Generic conjoint is the most common type of discrete choice experiments.
Technically known as choice-based generic/unlabelled conjoint design, it is used for:
- Feature selection for new or revamped products.
- Marginal willingness to pay for specific features relative to other features.
- Pricing your product, particularly in commoditised markets, where product characteristics do not vary substantially by brand or SKU.
- Testing branding, packaging and advertising claims.
Main outputs of Generic Conjoint
Relative importance of attributes
Do people care about price, data, international calls, or text messages?
Conjoint.ly estimates how important each attribute is relative to the other attributes in customers’ decision-making process.
For example, imagine you are investigating price (specifically $30, $50, or $70 per month) and SMS inclusion (300 messages or Unlimited) for mobile phone plans. If the variation in data inclusions sways customers three times as much as variations across international minutes inclusions, the relative importance score of data inclusion will be thrice as high as that of international minutes inclusion.
Relative value by levels
Is 300 min of international calls much better than 90 min?
Each level of each attribute is also scored for its performance in customers’ decision-making. For example, if low price ($30/mo) is seen favourably (relative to the other pricing options), it will show as positive. High price ($70/mo) can be least favourite and will be showing up as negative, while moderate price ($50/mo) can be in the middle showing as either low positive or low negative. The sign of the performance score of each attribute is only relative to the other options respondents faced - $70/mo can be negative when compared with $30/mo and $50/mo, but might show up as positive relative to $90/mo.
Marginal willingness to pay
How much are consumers willing to pay for a feature?
For experiments where one of the attributes is price, Conjoint.ly will calculate how much each of the levels is worth to customers. For example, inclusion of unlimited text messages (as opposed to the ‘baseline’ of 300 messages per month) can be shown to be as effective in increasing buyers’ uptake as lowering the price by $28. Thus, marginal willingness to pay is about substitution of a feature for a price change.
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.
Ranked list of product constructs
List all possible level combinations and rank them by customers' preference.
Conjoint.ly 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.
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.
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.
MaxDiff analysis for robust ranking of flavours of your product by consumer preference; or usage occasions by frequency.
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 advertisements using a comprehensive research method
Test the effectiveness of your digital advertisements in an online environment
Conduct automated TURF analysis on results of any Conjoint.ly 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 Conjoint.ly.
Ensure product-market fit and maximise your user acquisition and expansion, by differentiating software features according to your users' needs.