Interpreting Conjoint Outputs – From Importance Scores to Simulations

You’ve fielded the study and the dashboard is full of numbers. Now what? This post shows how to read attribute importance, level preference, and “what-if” simulations without getting lost in decimals.

Below is an example of what respondents would be exposed to during a conjoint study:

In this conjoint, respondents would be exposed to 14 tasks, and in each task, they would see 4 different full product profiles plus a “None of these” option.

Each full profile consists of 5 attributes, one of which, Configuration, was more compelling to show as a visual.

Outputs
The goal of Conjoint is to help us understand how consumers make purchase decisions by identifying what is important to them when evaluating different products and services. The outputs of Conjoint analysis provide these in a straightforward way:

1. Relative attribute importance: This is a comparison across attributes, to understand how consumers rank the importance of each attribute

2. Relative level importance: This is a comparison within each attribute, with Conjoint identifying the importance of each level (variation) relative to the others within an attribute

3. Simulations: Ability to test what-if scenarios by combining different attributes and levels to see which ones result in the highest preference share.

In the example below, Price was the most important attribute, followed by Hardware and Priority Reservations.

Within Price, $9.99 was the most preferred level.

Conjoint analysis is a powerful tool for understanding consumer decision making. By identifying what consumers find important and the tradeoffs they are willing to make, we can make more informed decisions on how to design or redesign products and how to price them to be compelling offerings in the market.