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

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 Conjointly, 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 an A/B Test on Conjointly

Step 1

Start by selecting the A/B Test experiment type from the list of experiment types. Then, insert the name for the first item (not displayed to respondents) and insert what will be shown to respondents. You can insert an image, description, or short video.

Step 2

Insert the name and respondent display for the second item

Step 3

Import questions from another experiment. If you have a previous Conjointly experiment such as a Monadic Test or Concept Test you can import questions from these experiments to quickly set up an A/B Test. You can skip this step as you will be able to add new questions later on.

Step 4

Review both stimuli, including adding and modifying 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 can also add or replace a stimulus in this step

While an A/B test is primarily 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.

Step 5

Add or 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.

Step 6

Customise the rest of the experiment. You are given complete control over the survey flow respondents will see.

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 lets you choose between sequential testing and random monadic.

Step 7

Preview the survey as a participant to test your setup and prepare to launch.

Complete solution for product testing


Survey Tool

Fully-functional online survey tool with various question types, logic, randomisation, and reporting for unlimited number of responses and surveys.

Range optimisation
Features and claims

Generic Conjoint

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

Range optimisation
Features and claims

MaxDiff Analysis

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.

Features and claims

Claims Test

Efficiently test up to 300 product claims on customer appeal, fit with brand, and diagnostic questions of your choice.

Pricing research
Range optimisation

Brand-Specific Conjoint

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

Range optimisation

Product Variant Selector

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.

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.

Pricing research

Gabor-Granger Pricing Method

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

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.

Concept testing

A/B Test

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

Concept testing

Monadic Test

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.

Concept testing

Ad Test

Test the effectiveness of your advertisements using a comprehensive research method

Concept testing

Digital Asset Test

Test the effectiveness of your digital advertisements in an online environment

Concept testing

Image Test

Test the effectiveness of your images in an online environment

Concept testing

Brand Name Test

Efficiently test potential brand names to identify the best one to represent your business

Concept testing

Business Name Evaluator

Efficiently evaluate potential business names to identify the best one to represent your brand

Concept testing

Domain Name Likability Check

Efficiently test potential domain names to identify the perfect new home for your brand

Concept testing

Product Description Test

Validate your product descriptions before release

Concept testing

Product Name Test

Efficiently test potential product names to identify the best one to reflect your brand

Concept testing

Product Concept Test

Determine the best concepts through focussed testing

Concept testing

Video Test

Test the effectiveness of your videos in an online environment

Concept testing

Idea Screener

Perform preliminary screening to find your best product ideas.

Concept testing

Package Test

Discover the best packaging for your products

Range optimisation
Features and claims

TURF Analysis Simulator

Conduct automated TURF analysis on results of any Conjointly experiment (or an outside dataset) using this user-friendly TURF analysis tool.


DIY Experimental Design

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

Features and claims
Feature and Pricing Suite for SaaS

Kano Model

Ensure product-market fit and maximise your user acquisition and expansion, by differentiating software features according to your users' needs.