Consumer Preferences Product Design Product Attributes

How to Use Conjoint Analysis for Product Design


Customers today, more than ever, expect brands to be responsive to their preferences and design products that meet their needs. Fortunately for consumers, the amount and variety of product options has never been more extensive. Large online marketplaces, product recommendation services, and hours upon hours of video reviews make consumers smarter and more equipped than ever. Unfortunately for brands, oversaturated markets are a grim reminder that designing products based on consumers’ needs remains crucial.


Conjoint analysis has emerged as a valuable tool in understanding customer preferences and their decision-making processes. Unlike traditional marketing surveys that often assess customer preferences by asking direct questions about single product features in isolation, conjoint analysis provides a holistic approach that considers the interactions and trade-offs between multiple product characteristics simultaneously. 


So rather than asking about a single aspect of a product (e.g., Would you buy a pen with red ink?), conjoints ask customers to consider several different hypothetical products in their entirety (e.g., Would you buy a BIC ballpoint pen with red ink and a cap? What about a blue, Pilot gel pen with a click-y top?). 


By presenting respondents with realistic product scenarios and requiring them to make choices, conjoint analysis captures the complexity of decision-making in a way that traditional surveys often fail to achieve, allowing product managers to gain insights into the relative importance customers assign to different product characteristics (e.g. How important is ink color in pen buyers’ decision-making?) and their impact on overall preferences.


In this article, we will explore how conjoint analysis can be used to optimize product design based on consumer preferences. In particular, this post will explain how to design a conjoint analysis and offer practical recommendations along the way.


Define your research objectives


To effectively utilize conjoint analysis for product design, it is crucial to clearly define your research objectives. Despite how powerful conjoint analysis is, it may not be the smartest or most appropriate methodology to answer your research question(s). This section will cover how to identify the stage of the product design lifecycle in which you find yourself.


What stage of the product design lifecycle are you in?

The application of conjoint analysis may vary depending on the stage of the product design lifecycle you find yourself in. Understanding your current stage can help you tailor the conjoint analysis process to derive maximum value. Here are some common stages and how conjoint analysis can be utilized:


  • Idea Generation & Concept Development: During early product conception, conjoint analysis can be used to gather insights into many different hypothetical product designs, exploring different product configurations to determine the most promising design features to test in later stages.


  • Prototype Development: Once you have initial product prototypes, conjoint analysis can assist in evaluating and refining different variations. By presenting respondents with different product profiles that vary in attributes and levels, you can assess their preferences and make data-driven decisions on the most desirable product features.


  • Product Refinement/Augmentation: Even after a product launch, conjoint analysis can be valuable for gathering customer feedback and identifying areas for improvement or future enhancements. By continuously analyzing customer preferences and evolving market dynamics, you can augment your product to stay competitive and meet changing consumer demands.


Considering the stage of the product design lifecycle allows you to tailor your conjoint analysis approach to the specific needs and goals of that stage, ensuring the insights gained are most relevant and actionable.


By defining your research objectives and considering both the suitability of conjoint analysis for your needs and the product design lifecycle stage you are in, you can lay a strong foundation for conducting effective conjoint analysis and making informed design decisions.


Design your attribute map


After you’ve determined that conjoint analysis is right for you, the next crucial step is to design your attribute map. This section will cover important considerations, including how to select what product features (otherwise known as product attributes) and nested characteristics (otherwise known as attribute levels) to test.


What are the product “attributes” you want to test?

Start by determining the key product features, or attributes, that you want to test. Most consumer products have many attributes that could be customized in appearance or functionality. Cars, for example, come in many shapes, sizes, and types. When it comes to cars, consumers can choose between different transmissions, seat fabric, color, and many other attributes.


The key here is to select product attributes that (a) directly impact customer preferences and influence their decision-making and (b) are configurable features of your product. Consider factors such as functionality, design elements, pricing, packaging, and any other attributes that are relevant to your product category and target market.


What specific product “levels” do you want to test?

For each product attribute, there are a few (or many) different varieties. For example, car shoppers have the option of choosing between electric, hybrid, and gas vehicles. They can decide between leather, fabric, and vinyl seats. They can even shop based on the exterior color they want.


Levels represent different variations or options within each product feature (or attribute). It is important to carefully select levels that are meaningful and representative of the product space you are exploring. Aim for a range of levels that capture the potential diversity of customer preferences.


Make sure you also keep an eye out for unrealistic combinations of product features. You may need to explicitly prohibit certain impossible or contradictory combinations of product features. 


