researcher conducting conjoint analysis

5 Types of Conjoint Analysis for Healthcare Market Research

Conjoint analysis is a key tool in healthcare market research, offering vital insights into customer preferences for different products and services. 

It’s a valuable research method for helping healthcare organizations and medical device manufacturers understand what matters most to their customers by analyzing how they value various product attributes and features.

researcher conducting conjoint analysis

With this knowledge, organizations are able to make more informed decisions about their product offerings, device design, pricing strategies, and marketing strategy.

In this article, we will look at five different types of conjoint analysis used in healthcare market research, each with its unique approach and benefits.


What is conjoint analysis?

Explained simply, conjoint analysis is a research technique used in surveys to measure the importance of various features of a product or service. 

In a typical conjoint analysis study, respondents are asked to make choices or trade-offs, which reveal which attributes they value the most. 

In the context of healthcare market research, conjoint analysis can be used to understand and predict how different aspects of a product, such as functionality, design, and cost, influence customer preferences. 

For instance, when evaluating medical devices, conjoint analysis can help manufacturers determine which features are most important to healthcare professionals or patients. 

By pinpointing the features that customers prioritize, marketers can develop strategies that align more closely with user needs and preferences.


What are the applications of conjoint analysis?

Conjoint analysis provides medical device manufacturers with valuable insights that can be applied in various aspects of their organization. 

By allowing an in-depth understanding of customer preferences, conjoint analysis can influence a variety of areas, including pricing strategies, sales and marketing initiatives, and research and development

Let's explore these applications in more detail:


Conjoint analysis in pricing

Conjoint analysis can help us understand how price changes will impact demand for a product in the market by revealing what prices customers will accept for products with different combinations of features.

In the context of healthcare, particularly for medical devices, this can be invaluable. 

With this knowledge, the company can then decide which features to emphasize and how to price them accordingly. If a feature is highly valued by customers, it may justify a higher price point.

This informed approach to pricing, guided by customer preferences, allows medical device manufacturers to align their prices with market expectations.

In the competitive healthcare market, where pricing decisions can significantly impact product success, conjoint analysis can offer a strategic advantage.


Conjoint analysis in sales & marketing

When a medical device manufacturer knows which aspects of their product are perceived as most valuable by end users, these features can become the focal point of advertising campaigns, marketing materials, and promotional efforts. 

For example, if conjoint analysis reveals that end users in the healthcare sector highly value user-friendliness in medical devices, a company can emphasize this feature in their marketing copy, highlighting it as a key benefit. 

This targeted approach ensures that marketing messages resonate with potential buyers, addressing their primary concerns and preferences.

Buyers are more likely to respond positively to marketing efforts that speak directly to their needs, which can lead to increased customer interest, higher conversion rates, and, ultimately, more successful sales performance. 


Conjoint analysis in research & development

In the research and development phase, medical device manufacturers often face decisions about which features to add, enhance, or possibly remove from their products. 

Conjoint analysis helps in this decision-making process by providing clear evidence of what customers value most.

For example, in the healthcare sector, a company considering the introduction of a new type of wearable health monitor can use conjoint analysis to determine which features - such as battery life, connectivity with other devices, or visual design, are most likely to make the product appealing to its target audience.


Conjoint analysis as a decision-making tool

In the context of healthcare, conjoint analysis can inform more than just product development, pricing, and marketing - it can be used as a strategic guide for organizational decision-making. 

By providing a comprehensive view of customer preferences, it can help inform top-level strategies and long-term planning.

For example, for a medical device manufacturer with a diverse range of products, conjoint analysis can help identify which products should be prioritized based on market demand.

It can also help healthcare organizations identify which areas their customers truly value about their services - which can influence their overall positioning and branding strategies.


Want to learn more? Check out The Ultimate Guide On Healthcare Market Research 


What are the different types of conjoint analysis?

For any healthcare provider or medical device manufacturer looking to set up a conjoint analysis study, it’s important to note that there isn’t a ‘one size fits all’ approach - it’s actually quite a nuanced area of research. 

There are a variety of different ways to structure and approach conjoint analysis, and organizations need to choose the type of analysis that best aligns with their unique goals and research objectives - whether it's understanding customer service preferences in detail, predicting market trends, or guiding product development.

Let’s take a look at the different types of conjoint analysis that are most typically used in healthcare market research.


Adaptive choice-based conjoint (ACBC)

Adaptive choice-based conjoint (ACBC) is an evolution of the choice-based conjoint (CBC) method, which combines elements of adaptive conjoint analysis (ACA) to create a more responsive and tailored survey experience. 

The survey adapts to the participants' preferences by tailoring follow-up questions and choice tasks that are more aligned and relevant to their individual decision-making process.

ACBC is especially suited for studies with smaller sample sizes, offering detailed insights into individual product features beyond what traditional CBC can provide.

This tailored and adaptive approach not only enhances the relevance of the survey for each respondent but can also improve the accuracy of the results.

However, the complexity of the data analysis process in ACBC requires a higher level of expertise compared to traditional CBC.


