In a comprehensive YouTube masterclass titled "Conjoint Analysis Masterclass: Launch your products and features the right way," Akhil from Monetizely breaks down what many consider the gold standard of pricing and feature optimization research. This video offers SaaS executives and product leaders a detailed guide to understanding how customers truly value different product features and what they're willing to pay for them.
What Is Conjoint Analysis and Why Does It Matter?
Conjoint analysis is a powerful research methodology that helps businesses understand precisely how customers value different attributes or features of a product or service. As Akhil explains, "At its core, conjoint analysis is the research technique that helps you understand precisely how your customers value different attributes or features of a product or service."
The magic of conjoint analysis lies in its approach. Rather than directly asking customers about feature preferences or pricing, it simulates actual buying decisions by presenting customers with different product configurations and asking them to choose between them repeatedly. This reveals true preferences through observed choices rather than stated preferences.
"You see, when you directly ask people how much they value a specific feature, like say, how important is battery life in your smartphone purchase decision? you will often get answers like 'very important,' but so will screen size, camera quality, storage and so on. Suddenly everything is 'very important,' which is not very helpful for making product decisions," Akhil points out.
Why Conjoint Analysis Has Become the Go-To Research Method
There are several reasons why conjoint analysis has become the preferred method for serious product and pricing decisions:
- Real-world accuracy: By simulating marketplace decisions, conjoint analysis provides highly reliable results that reflect actual customer behavior.
- Detailed feature-level insights: The method clearly identifies the value each specific feature adds to your product, guiding smarter development and prioritization decisions.
- Optimal pricing clarity: Conjoint shows exactly how much customers are willing to pay for each feature or combination of features, enabling optimized pricing tiers.
- Market scenario simulation: Once you have conjoint results, you can model how different product configurations would perform in the market before investing in development.
As Akhil emphasizes, "The business impact of these insights can be tremendous. Conjoint helps you design products that customers actually want, optimize pricing by understanding exactly how much customers value each feature, simulate market scenarios before launch, and identify distinct customer segments with different preference patterns."
Types of Conjoint Analysis
The video outlines several types of conjoint analysis, each suited to different business needs:
- Choice-Based Conjoint (CBC): The most common method today, where customers choose their favorite from several full product profiles. "CBC is the gold standard for most modern applications, particularly for consumer products, SaaS, and digital services," explains Akhil.
- Adaptive Conjoint Analysis (ACA): Adjusts questions dynamically based on earlier answers, making it ideal for testing many features (10+).
- Full Profile Conjoint: Shows complete products described by all features at once, with respondents rating each profile on a scale rather than choosing between alternatives.
- Menu-Based Conjoint: Perfect for products where customers build their own packages by selecting from a menu of options.
How to Perform a Conjoint Analysis: Step-by-Step
Akhil provides a practical approach to conducting an effective conjoint analysis:
Step 1: Identify Your Attributes and Levels
Start by identifying the specific features or attributes you want to test. For a project management software, these might include storage capacity, integration capabilities, reporting features, and pricing tiers. Keep the total number manageable (typically 4-7 attributes).
Step 2: Design Your Conjoint Survey
Create multiple product profiles with different combinations of attributes and price points using specialized software. Respondents will typically answer 8-15 choice tasks, each asking them to select from 3-5 product options.
Step 3: Collect Responses
Run your survey among a representative sample of target customers. "Usually, you should aim for at least 200-300 responses for a statistically significant insight," Akhil advises. Quality matters as much as quantity—respondents should be genuinely interested in your product category.
Step 4: Analyze the Results
This is where the data is processed to reveal how customers value each feature (part-worth utilities) and their price sensitivity. "Higher utilities indicate features customers value more. Lower utilities obviously indicate features less critical to customer choices," explains Akhil.
Step 5: Interpret and Implement
Use the insights to design product bundles, pricing tiers, and feature roadmaps aligned with customer preferences and willingness to pay. You can also simulate market scenarios to predict how different product configurations would perform.
Key Outputs and Insights from Conjoint Analysis
A well-executed conjoint analysis provides several valuable outputs:
- Part-Worth Utilities: Numerical values showing how much each feature level contributes to customer preference. Akhil explains with an example: "If unlimited storage has a utility of 2.5, when 24 support has a utility of 0.8, you know storage is roughly three times more valuable to customers than premium support."
- Importance Scores: Percentages showing how much influence each attribute has on the overall decision.
- Monetary Value Calculations: Since price is tested as an attribute, you can calculate the monetary worth of each feature to customers.
- Price Elasticity: Understanding how much customers are willing to pay for different feature combinations.
- Market Simulation: Predicting market share for different product configurations and against competitors.
- Segmentation Insights: Discovering distinct customer segments with different preferences.
Real-World Applications: Case Studies
Akhil shares three compelling case studies demonstrating the practical application of conjoint analysis:
Case 1: SaaS Product Optimization
A B2B project management software company redesigned their pricing tiers using conjoint analysis with 350 potential customers. The results revealed three distinct customer segments with different needs and preferences. By creating targeted pricing tiers for these segments, the company increased total revenue by 65% compared to their previous pricing structure.
Case 2: Consumer Electronics Feature Prioritization
A smartphone manufacturer planning their next generation of devices used conjoint analysis with 2,000 customers. The results showed that while their engineering team had been focusing on processing power, camera quality and battery life were three times more important to customers. The company reallocated R&D budgets based on these insights, achieving 40% higher first-month sales than the previous generation.
Case 3: Cloud Storage Service Pricing
A cloud storage SaaS needed to decide which features justified premium pricing tiers. After testing various options, they discovered customers highly valued Salesforce integration and unlimited storage but placed minimal value on phone support. The company restructured their tiers based on these insights, increasing conversion rates by 34% and average revenue per user by 27%.
Strengths and Limitations of Conjoint Analysis
Like any research methodology, conjoint analysis has both strengths and limitations:
Strengths:
- Extremely accurate insights reflecting real-world behavior
- Quantified feature values
- Ability to calculate monetary values for features
- Market simulation capabilities
- Segmentation analysis revealing preference differences
Limitations:
- More complex to execute than simpler methods
- Requires significant investment in time, expertise, and budget
- Can overwhelm respondents if too many attributes are included
- Limited in how many attributes it can realistically include
- Works best with concrete, easily described features
As Akhil notes, "When done correctly, the insights from conjoint analysis are so valuable that they typically far outweigh these limitations, especially for major product and pricing decisions."
When to Use Conjoint Analysis
Conjoint analysis is most valuable when:
- Launching a new product or major feature set
- Evaluating multiple features and needing clarity on their relative value
- Restructuring pricing tiers
- Understanding competitive positioning
- Identifying customer segments based on preferences
However, it might not be necessary when:
- You need a simple price range for a straightforward product
- You have very limited time or budget for research
- Your product is truly revolutionary with no market context
Common Pitfalls to Avoid
Akhil identifies several common mistakes in conjoint studies and how to avoid them:
- Attribute Overload: Including too many features overwhelms respondents. Focus on 4-7 key attributes that drive purchase decisions.
- Unrealistic Price Ranges: Setting prices that are too extreme distorts results. Use ranges that reflect realistic market conditions.
- Vague Attribute Descriptions: When respondents don't clearly understand features, they can't make informed choices. Use specific, quantifiable descriptions.
- Poor Sample Quality: Survey respondents must be in your target market with relevant purchase interests.
- Ignoring Segmentation: Looking only at aggregate results can hide crucial differences between customer groups.