MaxDiff (also known as Maximum Difference Scaling or Best–Worst Scaling) is a statistical technique that creates a robust ranking of different items, such as product features. MaxDiff is an alternative to conjoint analysis from which the respondent has to indicate which feature is most important or most desirable, and which is least important or desirable. Conjointly’s novel robust approach to MaxDiff allows for:
- Testing of multiple attributes in the same survey
- Brand-Specific combinations of attributes for when each brand is substantially different (to enable that, first create a Brand-Specific Conjoint and then convert it into the MaxDiff variety)
- Simulation of preference shares, at a highly indicative level
Traditionally, MaxDiff treats each product as an individual item, whilst conjoint treats products as a combination of attributes and levels. As such, the conjoint analysis produces rankings for particular products by summing the preference scores for each attribute level of a product, whilst MaxDiff produces rankings by polling the respondents directly.
Main outputs of MaxDiff Analysis
Relative value by levels
How do customers rank potential phone colour options?
Each level of each attribute is scored for its performance in customers’ decision-making. In our example, navy is the most favourable colour and is displayed as positive. Yellow is the least preferred colour and therefore displayed as negative. It's important to remember that the performance score of each attribute is relative to the other levels shown to respondents. For instance, the colour red will only be shown as negative when compared against a specific set of colours (levels) — testing red against a different range of colours could yield a positive result.
Ranked list of product constructs
List all possible level combinations and rank them by customers' preferences.
Conjointly forms the complete list of product constructs using all possible combinations of levels. They are then ranked based on the relative performance of the levels combined. This module allows you to find the best product construct that your customers will prefer over others.
Segmentation of the market
Find out how preferences differ between segments.
With Conjointly, you can split your reports into various segments using the information our system collects: respondents' answers to additional questions (for example, multiple-choice), simulation findings, or GET variables. For each segment, we provide the same detailed analytics as described above.
Analyse with TURF Simulator
Conduct TURF analysis on MaxDiff data using the TURF Analysis Simulator.
TURF analysis aims to find the combination of items that appeals to the largest proportion of consumers. Conjointly makes conducting TURF analysis easy by letting you export your MaxDiff data directly into Conjointly's TURF Analysis Simulator with a single click.
Preference share simulations
View simulations of preference shares for your product with the Preference Share Simulator.
With Conjointly, you can simulate shares of preference and volume projections for different product offerings, including those that are available in the market. Learn more about using the simulator for MaxDiff.
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:
|When do we use it?||To create a ranking for different alternatives, such as:|
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.
What is the benefit of performing TURF Analysis on the results of a MaxDiff experiment?
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!
Can I use MaxDiff in combination with Van Westendorp analysis?
How do I make brand-specific combinations of attributes?
Complete solution for features and claims research
Fully-functional online survey tool with various question types, logic, randomisation, and reporting for unlimited number of responses and surveys.
Feature and claim selection and measuring willingness to pay for features for a single product.
MaxDiff (aka Maximum Difference Scaling or Best–Worst Scaling) is a statistical technique that creates a robust ranking of different items, such as product features.
Efficiently test up to 300 product claims on customer appeal, fit with brand, and diagnostic questions of your choice.
Pricing, feature and claim selection in markets where product characteristics vary across brands, SKUs, or price tiers.
Identify winning product variants from up to 300 different ideas (e.g., designs, materials, bundle options) on customer appeal, fit with brand, and diagnostic questions of your choice.
Test pricing of new and existing consumer goods in a competitive context using elasticity charts, revenue, and profitability projections.
Determine price elasticity for a single product and identify revenue-maximising price level.
The Price Sensitivity Meter helps determine psychologically acceptable range of prices for a single product and approximately estimate price elasticity.
Perform focussed comparisons between two items to determine which performs better.
Ask respondents to evaluate product concepts and digital assets one-by-one to get a read of their preferences and perceptions with various question types.
Test the effectiveness of your digital advertisements in an online environment
Efficiently evaluate potential business names to identify the best one to represent your brand
Efficiently test potential images to identify the best one for your ads
Efficiently test ad copy to identify the best one for your campaign
Efficiently test potential brand names to identify the best one to represent your business
Efficiently test potential domain names to identify the perfect new home for your brand
Efficiently test print ads to identify the best one for your campaign
Efficiently test out-of-home ads to identify the best one for your campaign
Efficiently test potential product names to identify the best one to reflect your brand
Efficiently test potential business card designs to identify the best one for your business
Efficiently test potential logos to identify the best one for your ads
Efficiently test packages to identify the best one for your product
Efficiently test product concepts to identify the best one for your business
Efficiently test graphic designs to identify the best one for your brand
Conduct automated TURF analysis on results of any Conjointly experiment (or an outside dataset) using this user-friendly TURF analysis tool.
Allowing advanced choice modellers to upload their own experimental designs and perform data collection on Conjointly.
Ensure product-market fit and maximise your user acquisition and expansion, by differentiating software features according to your users' needs.