Decide how to display products in your survey experiment

Another critical consideration is how to present the product features and levels to survey respondents. You have the option to use visual assets, textual descriptions, or a combination of both.


  • Visual Assets: Including visuals can help respondents better understand the product attributes being tested. This can be particularly effective when evaluating design-related features, such as color, shape, or packaging. Visual representations can be images, mock-ups, or even virtual prototypes that accurately depict the product.


  • Descriptions: Providing clear and concise textual descriptions of the product features and levels is essential. This allows respondents to understand the attributes and make informed choices. Ensure that the descriptions are precise, unambiguous, and provide enough detail for respondents to differentiate between the levels.


Depending on the complexity of your product and the nature of the attributes being tested, you may choose to use a combination of visual assets and descriptions. This approach can provide a comprehensive understanding of the product features while minimizing ambiguity, but remember to maintain consistency in the presentation format throughout the conjoint analysis to ensure reliable and valid responses from participants.


Create a balanced design

Once you have determined the product attributes and levels to include in your conjoint analysis, the next step is to create a balanced design. A balanced design ensures that your attribute level pairings are optimally displayed, maximizing the power of your study. To achieve a balanced design, you need to ensure that each attribute level pairing appears an equal number of times across different choice tasks. This balance prevents biases resulting from the order or frequency of specific levels and allows for accurate estimation of attribute effects.


Various algorithms and software tools are available to assist in generating a balanced design. These tools help optimize the allocation of attribute level combinations and ensure an efficient and statistically robust conjoint. By creating a balanced design, you enhance the quality and reliability of your conjoint analysis results, enabling you to make more informed decisions regarding product design and feature preferences.


In this section, we will explore the key considerations for creating a balanced design, including the number of choice tasks, the number of alternatives, and the inclusion of a dual response none option.


  • Number of Choice Tasks: The number of choice tasks refers to the number of scenarios or product profiles that respondents will evaluate and make choices between. It is essential to strike a balance between gathering sufficient data and not overwhelming participants with an excessive number of tasks.

    A common approach is to aim for an efficient design with a manageable number of choice tasks. Depending on the complexity of your product and the number of attributes and levels, a typical conjoint analysis study may have between 8 and 15 choice tasks per respondent. However, you should consider factors such as respondent fatigue and survey length to ensure a positive user experience and high-quality responses.


  • Number of Alternatives: The number of alternatives refers to the number of product profiles presented within each choice task. Traditionally, conjoint analysis studies present respondents with a fixed number of alternatives, typically between 2 and 5.

    Having a larger number of alternatives can increase the complexity of the choice task, requiring participants to carefully evaluate each option. On the other hand, a smaller number of alternatives may lead to simpler decision-making but might not capture the full range of preferences.

    Consider your research objectives, respondent engagement, and the level of complexity in your product space when determining the appropriate number of alternatives to include in each choice task.


  • Dual Response None Option: The inclusion of a “dual response none option” allows respondents to indicate when they do not prefer any of the presented alternatives. This option acknowledges the possibility that respondents may find none of the options appealing or may choose to not make a selection.

    The decision to include a dual response none option depends on your research objectives and the context of your study. If you want to capture the preference for not selecting any alternative or to understand the level of dissatisfaction with the presented options, including a none option can provide valuable insights.

    However, it's important to note that the inclusion of a none option may affect the overall choice patterns and potentially decrease the discrimination power of the analysis. Carefully consider the trade-offs and potential impacts on your research objectives before deciding whether to include a dual response none option.




Conjoint analysis is a powerful tool for optimizing product design based on customer preferences. By considering the interactions and trade-offs between multiple product characteristics, conjoint analysis provides valuable insights into the relative importance customers assign to different attributes and their impact on overall preferences.


By leveraging conjoint analysis, you can gain a deeper understanding of customer preferences, identify key drivers of preference, and make informed decisions about product design, and market positioning. Ultimately, using conjoint analysis empowers you to create products that resonate with customers and meet their evolving needs in today's competitive marketplace.


Does your organization need help identifying your consumers’ product preferences? We’d be happy to help you with your conjoint analysis. 

Brendon Ellis

Written by Brendon Ellis

Brendon earned his master’s degree in Positive Organizational Psychology & Evaluation from Claremont Graduate University and is currently pursuing his PhD in Positive Organizational Psychology. He applies his background in survey research methods on a daily basis to help clients develop instruments that measure anything from attitudes and preferences to intentions and behavior.

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