Conjoint Analysis Simulator
Choice-Based Conjoint (CBC) simulation. Determine which product features drive purchasing decisions and how they trade off against price.
Product Attributes
Define the features you want to test.
Run analysis to view utility scores
Understanding Utilities
The higher the score, the more customers want it.
Relative Importance (Pie Chart)
This tells you what drives the decision. If 'Brand' is 60%, then features matter less than the logo.
Utility Scores (Bar Chart)
These are relative values. If 'Gold' is +50 and 'Silver' is +25, customers prefer Gold twice as much as Silver (roughly).
Negative Utility
It is normal for higher prices to have negative utility. The goal is to see if the positive utility of a feature (e.g., +100 for 1TB storage) outweighs the negative utility of the price increase.
Execution Steps
Define your Attributes (e.g., Screen Size, Battery Life, Price).
Define Levels for each attribute (e.g., 6 hours, 12 hours, 24 hours).
Run the analysis to simulate how a market segment might value these features.
View the 'Relative Importance' to see which attribute matters most.
View 'Part-Worth Utilities' to see the specific value of each option.
Pro Strategy
- Price is almost always the most important attribute. If it isn't, your price range might be too narrow.
- Use this data to build your 'Good, Better, Best' packaging tiers.
- Features with high utility but low cost to produce are your 'Margin Drivers'.
Core Concepts
Part-Worth Utility
A numerical score representing how much preference a consumer assigns to a specific feature level. Higher is better.
Relative Importance
The percentage weight a customer gives to an attribute when making a decision. (e.g., Price might be 50% of the decision, Color only 10%).
Trade-Off Analysis
Measuring what a customer is willing to give up (e.g., paying $100 more) to get a feature they want (e.g., more storage).
What is Conjoint Analysis Simulator?
Conjoint Analysis is a statistical technique used to determine how people value different attributes (features, functions, benefits) that make up an individual product or service. It presents respondents with choices (e.g., Option A vs Option B) to simulate real-world trade-offs.
Best For
- • Designing a new product: determining which features are 'must-haves'.
- • Optimizing packaging: finding the best combination of features for a specific price point.
- • Pricing strategy: understanding how much more you can charge for premium features.
Limitations
- • Complex to set up and analyze compared to simple surveys.
- • Assumes customers make rational trade-offs (which isn't always true for impulse buys).
- • Can be expensive to recruit respondents for a statistically significant sample.
Alternative Methods
MaxDiff Analysis
Best for simply ranking a long list of features from 'Most Important' to 'Least Important'.
Van Westendorp
Simpler if you only care about price, not feature trade-offs.
Industry Applications
See how this methodology generates real revenue uplift in different sectors.
Smartphone Manufacturer
Deciding whether to include a high-cost OLED screen in the base model.
Conjoint revealed that the screen type had very low importance compared to Battery Life for the target budget segment.
Hotel Chain
Should we offer free breakfast or free wifi?
Analysis showed 'Free Wifi' was a hygiene factor (expected), while 'Free Breakfast' had high positive utility.