You can ask people to select preferred product attributes or blog topics or ask people to rank them. One of the challenges of surveying users is that what they say doesn’t necessarily match what they do.
Conjoint analysis is an approach designed to mitigate this effect. Rather than asking users to focus on an isolated attribute, a conjoint survey presents users with attribute bundles. A bundle could include screen size, color, camera resolution, and price. In a conjoint survey, users are given a series of choices between two (sometimes more) bundles of specific attributes.
For example, the first comparison might be bundle a) 5.6” screen size, gray color, 12mp camera, $700 or b) 4.5” screen size, black color, 16mp camera, $500. The user selects a or b and then is given another comparison, makes another choice, and so on. The total number of comparisons will depend on the number of attributes being tested, the different options for each attribute, and the number of user respondents.
Selecting bundles from different comparisons, as described, is a choice-based conjoint analysis. Another conjoint method is rank-based. Users rank several bundles from most preferred to least preferred.
An analysis of conjoint results reveals how much each attribute impacts the users preferences and how much each of the attribute options impacts the preference (either positively or negatively). When price is one of the attributes, conjoint provides the additional advantage of understanding user price sensitivity. By forcing choices between bundles of features, conjoint gets at the underlying, implicit value users place on the different attributes, which is likely more accurate than responses from a user that is directly asked about each attribute separately.
Conjoint is often applied to features sets of products (software, hardware, microwave, power drill, etc.). It can also be applied in other ways. For example, conjoint can be applied to online content to understand what formats, topics, lengths (summary or deep dive) that users might prefer.