Brand-Specific Conjoint

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:

  • Feature selection for new or revamped products.
  • Pricing your product, taking into account competitors' offerings and pricing.
  • Testing branding, packaging and advertising claims.

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Share of preference simulation for feature selection

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.

Share of preference simulation for NPD

Understand amount of interest in new product launches

(through preference share simulations)

Run "what-if" scenarios to measure amount of interest in new product ideas, compare with current SKUs and assess sources of business (i.e. if you are sourcing from competitors or cannibalising your product line).

Price elasticity of demand through preference share calculation

Price elasticity of demand

(estimate consumers' sensitivity to price changes and its effect on sales)

Run "what-if" scenarios to assess consumers' reactions to price increases or decreases in the context of your other SKUs and competitor offerings. Project revenue levels at different pricing options to select optimal pricing for your new or existing SKUs.

Relative performance of brands

(Performance of different brands, considering their best variants) 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.

for the brand

Partworth utilities

(importance of product characteristics and performance of different features) 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.

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.