conjoint-analysis-example-in-healthcare-market-research

Conjoint Analysis in Healthcare: Examples & Challenges

 

A conjoint analysis example can be complex. As global leaders in medical market research, IDR Medical show the benefits and challenges they can present.

 

COVID-19 has illuminated the healthcare industry's capacity for innovation. AI-aided accurate chest X-ray analysis, cost-effective genomic sequencing, and bespoke cancer treatments are noteworthy advancements. However, the persisting crisis necessitates further innovation in this hard-hit sector.

Healthcare companies wrestle with multi-faceted supply chains, fluctuating healthcare professional interactions, and disrupted clinical trials. Clinical settings face distinct challenges: shifting protocols, patient care transformation, limited resources, and financial constraints—all evoking calls for inventive solutions.

The pandemic's seismic impact led 90% of executives to expect major business transformations, with 85% anticipating enduring alterations in consumer preferences. For two-thirds of healthcare leaders, navigating this crisis is the pinnacle of their career challenges.

Here, conjoint analysis emerges as the guiding light for data-driven decision-making in healthcare. Implementing conjoint analysis bolsters cost efficiency, customer engagement, and profit dynamics.

Delving into conjoint analysis examples, we shall examine the benefits and challenges encountered when employing this technique in healthcare market research.

 

The use of conjoint analysis for healthcare market research

Conjoint analysis can be used in healthcare to find which aspects of a product or service are most important to consumers and providers, enabling healthcare organizations to develop strategies to reduce costs while improving outcomes.

Conjoint analysis is a quantitative research methodology, quantifying customer preferences and determining the trade-offs people make when making decisions about their care. Conjoint analysis can help:

  • Healthcare organizations better understand the market
  • Determine pricing strategies
  • Create new products or services
  • Optimize marketing campaigns
  • Help healthcare organizations better understand the needs of their customers and which treatments are most valuable to them
  • Improve patient engagement and inform policy decisions that benefit patients and health providers

Conjoint analysis serves medical market research and device manufacturers by assessing the competitiveness of product features and affordability. Consumers also relay future expectations and perceptions of current products, influencing overall reception. 

Another conjoint analysis example is to use varying conjoint methodologies during medical device and pharmaceutical product development and launch. By gauging customer responses regarding different product aspects (such as design, value, brand perception, and price sensitivity), IDR Medical can help estimate preferences and predict market share. By utilizing customized conjoint methodologies, we can ensure more accurate and specific responses.

 

Why use conjoint analysis?

Conjoint analysis is an effective solution but is most relevant at certain stages of the product lifecycle. The main reasons to use conjoint analysis are:

  • To inform new product development decisions

When embarking on the creation of a new product or service, to drive new product development decisions, it's crucial to uncover features that enhance value, ignite customer excitement, and boost market share.

Using a conjoint analysis example for medical device market research will aid you in encapsulating these focal points. Whilst acknowledging and deciphering these aspects, you edge closer to delivering solutions that resonate with your audience, thereby driving business growth and solidifying your market position.

  • To evaluate price sensitivity

Before launching a new product, feature, or service, you should consider customer preferences for competitor products within the same category or price range. Understanding customer willingness to pay a premium price compared to competitors is essential. Conjoint analysis examples provide a deeper insight into the juncture of perceived value and consumer expectations.

This method navigates price sensitivity complexities and empowers informed decision-making. Offering guidance for optimal pricing strategies, their willingness to accept new offerings, while catering to the healthcare industry's dynamic demands.

 

Things to consider before undertaking conjoint analysis

As experts navigating the complexities of the healthcare industry, we understand that conjoint analysis may not always be the optimal approach for every project. At IDR Medical, we assess each undertaking from the outset to ensure time and budget efficiency, while presenting suitable alternatives when needed.

Below are a few critical considerations to weigh before embarking upon conjoint analysis:

  • Alignment with project objectives: Assess whether the project's objectives and ensuing business decisions align seamlessly with a conjoint methodology. Meticulous evaluation is paramount in this process.

  • Dissecting product or service components: Conjoint analysis thrives on breaking down products or services into distinct attributes and levels. Adequate reflection on this aspect is vital before moving forward.

  • Adequate sample size: Ascertain whether it's feasible to target a sufficiently large sample size warranting robust data outputs that genuinely represent the market.

  • Comprehension of product design choices: Conjoint analysis may fall short in exploring unmet customer needs. Should product design aspects be well understood, qualitative methodologies may be better suited for projects aiming to explore customer needs.

 

Conjoint Analysis Example

 

A conjoint analysis example could be asking participants to select one of two blood pressure machines with varying attributes such as analog or digital, price,  size, weight, etc. Each of the features has several levels that participants can choose from.

