The Monadic Test is an experimental method that exposes respondents to individual stimuli to allow for focussed testing of products and packages. Monadic tests are best used when asking respondents multiple questions about many concepts or products. Monadic testing is used for:
- Finding consumer attitude towards potential product concepts
- Testing attitude towards different packaging
- Rating concepts on several different attributes
Use Monadic Test to determine the best concepts or products.View case study
The survey flow consists of 7 concepts with respondents being asked 4 questions for each concept.Take example survey
Monadic Tests can be automatically translated to more than 30 languages.View translations
Bring your own respondents or buy quality-assured panel respondents from us.Get respondents
Main outputs of monadic tests
Table of outputs for each question:
Summary metrics for each question included in the monadic for each stimulus for easy comparison.
At-a-glance summary allowing for quick comparison between stimuli with the option of drilling down into detailed metrics of any question for any stimulus.
Detailed question output for a particular stimulus:
Displays detailed outputs for an in depth look into responses for a particular question.
With Conjointly, we provide detailed statistics into each question for each stimulus if you are interested in the nitty gritty details such as distribution of responses, medians, ranges.
Segmentation of the market
Find out how preferences differ between segments.
With Conjoint.ly, you can split your reports into various segments using the information collected automatically by our system, respondents' answers to additional questions (for example, multiple choice), or GET variables. For each segment, we provide the same detailed analytics as described above.
How to set up Monadic testing on Conjoint.ly
Start by selecting the Monadic testing experiment type from the list of experiment types. To get started with the set up, create a list of stimuli. You can Import a text list or type it in manually to populate the initial list of stimuli.
After importing the list of stimuli, you can then add, remove, or modify each stimuli individually. This includes adding new views. Views allow you to insert different representations of the concept in the diagnostics question. You can add images, long descriptions, use fancy formatting and lots more.
You will see all your stimuli displayed as columns by default. If you have a long list of concepts and only a few views, you can flip around the table to display stimulus as rows instead of columns.
After completing the table of stimuli, you can continue customising the rest of the experiment. You are given complete control over the type of monadic testing your experiment will run.
We strongly recommend that you also include your best-selling product, the best-selling product in your current market, and the best-selling product of your top competitor to benchmark your concepts against products already on the market.
Modify the following parameters to change the flow of the survey:
- Randomise questions allows the system to vary the order of the questions inside the monadic block in order to reduce respondent bias.
- Maximum number of questions to display per respondent lets you control the survey length by limiting the number of questions for each respondent.
- Maximum number of stimuli per respondent helps you get more detailed feedback for each concept without introducing survey fatigue.
- Sequence of questions lets you choose between sequential testing and random monadic.
To modify the questions that will be asked of respondents, you can adjust the questions found in the Monadic block section. To add questions to this block, simply drag and drop any of the additional question types into Monadic block.
Preview the survey as a participant to test your setup and prepare to launch.
Complete solution for pricing research
Feature and claim selection and measuring willingness to pay for features for a single product.
Feature and claim selection and pricing in markets where product characteristics vary across brands, SKUs, or price tiers.
Test pricing of new and existing consumer goods in a competitive context using elasticity charts, revenue, and profitability projections.
Perform focussed comparisons between two items to determine which performs better.
Conduct automated TURF analysis on any dataset using Conjoint.ly’s user-friendly TURF analysis tool.
General survey with standard question types, randomisation blocks, and image standardisation.
Allowing advanced choice modellers to upload their own experimental designs and perform data collection on Conjoint.ly.
MaxDiff analysis for robust ranking of flavours of your product by consumer preference; or usage occasions by frequency.
Determine price elasticity for a single product and identify revenue-maximising price level.