GC

Generic Conjoint

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

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

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

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

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

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 where to use it.


Complete solution for pricing research

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.

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.

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.

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.

AB
Concept testing

A/B Test

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

MT
Concept testing

Monadic Test

Compare performance of concepts or products though focussed testing

PCT
Concept testing

Product Concept Test

Determine the best concepts through focussed testing

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.

DIY

DIY Experimental Design

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

FST

Free Survey Tool

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