Brand-specific conjoint is a discrete choice method for markets where potential product characteristics vary across brands or SKUs (it is commonly the case in FMCG, telco, home appliances, and tech). Technically known as choice-based alternative-specific/labelled conjoint design, it is used for:
- Feature selection for new or revamped products.
- Pricing your product, taking into account competitors' offerings and pricing.
- Testing branding, packaging and advertising claims.
Main outputs of Brand-Specific Conjoint
Share of preference simulation
Estimate share of preference based on customers' revealed preferences.
You can run "what-if" scenarios to see how consumers will behave if you change features of your product. Learn more about preference share scenario modelling.
Understand amount of interest in new product launches
Through preference share simulations.
Run "what-if" scenarios to measure the amount of interest in new product ideas, compare with current SKUs and assess sources of business (i.e. if you are sourcing from competitors or cannibalising your product line).
Price elasticity of demand
Estimate consumers' sensitivity to price changes and its effect on sales.
Run "what-if" scenarios to assess consumers' reactions to price increases or decreases in the context of your other SKUs and competitor offerings. Project revenue levels at different pricing options to select optimal pricing for your new or existing SKUs.
Relative performance of brands
Performance of different brands, considering their possible variants.
Conjointly estimates how strongly customers prefer different brands of products, taking into account the different variants (combinations of features and prices) presented to them. In this example, Landrange and Ladina tend to have more appealing variants than Kea.
Importance of product characteristics and performance of different features.
Conjointly estimates how important each attribute is relative to the other attributes in customers' decision-making process (called relative importance of attributes).
Each level of each attribute is also scored for its performance in customers’ decision-making (relative performance of levels). In this example, Hybrid is preferred over Petrol for this brand.
Ranked list of product constructs
List all possible level combinations and rank them by customers' preference.
Conjointly forms the complete list of product constructs using all possible combinations of levels. They are ranked them based on the relative performance of the levels that they combine. 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 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.
Learn more about conjoint analysis
What is Conjoint Analysis?
Conjoint analysis is one of the most widely-used and powerful quantitative methods in market research. Discover how it works and when to use it.
Complete solution for pricing 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.