# Conjoint Analysis

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## What is Conjoint Analysis?

Conjoint analysis can be defined as a survey-based statistical technique that is used as a research analysis method. It attempts to understand how people make complex decisions and value different attributes that make up individual products and services. Conjoint analysis is based on the principle that all products can be broken down into attributes that define how users perceive their value.

Conjoint Analysis is often used across a sundry industry as it is applicable in various instances that aim to define the types of products consumers are most likely to buy. It can help companies make pivotal decisions regarding many aspects of their organization.

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## Conjoint Analysis in Surveys

Conjoint analysis, unlike traditional surveys, allows for the rating of different attributes that make a product. With the right survey analytics software at your disposal, It lets you screen through the responses/choices, enabling you to determine the reasons for why respondents chose them.

## Conjoint Design

The four steps required to create a Conjoint Design

1. Determine the type of study: Studies may be designed differently, such as ranking-based conjoint, rating-based conjoint, and choice-based conjoint.
2. Identify relevant attributes: Then, different characteristics should be identified. Attributes used in the conjoint analysis should satisfy specific criteria. For instance, they need to be relevant to managerial decision-making and be clearly defined and communicable.
3. Specify Levels: Next, the levels of attributes must be specified. The levels must be realistic, unambiguous, and mutually exclusive.
4. Design questionnaire: The last step is to design the questionnaire. When potential profiles are really large in number, fractional factorial design is used to narrow down the number of profiles to be evaluated.

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## Why, When, and How is Conjoint Analysis used

Why

Conjoint analysis is considered to be the best survey method to determine consumer values. It allows companies to understand product features and service features that customers value. This information aids businesses in making highly informed decisions when it comes to things like product development, sales, and pricing.

When

There are many applications of conjoint analysis. These are a few:

1. To test the appeal of a new product: Conjoint analysis is widely used to test the appeal for new products and services.
2. To optimize product portfolios: Most companies aim to design product portfolios as optimally as they can. Conjoint analysis can help companies create optimization techniques after understanding how consumers value different attributes.
3. To optimize price: Conjoint analysis, especially choice-based conjoint analysis, is widely used to help companies determine their pricing strategies. They can use this analysis to understand the potential impacts of price increases or decreases on how likely the consumer is to purchase the product after the change.
4. To compute brand equity: Choice-based conjoint analysis helps estimate the utility for each brand. This becomes an estimate of brand equity.
5. To assess the impact of product design changes: Conjoint analysis can determine consumers’ feelings toward potential changes in product design. This allows companies to shift to product designs that consumers will most appreciate.

How

It works by directly asking respondents to compare different attributes to understand how they value each one. By understanding this, a company can understand how customers would appreciate its products. This information can help develop many strategies for the company’s products, pricing strategies, for example.

Key terms

• Profiles: Profile refers to the hypothetical offering/product used in the analysis.
• Brand Premium: This term refers to how many more attributes will help a customer pay for a higher-priced product.
• Attributes: Attributes, in this context, are the product features that are being evaluated.
• Levels: Levels refer to the specifications of every individual attribute.
• Tasks: This term refers to the number of times the respondent has to make a choice.

## Types of Conjoint Analysis

Today’s powerful analytics survey software allows researchers to perform multiple forms on analysis on their data
• Discrete choice-based conjoint (CBC) analysis:

This type of conjoint analysis asks consumers to imitate their purchasing behavior like they would in real life. It studies the products they would choose depending on specific criteria.

ACA analysis customizes every respondent’s survey experience based on their answers to the first few questions. This conjoint analysis is usually employed in studies where multiple attributes need to be evaluated to streamline the process and obtain valuable insights from every respondent.

• Full-Profile Analysis

The full-profile conjoint analysis presents a series of full product descriptions to respondents. Then, respondents are asked to select the one that they would most likely buy.

• MaxDiff Conjoint Conjoint Analysis

This form of conjoint analysis will present respondents with a series of options. Then, the respondent is asked to order these options on a scale of “most likely to buy” to “least likely to buy.”

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