The choices are presented in specific combinations, and participants must rank them based on their preferences. The data collected is then analyzed to understand customer preferences for different product features and attribute levels.

A researcher can gain insights into why the respondents highly value specific attributes and how alterations in those attributes could impact their decision-making by studying their responses.

Conjoint analysis methods can include:

 

Full-Profile Conjoint Analysis Example 

 This approach uses specific attributes to construct diverse product descriptions or profiles, presented to study participants to be ranked or rated, evaluating their preferences, and likelihood to purchase. The data collected from this analysis can be used to determine the best configuration of features for each product.

 

Adaptive Conjoint Analysis Example (ACA)

This conjoint analysis example is a computer-aided survey that enhances each respondent's experience by customizing survey questions. Leveraging respondents' initial responses, each subsequent question narrows focus, guiding them toward their ideal product selection. 

 

Choice Based Conjoint Analysis Example (CBC)

Mimicking real-life trade-off situations in decision-making, this conjoint analysis example identifies valued feature combinations. It's also known as discrete choice experiment (DCE) or discrete choice modeling (DCM).

Respondents gauge feature significance, evaluating their monetary value. Utilizing gathered data, an optimal product configuration tailored to customer preferences emerges, refining overall market offerings.

 

Adaptive Choice-Based Conjoint Example (ACBC)

Adaptive Choice-Based Conjoint (ACBC) is the most modern conjoint analysis example and fuses the ACA and CBC methodologies. Through three interview phases, it delves deeper into participant preferences, adapting at the individual level as it learns from their prior responses. It’s longer than a traditional choice-based conjoint survey but provides a more in-depth analysis of each respondent's decision-making process.

 

To learn more about these five conjoint analysis examples and how each method works, follow this link to our eBook.

Acknowledging the intricacies of healthcare market research, allows us to support you in crafting a successful strategy tailored to your project's unique demands, with an unwavering commitment to your success.

 

Download our FREE guide to conjoint methods

 

 

Benefits and challenges of conjoint analysis

Like any healthcare market research methodology, conjoint analysis has strengths and weaknesses to consider when deciding whether it is the right tool for your project. Conjoint analysis aims to understand how customers make decisions about products and services. The main challenge is ensuring the respondents accurately represent a market or target population. 

 

Benefits of conjoint analysis

 

  • Realistic market simulations
    Conjoint analysis allows researchers to simulate real-world product decisions. This market research method provides actionable insights and helps organizations make more informed decisions about their products, services, and pricing strategies.


  • Captures preferences and trade-offs
    Used as a method to estimate how the market values different features, prices, and brands; conjoint analysis helps researchers to understand customer preferences by presenting them with various product attributes and levels. By studying their choices or ratings, they can determine the relative importance of different attributes and customers' trade-offs.


  • Accurate measurement of attribute importance 
    Providing a systematic and quantitative approach to measuring the relative importance of different product attributes, conjoint analysis allows researchers to assign utility values to each attribute and level, enabling a precise understanding of how changes in attributes impact consumer preferences.


  • Market segmentation
    Conjoint analysis serves as an insightful tool, quantifying perceived benefits from your products or services. This pivotal methodology aids market segmentation, informing marketing strategies and effectively conveying these benefits. Understanding values attributed to your offerings empowers enhancing existing products or pioneering new ones.


  • Predictive power 
    Conjoint analyses offer high predictive power and help researchers understand customers' trade-offs when selecting products or services. By obtaining this data, organizations can make better decisions about product design, pricing strategies, and promotional campaigns.


  • Flexibility
    Conjoint analysis offers flexibility in terms of survey design. Researchers can adapt the survey to fit the research project and adjust it according to specific needs allowing researchers to customize the attributes and levels presented to respondents.  Furthermore, different conjoint analyses can be combined to generate a comprehensive view of customer preferences.

 

Challenges of conjoint analysis

 

  • Complexity And Expertise
    Conjoint analysis is complex in nature and execution. Researchers are required to have expertise in statistical methods and market research techniques. In addition, specialized software is needed to design the survey and analyze the data.


  • Assumption Of Rational Decision-Making 
    Conjoint analysis assumes a rational decision-making process based on assessed attributes. Yet, we must acknowledge that human decision-making can be influenced by personal biases, emotions, and psychological factors, establishing a compelling dimension to consumer choices.


  • Limited Attribute Representation
    In conjoint analysis, researchers may focus on only a few attributes and levels, which may not fully cover the complexity of consumer preferences. This may result in the underrepresentation of other attributes and levels.


