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.

CapacityCostHeat Retention Time
18 oz$256 hours
18 oz$3212 hours
18 oz$4518 hours
24 oz$326 hours
24 oz$4512 hours
24 oz$5024 hours
30 oz$2512 hours
30 oz$3218 hours
36 oz$456 hours
36 oz$5024 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.


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