaction surveys, for example: When a consumer ranks all aspects of a product or service as “very happy” or “very satisfied,” it is difficult to identify areas where improvements may be made. This might give the impression that everything is well when, in reality, there are areas for development.
The problem of Yeah-saying bias
In survey research, yeah-saying bias occurs when respondents tend to agree with statements even if they don’t necessarily believe them to be true. Among the reasons for this bias are:
- Social desire bias, in which respondents may feel pressured to give answers that are socially acceptable
- Acquiescence bias that occurs where respondents agree with statements to avoid appearing confrontational or disagreeable.
In everyday conversation, the word “yeah” is used as an affirmative response. Yes-saying bias can lead to inaccurate survey data as respondents may provide responses that do not reflect their true opinions. In studies aiming to measure attitudes, opinions, or preferences accurately, this can be a challenge.
Example of “yeah-saying” bias
One example of Yeah-saying bias utilizing rating scales is an employee satisfaction survey, in which individuals may prefer to score all aspects of their employment as “excellent” or “very positive” on a 5-point scale, even if there are areas for improvement. This might lead to biased conclusions and make identifying areas for improvement difficult ones.
What are the advantages of MaxDiff?
Besides improving discrimination between alternatives and reduced response bias using MaxDiff has some other advantages:
Easy to understand
MaxDiff is easy to understand for survey participants, which helps to increase response rates and decrease survey dropout rates. MaxDiff, unlike certain survey question types, is straightforward and intuitive, making it less daunting for respondents.
A clothes retailer, for example, may wish to know which characteristics are most significant to buyers when selecting apparel brands. They may use MaxDiff to offer clients sets of variables such as price, style, and quality and ask them to choose the most and least significant. This would offer the organization simple insights into what people value while shopping for clothing.
Lastly, MaxDiff is a versatile question type that may be applied in a number of situations. It may be used to compare and rate different product characteristics, as well as to understand consumer preferences and identify significant drivers of customer happiness.
A software business, for example, may wish to know which elements of their product are most valuable to customers. They may utilize MaxDiff to show clients sets of features like ease of use, speed, and security and ask them to choose the most and least essential aspects. This would provide the organization with vital information about what clients prioritize while using their program.
Provides actionable insights
The results of MaxDiff may be used to generate a clear ranking of things based on their relative attractiveness. This score may be used to make educated business decisions such as deciding which product features to prioritize, which goods to launch, and which pricing strategies to employ.
More precise and dependable data
As compared to other types of questions, such as ranking or rating scales, MaxDiff can give more accurate and dependable data. MaxDiff requires respondents to make a sequence of selections among various items, which implies that each item is compared to every other item on the list. As a result, respondents’ perceptions of the alternatives are more accurate and complex. MaxDiff is also less susceptible to response and acquiescence bias, which can impair the accuracy of other types of queries.
MaxDiff vs. Rating scales (matrix questions)
In general, how do max-diff questions and rating scales (matrix questions) compare? First we need to understand what the matrix is in order to make a comparison.
The rating scale or matrix is a closed-ended survey question used for comparing responses to specific features, products, or services. Inside Survalyzer rating scale questions are known as Matrix questions and usually involve asking participants to rate abstract concepts, such as satisfaction, ease of use, or likelihood of recommendation.
Rating scales are a type of matrix question, in which rows represent a group of questions on a specific topic (features of a product or service), while columns represent the corresponding answer options (like numbers from 1 to 5).
This type of questions, however, have their own drawbacks, such as being too biased, difficult for respondents to assess, lack of discrimination, generating ordinal data that restricts analysis, and not allowing tie results.
MaxDiff analysis enables researchers to compare many variables simultaneously when they understand the issues and limitations of various survey question types, especially rating scale questions. Comparing to traditional rating scales MaxDiff questions also:
- show greater discrimination between items and between replies obtained for each item when compared to regular rating scale questions.
- MaxDiff surveys produce more trustworthy data because they are easy to complete
- With a MaxDiff survey, you ask respondents to pick rather than express their strength or preference numerically. As a result, there is no room for bias in scale usage.
We have provided a detailed comparison between MaxDiff and Rating scales in the table below:
|Definition||A Survey question type that present alternatives and ask respondents which are the most and least important||A survey question type that asks respondents to rate an attribute or item on a numerical scale|
|Benefit||Provides clear differentiation between alternatives and avoids “yeah-saying” bias||Easy to administer and interpret data|
|Discrimination||High discrimination, allows for accurate ranking of alternatives||Low discrimination, leads to poor differentiation between alternatives|
|Response bias||Reduces response bias by forcing respondents to make trade-offs||Prone to response bias, as some respondents tend to give higher ratings than others|
|Suitability||Ideal for complex or multi-dimensional issues||Suitable for simple issues or when a quick survey is needed|
|Examples||Determining the most important features of a product or service, identifying customer needs or preferences||Measuring customer satisfaction with a recent purchase, assessing employee job satisfaction|
|Survey Length||Longer survey length due to the presentation of sets of alternatives||Shorter survey length due to the simplicity of rating scales|
|Analysis||Requires more complex analysis methods such as latent class analysis or hierarchical Bayes models||Simple analysis methods such as mean or median calculation can be used|