May 3, 2024 by Cyril Noirot
Conjoint analysis for measuring customer willingness to pay
Introduction
Understanding what drives customer preferences is crucial for creating successful products. Conjoint analysis is a powerful tool that helps businesses identify which product attributes matter most to their customers and how much they are willing to pay for them.
In this article, we’ll explore the methodological approach of conjoint analysis and demonstrate its application using a practical example. Whether you’re a data scientist or a business stakeholder, this guide will provide valuable insights into leveraging conjoint analysis for strategic decision-making.
What is conjoint analysis?
Conjoint analysis is a statistical technique used in market research to determine how people value different attributes of a product or service. By presenting respondents with a series of product profiles that vary systematically, we can estimate the relative importance of each attribute and the trade-offs consumers are willing to make.
Methodological Approach
Step 1: Define attributes and levels
The first step in conjoint analysis is to identify the key attributes of the product and the levels for each attribute. For our example, let’s consider a coffee mug with the following attributes:
- Capacity: 18 oz, 24 oz, 30 oz, 36 oz
- Cost: $25, $32, $45, $50
- Heat retention time: 6 hours, 12 hours, 18 hours, 24 hours.
Step 2: Create product profiles
Next, we create various combinations of these attributes to form product profiles. Each profile represents a unique combination of attribute levels.
Capacity | Cost | Heat Retention Time |
---|---|---|
18 oz | $25 | 6 hours |
18 oz | $32 | 12 hours |
18 oz | $45 | 18 hours |
24 oz | $32 | 6 hours |
24 oz | $45 | 12 hours |
24 oz | $50 | 24 hours |
30 oz | $25 | 12 hours |
30 oz | $32 | 18 hours |
36 oz | $45 | 6 hours |
36 oz | $50 | 24 hours |
Step 3: Collect preference data
We survey respondents and collect their preference ratings for each profile on a scale of 1 to 5. Here’s an example dataset for five respondents:
Step 4: Estimate utility values
Using a linear regression, we estimate the utility values for each attribute level. For instance, the utility values might be:
- Capacity: 24 oz (0.39), 30 oz (0.56), 36 oz (-0.32)
- Cost: $25 (-0.48), $32 (0.18), $45 (0.46), $50 (-0.16)
- Heat retention: 6 hours (-0.49), 12 hours (0.52), 18 hours (0.68), 24 hours (-0.71)
Step 5: Compute willingness to pay
To find the increased willingness to pay for extending the heat retention time from 12 hours to 18 hours, we calculate the utility difference and convert it into monetary terms:
$$ Δutility=uv_{18h} − uv_{12h} $$
Conclusion
Conjoint analysis provides a robust framework for understanding customer preferences and quantifying their willingness to pay for specific product features. By following the steps outlined in this article, you can leverage this methodology to inform product design and pricing strategies, ensuring your offerings align with customer needs and maximize market success.
🚀 Interested in applying conjoint analysis to your products? Feel free to reach out for a detailed consultation.