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What is conjoint analysis?

Conjoint analysis is a choice modeling research method to understand how people make purchasing decisions. In the real world, we often come across situations when we have to make tough choices between various choices. The conjoint analysis helps us understand what is important for your target audience. It involves how they make trade-offs and what essential features they are not willing to let go.

The conjoint question in surveys is an advanced question type, used to present many combinations of product attributes like features, cost, brand, etc. Based on the respondents' answers, market researchers can find out which are the most liked features by customers and at what price. Many times a purchase involves evaluating several parameters that make it complicated. In such a situation, conjoint analysis can help understand customer psychology.

Types of conjoint analysis

Choice-based conjoint analysis: This type of analysis asks respondents to imitate their purchasing behavior while answering the survey. The respondents submit responses based on the actual products they would choose in real-life, given specific prices and features.

Types of design for the discrete choice model

QuestionPro offers below design types for conjoint analysis using the discrete choice model.

  1. Random: This design is a random sample of the possible attribute levels. The survey software will create a unique combination of attributes for the number of tasks per respondent. To know what choices will be presented when your survey is actually deployed, you can run a conjoint concept simulator.
  2. D-Optimal: While designing experiments for estimating statistical models, optimal designs estimate parameters without bias and with minimum-variance. D-optimal design runs a set of experiments to optimize or investigate the subject under study. The algorithm creates an optimal design for the tasks per respondent and sample size.
  3. Import design: This design type allows designs in the SPSS format to be imported and used by the discrete choice module. For instance, you can import fractional factorial orthogonal designs and use them in QuestionPro surveys.
Example of conjoint analysis in surveys

Consider a smartphone manufacturing company that wants to launch a new phone in the market. They need to know how their target customers value different product attributes such as size, brand, price, etc. Based on the responses, they will create a strategy that will ensure maximum sales.

Conjoint-anlaysis-example

If the company receives more responses for device 1, they can infer that consumers want higher battery power instead of more cameras.

Adaptive conjoint analysis: This type of conjoint analysis is used when there are many product features. It is generally used to identify key features to be included in the product, and not the best choice for determining the price.

For instance, the survey respondents are asked to select their relative preference from a number of attributes. Each pair is then assessed on a grade point scale.

The choice-based conjoint analysis, also known as discrete-choice conjoint analysis, is the most commonly used type of conjoint analysis.

Data analysis of conjoint survey question

With QuestionPro surveys, you can generate a conjoint analysis report and filter survey data.

Learn more: Data segmentation and filtering analysis in surveys

The statistical analysis report consists of below tabs.

Attribute importance: This tab shows which attributes are more important as compared to others and by what percentage.

Learn more: Conjoint analysis attribute importance

Conjoint-anlaysis-attribute-importance

Profiles: It is a set of attributes with different levels. The conjoint analysis software shows respondents various combinations of product features, prototypes, mockups, or pictures created from a combination of levels. Each example is similar enough to be close substitutes but different enough to be distinguishable.

Learn more: Conjoint analysis profiles

Conjoint-anlaysis-profiles

Market simulation: Using this feature, you can forecast the market share of new products that don't exist today. You can also measure the gain or loss in the market share based on the changes in the existing products. The conjoint analytics tool simulates the market share of the products to establish a baseline. Then, you can see how the market share changes depending on new products and configurations.

Learn more: Market segmentation simulator

Conjoint-anlaysis-market-simulation

Estimated brand premium: In many cases, customers are willing to pay extra for a product with the same features as others but with a different brand. This report finds out how much premium a customer will pay for a brand.

Learn more: Brand premium

Price elasticity: It is the proportional change in demand for a product for change in attributes and price. To view this report, map attribute type to brand & price for each level.

Learn more: Feature attribute type

Conjoint-anlaysis-brand-premium

Export reports: You can also download below reports in the form of .xls.

Uses of conjoint analysis in surveys

Advantages of conjoint analytics in surveys

How to add conjoint analysis question in your surveys?

Learn how to use this survey feature with our help file on the Conjoint Analysis question.

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