Conjoint Analysis

By Philip Burgess | UX Research Leader

Understanding what drives customer choices is a challenge for many businesses. Conjoint analysis offers a practical way to uncover how people value different features of a product or service. This method helps companies design offerings that better match customer preferences, leading to improved satisfaction and sales.

Survey form showing product attributes and customer ratings

What Is Conjoint Analysis?

Conjoint analysis is a research technique used to determine how people make decisions when faced with multiple options. Instead of asking customers directly what they want, it presents them with different combinations of product features. Respondents choose or rank these combinations, revealing the relative importance of each feature.

For example, a smartphone company might test how customers value battery life, camera quality, price, and brand. By analyzing choices, the company learns which features influence buying decisions the most.

How Conjoint Analysis Works

The process involves several steps:

• Define attributes and levels: Identify key product features and possible variations. For a car, attributes could be fuel type, color, price, and safety rating.

• Create product profiles: Combine attributes into different product options. Each profile shows a unique mix of features.

• Collect customer preferences: Ask participants to rank, rate, or choose between profiles.

• Analyze data: Use statistical models to estimate the value customers place on each attribute level.

  
  

This approach mimics real-world decision-making, where buyers weigh trade-offs rather than evaluate features in isolation.

Types of Conjoint Analysis

There are several types of conjoint analysis, each suited for different research needs:

• Traditional Conjoint: Respondents rank or rate full product profiles. It works well for simple products with few attributes.

• Choice-Based Conjoint (CBC): Participants choose their preferred option from sets of profiles. This method reflects actual buying behavior more closely.

• Adaptive Conjoint Analysis (ACA): The survey adapts based on previous answers, focusing on the most relevant attributes for each respondent.

• Hierarchical Bayes Conjoint: A statistical technique that improves individual-level preference estimates, often used with CBC.

  
  

Choosing the right type depends on the product complexity and research goals.

Practical Uses of Conjoint Analysis

Businesses use conjoint analysis in many ways:

• Product design: Identify which features to include or improve. For instance, a coffee maker brand might discover customers prefer programmable timers over fancy displays.

• Pricing strategy: Understand how price changes affect demand. A software company could test different subscription fees to find the optimal price point.

• Market segmentation: Group customers by preference patterns. This helps tailor marketing messages and product versions.

• Competitive analysis: Compare your product features with competitors to find gaps or advantages.

  
  

Example: Choosing a Laptop

Imagine a company wants to launch a new laptop. They select attributes like screen size, processor speed, battery life, and price. By running a conjoint survey, they find customers value battery life twice as much as screen size and are willing to pay more for faster processors. This insight guides the product development team to focus on battery improvements and processor upgrades while keeping screen size moderate.

Laptop surrounded by icons representing screen size, battery, processor, and price

Benefits of Using Conjoint Analysis

• Realistic insights: Reflects how customers make trade-offs in real life.

• Quantifies preferences: Assigns numerical values to feature importance.

• Supports decision-making: Helps prioritize features and set prices.

• Reduces risk: Tests concepts before costly product launches.

  
  

Limitations to Consider

While powerful, conjoint analysis has some limits:

• Complex surveys: Too many attributes or levels can overwhelm respondents.

• Assumes rational choices: People may not always behave logically.

• Requires careful design: Poorly chosen attributes lead to misleading results.

• Data interpretation: Statistical analysis can be complex and needs expertise.

  
  

Tips for Effective Conjoint Studies

• Keep the number of attributes manageable, ideally 4 to 6.

• Use clear, simple language for feature descriptions.

• Pilot test surveys to catch confusing questions.

• Combine conjoint results with other research methods for a fuller picture.