Menu based conjoint (MBC)

Menu-based conjoint (MBC) delivers a slightly different approach to conjoint analysis, allowing respondents to determine their preferences from different sets of features -  similar to choosing from a menu. 

This type of conjoint analysis is particularly effective in evaluating the overall desirability of various feature combinations offered in a product or service.

One of the key advantages of MBC is that it can help simplify the research process. Traditional conjoint analysis might require respondents to consider a large number of attribute combinations, which can be overwhelming. MBC addresses this by presenting a more manageable set of options, reducing the likelihood of respondent fatigue.

In terms of limitations and considerations, MBC can require a lot of work in terms of survey design and data analysis. It requires careful construction of menu sets and a sophisticated analysis approach to interpret the combination of features chosen by respondents.


Full-profile conjoint analysis

Full-profile conjoint analysis is typically conducted through web surveys. It presents respondents with a series of full product profiles, each one including a specific combination of attributes.

Respondents are asked to evaluate the different product profiles in terms of preference rather than make a direct purchasing decision. 

By structuring the series of pairings carefully, full-profile conjoint analysis can be effective in identifying which product attributes are most valued by end users. 

One of the key considerations when setting up a full-profile conjoint study is to make sure the number of profile pairings and the level of detail provided doesn't lead to respondent fatigue.


Adaptive conjoint analysis (ACA)

Adaptive conjoint analysis (ACA) is a type of conjoint analysis that focuses on tailored choices and trade-offs.

The characteristic that makes this method unique is that the survey experience is tailored for each respondent, delivering questions that are directly relevant to their previous answers. 

This tailored customization enables researchers to get a deep understanding into individual preferences, making ACA an effective tool for identifying which product features or specifications are most valued by customers

A typical approach to ACA would be structured over several stages, such as: 

  • Preference for levels
    Respondents rate the desirability for different options (such as product attributes) based on their personal preferences.
  • Attribute importance
    The respondent is asked to determine the relative importance of different options.
  • Paired-comparison trade-off questions
    The respondent is presented with a series of two product/service concepts and asked to choose their preferred option, indicating the strength of their preference.
  • Calibrating concepts
    This stage involves refining the options based on respondent feedback and asking about their likelihood to purchase. 

The strength of ACA comes from its ability to get a deep understanding of customer choices and the decision-making processes. This can lead to more accurate insights into what customers value in a product, which is crucial for effective product development and marketing.

However, the method's complexity can be a limitation, as it requires careful survey design. 


Choice-based conjoint analysis (CBC)

CBC is one of the most common types of conjoint analysis, which can be used to determine how respondents value different combinations of product features. 

This approach typically presents respondents with a series of choice tasks where they are presented with various product profiles. They are asked to choose between these profiles, reflecting real-life decision-making scenarios. In some CBC studies, the approach may also include the option for participants to select none of the presented choices, adding to the realism of the buying process.

CBC is particularly useful in medical device market research, as it helps manufacturers understand the trade-offs healthcare professionals and patients are willing to make between device features and pricing.

The main challenges in using CBC include the complexity of data analysis and the potential for biases in respondent decision-making.


Want to find out more about the five different conjoint analysis methods? 

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Advantages and disadvantages of conjoint analysis

If you’re interested in getting a better understanding of customer preferences for various products and services, conjoint analysis can be an important part of your research - offering deep insights into the decision-making process. 

However, like any research methodology, it's crucial to understand both its strengths and limitations so you can make an informed decision about whether it's the right approach for your specific research needs. 

Let's take a quick look at the common benefits and disadvantages of conjoint analysis.


Benefits of conjoint analysis:

  • A better understanding of customer preferences
    It can help organizations understand their customer's needs and preferences and tailor their products and strategies based on these insights. 
  • Data-driven decision making
    It enables healthcare organizations to make decisions based on solid data that back up assumptions about customer preferences. 
  • Unique insights
    Conjoint analysis can provide insights that are hard to achieve through traditional market research methods.
  • Realistic decision-making scenarios
    Conjoint analysis can mimic real-world scenarios, giving a more accurate reflection of customer preferences and decision-making processes. 
  • Flexibility
    The various different approaches to conjoint analysis mean it is suitable for a wide variety of research projects. 


Disadvantages of conjoint analysis:

  • Data reliability
    Accuracy of insights can be a challenge if there are too many or too few attributes, making it hard to capture true customer preferences. A clear and precise survey design is crucial.
  • Privacy issues and ethical considerations
    It's important to follow ethical guidelines like getting informed consent from participants and ensuring patient privacy to maintain the integrity of the research.
  • Time-consuming process
    While it offers valuable insights, conjoint analysis can be resource-intensive and may not be suitable for every market research project due to the time and effort required.



Let IDR Medical help you with conjoint analysis

Conjoint analysis is a powerful tool in healthcare market research, offering a range of approaches that can be tailored to different research needs. 

Although conducting conjoint analysis can be time-consuming and requires careful consideration of data reliability, the insights gained are invaluable - enabling healthcare organizations to gain a deep understanding of customer preferences to tailor their products and services effectively.

IDR Medical is here to help you unlock the full potential of conjoint analysis.

Ready to find out more about how we can help? 

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