  • Respondent fatigue and cognitive burden
    It is hard to confirm the accuracy of the results obtained from conjoint analysis surveys. Additionally, conducting conjoint analysis surveys can be tiring for respondents as they need to go through various product profiles or scenarios, leading to fatigue and decreased attention.


  • Limited ability to capture dynamic preferences
    Conjoint analysis shines in capturing static preferences at a singular instance. However, its efficacy may falter when dealing with evolving preferences over time, or influenced by external events or contextual factors.


  • Sample Sizes
    Particularly relevant for healthcare market research where it is often challenging to target a sample population large enough to be representative.

 

Navigating these intricate methodologies can be challenging. Let IDR Medical support you in making informed decisions that ultimately contribute to your success in healthcare industry market research.

 

Book a FREE consultation

 

 

How Do You Create A Conjoint Survey?

Conjoint analysis is increasingly used in healthcare and has proven successful in various areas. These include eliciting patients and communities' preferences for healthcare services, identifying consultants' priorities, establishing the best treatments, and determining patients' preferences in their interactions with doctors.

There are various stages that need to be completed before determining which conjoint method example should be used. Designing a conjoint survey involves several key steps to ensure that the survey accurately captures respondents' preferences and provides meaningful insights.

Here's a step-by-step guide to designing a conjoint analysis survey:

 

1. Define The Research Objective

The first step is to define the objective of your conjoint analysis. Specify the insights or information you need to collect and explain how you intend to use the findings to make informed decisions. This will determine what type of conjoint analysis should be used and which attributes and levels need to be included in the survey design.

 

2. Identify Attributes

Pinpoint crucial attributes and distinguishing characteristics like brand, price, or battery life of your product or service to be under scrutiny. Ensure these attributes align with your research goals, bolstering the relevance and impact of your study.

 

3. Determine Levels

Determine the different levels or variations within each attribute. Each attribute should have at least two levels, and the number of levels per attribute should be balanced to avoid bias.

 

4. Create Choice Sets

Choice sets are combinations of attributes for respondents to select from. For effective conjoint analysis, incorporate a comprehensive, but manageable range of attribute combinations that encapsulates all possible variants. Strive for diversity, ensuring the total choice sets remain feasible to prevent respondent fatigue.

 

5. Define The Choice Task

The choice task is a key aspect of surveys that requires respondents to evaluate products based on attributes, rank preferences, or determine purchase likelihood. Clarity is vital in defining this task, enabling effortless understanding and alignment with research objectives. Precision and comprehensibility are paramount in attaining valuable insights.

 

6. Determine Experimental Design

The experimental design is how the survey will be structured and presented to respondents. Factors such as the order of choice sets, the type of question layout, and response scales should all be considered when designing a conjoint survey. The design should ensure that each respondent sees a different combination of attribute levels to capture preferences accurately. 

 

7. Test And Validate

Before releasing the survey, it is essential to test and validate it. This involves piloting the survey with a small sample of respondents and making any necessary adjustments before launching the survey. Testing will help you identify any potential issues or errors that could impact the reliability of results. Additionally, validating the survey ensures that data collection is accurate and meaningful. 

 

8. Collect Data

Launch the survey and gather responses from your respondents.  Ensure that you monitor the data collection progress and that your sample has enough size to generate reliable outcomes. You invite more respondents if needed.  When enough responses have been collected, you can begin analyzing the data. 

 

9. Analyze Results

Once all responses have been collected, analyze them using appropriate techniques to draw meaningful insights from the survey. Conjoint analysis examples, such as traditional or choice-based conjoint analysis, can help determine the importance of different attributes by assigning them utility scores. 

 

10. Interpret And Apply Findings

The last step in designing a conjoint survey is to interpret the results. Analyze the data and draw meaningful conclusions from it. Use these findings to arrive at informed decisions about product development, marketing strategies,  pricing, or other relevant topics based on the research objective that can be implemented within your organization.

 

IDR Medical Is Here To Help

Pioneering a new medical device or service launch, or seeking an edge over competitors? Mastering healthcare market research and analytical technique is necessary for success.

IDR Medical is an esteemed international healthcare market research and consulting firm, guiding medical device and pharmaceutical industry leaders. With decades of combined expertise, we amalgamate in-depth medical market research, business analytics, and strategic problem-solving for healthcare projects. Supports clients through idea conception, development, market evaluation, pricing, and commercialization, leveraging hands-on guidance and collaborative exploration of growth avenues.

Our talented team design effective conjoint surveys to unearth actionable insights into consumer preferences. IDR Medical span diverse areas within the medical technology and pharmaceutical realm, working as partners in discovering innovative solutions for complex challenges.

Start taking advantage of the benefits and witness the transformative power of conjoint analysis today.

 

Book a FREE consultation

 

 

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