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Since the early 1970s, conjoint analysis has been used to measure consumer trade-offs among products and services with multiple attributes. Conjoint analysis originated in mathematical psychology, but today is considered a staple in the market research industry.

What is Conjoint Analysis?

Conjoint analysis is a survey-based statistical technique. In market research, it is used to determine how people value different attributes (features, functions, price, benefits, etc.) that make up an individual product or service. It is applicable in various instances that focus on discovering what type of product consumers are likely to buy and what they value the most (and least) about a product. As such, it is commonplace in marketing, advertising, and product management.

Conjoint analysis can help determine pricing, product features, product configurations, bundling packages, or all of the above. Marketers and product managers find it helpful because it simulates real-world buying situations that ask respondents to trade one option for another.

Conjoint analysis can also be used outside of the product experience, such as to gauge what employee benefits to offer, assess the appeal of advertisements, develop real estate, etc.

Ultimately, conjoint analysis can be a great fit for any researchers interested in analyzing trade-offs consumers make or pinpointing optimal packaging/bundling. It is quite flexible and can be used across most industries.

How does Conjoint Analysis Work?

Conjoint analysis breaks a product or service down into its components (referred to as attributes and levels) and then tests different combinations of those components to identify consumer preferences. The objective is to determine what combination of components is most influential on consumer choice or decision making.

The most common form of conjoint analysis is choice-based conjoint analysis (also known as discrete-choice conjoint analysis). In choice-based conjoint analysis, respondents are presented with several component combinations (each described in terms of features and price levels) and asked to choose between the combinations rather than ranking or rating each of them.

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For example, consider a conjoint study on televisions. The television would first be sorted into four attributes: brand, screen size, screen format, and price. The attributes would then be further broken down into different variations to create levels.

Choose your preferred concept from the options below:

Conjoint example

In a choice-based conjoint study, respondents would be asked to choose between the potential product concepts formed through the combination of attributes and levels. This employs a more realistic approach than simply asking respondents what they like in a product or what features they find most important.

Designing Conjoint Studies

Each conjoint concept should be similar enough that consumers will see them as close substitutes but dissimilar enough that respondents can clearly determine a preference. Each concept should be composed of a unique combination of levels. As the number of combinations of attributes and levels increases the number of potential concepts increases exponentially.

Typically, a choice-based conjoint project will include around three to eight attributes, and each attribute will have about two to seven levels. The process of assembling attributes and levels into product concepts and then into choice sets (or questions) is called experimental design and requires extensive statistical and mathematical analysis (done manually or using automated research tools).

As a general rule, it is best to keep conjoint surveys simple. Too many combinations of attributes and levels increases the number of concepts each respondent is asked to choose between, which can lead to respondent fatigue. Market research rules of thumb apply in regard to statistical sample size and accuracy when designing conjoint analysis studies.

Why Use Conjoint Analysis?

Using conjoint survey results, it is possible to calculate a numerical value that measures how much each attribute and level influenced the respondent’s choices. Each of these values is called a utility score or part-worth score. These scores indicate what each respondent prefers (in terms of attributes and/or levels) and ultimately are used to predict the optimal package or concept that a company should offer.

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In addition to those insights, utility scores can also be used to simulate market share. A market simulation provides information on the relative share of consumers who prefer predefined products in a certain context.

There are several research techniques used to calculate utility scores. The most widely used are Latent Class Analysis and Regression modeling based on Hierarchical Bayesian (HB).

Conjoint studies enable marketers and product managers to pre-test products before launch using a realistic methodology that is similar to an actual buying situation. Through the statistical analysis, researchers can estimate psychological tradeoffs that consumers make when evaluating several attributes or levels together to uncover real or hidden drivers which may not be apparent to respondents themselves.

Conjoint analysis is most frequently used for enhancing product development and feature prioritization. It is also the premier survey methodology for estimating price sensitivity and how cost influences customers’ purchasing decisions.

Advantages of Conjoint Analysis Over Other Tools

Conjoint analysis is used by numerous businesses across a variety of industries ranging from financial institutions to technology firms. Health care, real estate, and the smartphone industry also benefit from the use of conjoint analysis.

Some of the advantages of conjoint analysis in comparison to other tools include:

  • Conjoint analysis allows researchers to evaluate multiple variables unlike other tools, which are only capable of measuring one variable at a time.
  • The tool has a unique ability to provide measure preference on a scale rather than a “yes or no” approach, which enables a more insightful understanding of consumer behaviors and preferences.
  • The data collected from conjoint analysis can be used to construct various models that help businesses make better and more informed decisions. Few other techniques offer results that can be converted into models for further analysis.
  • Conjoint analysis can be used to measure price sensitivity to brand names and features on an individual level, assisting companies during the research design process. The interactions between price and various attributes can be determined, allowing for the evaluation of price sensitivity due to changes in variations.
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