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:
Conjoint.ly 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. Telco A is the strongest performer, while Telco E lags behind.
Conjoint.ly estimates how important each attribute is relative to the other attributes in customers’ decision-making process. This is called relative importance of attributes. For example, in this test for Telco A, coverage is approximately three times as important as price (given the range of price levels tested): 54 points vs. 16 points.
Each level of each attribute is also scored for its performance in customers’ decision-making. This is called relative performance of levels. For example, for Telco A there is clear preference for nationwide coverage over metro areas, and for metro areas over downtown-only coverage.
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:
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.
With Conjoint.ly, 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.