We are constantly making choices, some conscious, but most unconscious. Standard methods with traditional question scales are unable to reveal what component of a product or concept drives its preference.
Most of these scales don’t simulate real-life conditions, which always include some type of situational trade-offs. To simulate realistic consumer decision making, the most precise approach is through either a conjoint analysis or MaxDiff analysis. Gradient has designed countless experiments within a wide range of product types and sectors.
Conjoint analysis is a technique for quantifying how the attributes of products and services affect preference. It is typically used to help identify the optimal design of products, messaging, and pricing.
A conjoint compares combinations of attributes. Each respondent is shown multiple lists with varying combinations of the attributes.
Across multiple lists of choices from hundreds of respondents, a statistical model identifies precisely how much each attribute and level contributes to making the decision.
The attribute importance is the proportion that a larger category or attribute (e.g. genre), contributes to driving preference.
The level importance is the proportion that a specific level within an attribute (e.g. within the genre attribute, drama and action) contributes to preference.
A showcase of the potential and versatility of discrete choice experiments.
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