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)

People make choices given the current landscape of available options. If you believe that your experiment covers the most important product attributes and common levels, our market simulation tool can predict shares of preference of the different offerings available on the market. One way to use it is to compare two scenarios:

  • “Before”: List out the products currently available in the market by specifying the levels for each of them. If two products that are present in the market now have identical levels, enter them separately
  • “After”: Add your new product with the levels you intend for it. The simulator will predict the market share of your new product in the existing competitive environment using respondents’ revealed preferences

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.