What is Conjoint Analysis?

Overview

Conjoint Analysis is a quantitative research technique used to understand how people make decisions when faced with trade-offs. Instead of asking participants what they want directly, it presents them with realistic combinations of product features and asks them to choose between options.

This reflects how purchase decisions are actually made in real life, rarely based on a single factor, but on a combination of features, price and other attributes considered together.

How Conjoint Analysis Works

A Conjoint study is built around two core concepts: attributes and levels.

An attribute is a feature or characteristic of the product such as price, brand, packaging size or delivery speed
A level is a specific value or option within that attribute such as Rs 299, Rs 399 and Rs 499 under the Price attribute

The study generates combinations of these attributes and levels, called bundles or concepts, and presents them to participants. Participants choose their preferred bundle from each set. By analysing which combinations were chosen most often, the platform calculates how much each attribute and level influenced the decision.

Attribute: Brand and Levels: Brand A, Brand B, Brand C
Attribute: Price and Levels: Rs 299, Rs 399, Rs 499
Attribute: Delivery and Levels: Same day, Next day, 3-5 days

A bundle shown to a participant might be:
Brand A | Rs 399 | Next day delivery

Another bundle might be:
Brand B | Rs 299 | 3-5 days delivery

The participant selects the one they prefer. This choice reveals how they weigh price against brand and delivery speed.

KEY CONCEPTS

Importance Score

After the data is collected, each attribute is given an Importance Score expressed as a percentage. This shows how much that attribute influenced participants' choices overall. An attribute with a high importance score had a strong impact on decisions. One with a low score had little influence.

Utility Score

Each level within an attribute is given a Utility Score, also called a part-worth. This shows how much each specific option contributed to preference. Positive scores mean the level was preferred. Negative scores mean it was less preferred.

Note
Utility scores can only be compared within the same attribute, not across different attributes.

Willingness to Pay

By including price as an attribute, Conjoint Analysis can estimate how much participants are willing to pay for specific features. This makes Conjoint particularly useful for pricing and product strategy decisions.

When to Use Conjoint Analysis

Use Conjoint Analysis when you need to understand how your audience makes decisions across multiple product features or variables where trade-offs are involved.

You want to know which product features matter most to your audience
You need to find the optimal price point for a product or service
You are deciding which combination of features to include in a new product
You want to simulate how changes to one feature such as price affect overall preference
You need to compare how different audience segments prioritise features differently

USE CASES

Pricing research

A beverage brand wants to know the maximum price consumers will pay for a premium variant before switching to a competitor. Conjoint Analysis shows the price threshold by measuring how utility drops as price increases.

Product feature prioritisation

A mobile app team needs to decide which three features to build in the next release from a list of ten. Conjoint Analysis reveals which features drive the most preference so the team can prioritise development accordingly.

Packaging and format decisions

An FMCG brand is deciding between three packaging sizes and two material types for a new product. Conjoint Analysis shows which combination of size and material is most preferred by the target audience.

Subscription plan design

A streaming platform wants to design subscription tiers that maximise sign-ups. Conjoint Analysis helps identify which combination of price, content library size and number of screens drives the strongest preference.

Competitive positioning