Suggested search

Looking for a free online survey tool?

Conjoint.ly offers a general survey tool with standard question types, randomisation blocks and multilingual support. Always free.

Get started for free
Request consultation

Do you need support in running a pricing or product study? We can help you with agile consumer research and conjoint analysis.

Request consultation
AB

A/B Test

A/B Test is an experimental method for comparing the performance of two stimuli. This experimental method exposes respondents to each stimuli individually to allow for focussed comparison between the two stimuli. These stimuli may be anything from claims, to packaging, to two different products. A/B testing can be used for:

  • Determining which ad is more effective at increasing brand awareness
  • Finding which packaging will increase purchase intent
  • Deciding between two claims

Use A/B Test to perform a focussed comparison between two items.

View case study

The survey flow compares two products on 4 questions.

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 A/B tests

Table of outputs for each question:

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:

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

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 A/B testing on Conjoint.ly

Start by selecting the A/B 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.

While A/B testing is primary designed to compare the performance of two concepts, including the top selling products in your categories will allow you to benchmark your concepts against existing products.

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 split-cell testing.

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 product testing

GC
Range optimisation
Features and claims

Generic Conjoint

Feature and claim selection and measuring willingness to pay for features for a single product.

BSC
Pricing research
Range optimisation

Brand-Specific Conjoint

Feature and claim selection and pricing in markets where product characteristics vary across brands, SKUs, or price tiers.

CT
Features and claims

Claims Test

Test pricing of new and existing consumer goods in a competitive context using elasticity charts, revenue, and profitability projections.

MT
Concept testing

Monadic Test

Compare performance of concepts or products though focussed testing

AB
Concept testing

A/B Test

Perform focussed comparisons between two items to determine which performs better.

TURF
Range optimisation
Features and claims

TURF Analysis Simulator

Conduct automated TURF analysis on any dataset using Conjoint.ly’s user-friendly TURF analysis tool.

FST

Free Survey Tool

General survey with standard question types, randomisation blocks, and image standardisation.

DIY

DIY Experimental Design

Allowing advanced choice modellers to upload their own experimental designs and perform data collection on Conjoint.ly.

BPTO
Pricing research

Brand-Price Trade-Off

Test pricing of new and existing consumer goods in a competitive context using elasticity charts, revenue, and profitability projections.

MD
Range optimisation
Features and claims

MaxDiff Analysis

MaxDiff analysis for robust ranking of flavours of your product by consumer preference; or usage occasions by frequency.

PVS
Range optimisation

Product Variant Selector

Feature and claim selection and pricing in markets where product characteristics vary across brands, SKUs, or price tiers.

GG
Pricing research

Gabor-Granger Pricing Method

Determine price elasticity for a single product and identify revenue-maximising price level.

VW
Pricing research

Van Westendorp Price Sensitivity Meter

The Price Sensitivity Meter helps determine psychologically acceptable range of prices for a single product and approximately estimate price elasticity.