What is conjoint analysis?

The main objective of conjoint analysis is to break down the total value that customers perceive in a product or service. This allows you to:

• Identify the relative importance of each attribute in the purchase decision.

• Calculate the price elasticity of a specific solution.

• Estimate purchase intention for different combinations of attributes and prices, including competitors’ options.

A practical example: business travelers and tourists
Imagine an airline wants to evaluate how much value its customers assign to three attributes: seat comfort (regular or wide), flight duration (3 or 5 hours), and ticket price ($400 or $700). To do this, all possible combinations of these variables are created.

Each combination is shown to respondents as a card, and they are asked to rate their purchase intention on a scale from 1 to 10. With these ratings, you can estimate the utility that each segment (for example, business travelers) assigns to each attribute.

The results may reveal, for example, that for business travelers the most relevant attribute is price, followed by flight duration. Meanwhile, seat comfort has marginal importance. This helps the company adapt its offering and communication according to what customers value most.

From cards to choices: the CBC approach
In practice, conjoint analyses are not done with physical cards but with statistical platforms using the Choice-Based Conjoint (CBC) approach. In this format, the respondent chooses between different combinations of attributes displayed on the screen.

For example, in a study on liquid bleach, five attributes were evaluated: Brand, Performance, Scent, Packaging, and Price. Each person saw twelve screens, with two options per screen, and had to select the one that best matched their preference.

The results revealed that—excluding price—the most important attribute in value perception was brand (with a weight of 78%). The other attributes such as performance, scent, and packaging had much lower importance.

Calculating purchase intention and elasticity
A key advantage of conjoint analysis is that by including price as one of the attributes, it's possible to calculate purchase intention at different price levels. This enables the construction of a purchase-intention curve and the calculation of elasticity between the levels analyzed.

This allows companies to estimate how customers will react to price changes and which attributes they should strengthen to increase perceived value.

What’s next: artificial intelligence and pricing decisions
With conjoint analysis, we already know how to measure customers’ value perception and price sensitivity. But how do we take this to the next level and incorporate these insights into AI models that predict real purchasing decisions?

We’ll explore that in upcoming articles, where we’ll look at how to apply artificial intelligence to pricing decision-making.