MaxDiff Analysis

How it works

For each MaxDiff question, Conjointly asks respondents to select the best and worst options from a random selection of options. Each respondent is asked to complete 12-16 of these questions.

The main output of the MaxDiff survey is a bar chart displaying the average preference scores, representing the relative preference for each item. An alternative output of the MaxDiff survey is a bar chart showing the best/worst options percentages and net percentage. This output shows the number of times an attribute level was selected as part of the best option and the number of times an attribute level was chosen as part of the worst option, divided by the number of times it was presented to respondents and expressed as a percentage. The net percentage is simply the best per cent - worst per cent and is another way of measuring respondents’ preferences for the features.

For example, let’s say we performed a MaxDiff survey on soda flavours:

Cola was presented to respondents in 100 trials. It was selected as the best option 76 times and the worst option 6 times. Then, to calculate the outputs:

• Best percent: 76/100 = 76%
• Worst percent: 6/100 = 6%
• Net percent: 76%-6% = 70%

Now, compared to another flavour, Kiwi, which has a best per cent of 11%, a worst per cent of 70%, and a net per cent of -59%, we can infer that respondents prefer Cola to Kiwi.

With Conjointly, you can perform a MaxDiff survey on flavour, pack size, format, and any other attribute you may be looking to test in the same experiment. The preference scores and percentage chosen outputs are presented separately for each attribute.

Setting up on Conjointly

To set up a MaxDiff survey on Conjointly, you will need to prepare a list of attributes and levels you wish to test. Then, insert these attributes and levels in the experiment setup screen.

Conjointly also allows you to present respondents with additional questions.

Only one MaxDiff block may be presented to respondents, but any number of additional questions may be added.

Differences between MaxDiff and Conjoint Analysis

Both techniques are similar in presenting respondents with a set of options and asking them to choose, which is a method based on consumer trade-off decisions that realistically mimic decisions respondents would make in real life. However, there are some core differences in approach and usage between MaxDiff and Conjoint experiments:

	MaxDiff	Conjoint
Respondent View
When do we use it?	To create a ranking for different alternatives, such as: Features of a product by importance, Aspects of brands by customer satisfaction, Flavours or variants of products by consumer preferences, Usage occasions by frequency.	Feature selection for new or revamped products, Marginal willingness to pay for specific features, To test branding, packaging and advertising claims, To find the optimal pricing of products while considering competitor offerings.
Similarities	Both techniques are advanced analytical tools constructed based on consumer trade-off decisions. Both techniques result in interval-scaled utility scores, which can effortlessly be transformed into ratio-scaled probabilities so they can be hierarchised. Both techniques are discrete-choice experiments.
Differences	Respondents are asked to choose both their favourite and least favourite alternatives. Results are scored by directly polling the respondents.	Respondents are prompted to make a single choice across a set of alternatives for their most preferred option. Results are calculated by summing all individual-level scores.
Example	A car manufacturer wants to discover which car colour is the most preferred among consumers. MaxDiff analysis is used to provide a robust ranking of the colours according to consumer preferences.	A car manufacturer wants to discover how much each attribute of a car contributes to a consumer's buying decision. It also seeks the optimal combination of these components that will increase its market share.

Using MaxDiff Analysis in conjunction with other research tools

TURF Analysis based on MaxDiff results

TURF Analysis is a natural extension of MaxDiff, as it allows you to identify which combination of attributes will “reach” the most amount of consumers, where reach is defined as the percentage of respondents for whom at least one of the attributes in a particular combination is their most preferred.

When considering launching multiple products/features, the powerful TURF Analysis Simulator lets you use the results of your MaxDiff experiment to identify the combination of items that appeals to the largest proportion of consumers with a single click.

Preference Share Simulator based on MaxDiff results

The Preference Share Simulator allows you to simulate preference shares for different product offerings, including those that are available in the market.

Caution must be exercised when using the preference share simulations with MaxDiff results because the simulator aims to predict what products people will choose, whereas MaxDiff scores also takes into account the ones they want the least. Therefore, the simulator may underestimate the preference shares of the products that some people want the least.

Feature Placement Matrix by combining MaxDiff Analysis and Van Westendorp

When you include Van Westendorp (VW) as additional questions in your MaxDiff experiment, a Feature Placement Matrix will be automatically generated for each VW question. The matrix helps you compare and classify features by both their importance (MaxDiff scores) and users’ willingness to pay (VW “optimal” price).

To generate the Feature Placement Matrix, a minimum sample size of 40 respondents is required. This is because the VW analysis itself requires a minimum of 8 respondents to compute the optimal price point. Additionally, for each feature, the VW “optimal” price is calculated using the top 20% of respondents with the highest MaxDiff scores for that specific feature (8/20%=40).

Learn more about how the Feature Placement Matrix works.

Brand-Specific MaxDiff Analysis

To create brand-specific combinations of attributes, first, create a Brand-Specific Conjoint and assign each attribute being tested to the appropriate brand. After the experiment is saved, it can be converted to MaxDiff under Advanced Survey Options.