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.

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Relative importance of attributes

(do people care about price, data, international calls, or text messages?) 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 price sways customers three times as much as variations across SMS inclusions, the relative importance score of price will be thrice as high as that of SMS 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 they willing to pay for a feature)

For experiments where one of the attributes is price, 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 $14. 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) 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 example: Segment 1 (~27%) cares more about SMS, segment 2 (~73%) care more about data. Both are price-conscious.

Segmentation of the market

(find out how preferences differ between segments)

With, 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.