Success in Pricing Series Part Three: Understanding Conjoint Analysis (And When to Use It)

When working with clients  to set prices for products or services, we strive to strike the delicate balance between desired profitability and the customer’s willingness to pay. Three pricing research methods typically help us to do that: Van Westendorp, Gabor Granger, and Conjoint Analysis. In this three-part series, Success in Pricing, we have been discussing each technique in detail. Here, we’re wrapping up the series with a deep dive into the benefits of conjoint analysis, when to use it, and its limitations.

Conjoint Analysis

Best used for understanding trade-offs between features and price, conjoint analysis is a more sophisticated method that helps you understand how consumers value different attributes of a product or service, including price. Respondents are presented with different configurations, each with varying levels of features and price, and are asked to choose between them. This technique helps determine the value consumers place on each feature and how much they are willing to pay for specific features and the product or service as a whole.

When It Is Used:

• For Complex Products: If your product or service has multiple features that can be bundled in different ways, Conjoint Analysis helps to understand which features consumers value most and how these preferences translate into willingness to pay.
• To Optimize Product Design and Pricing: Conjoint Analysis is ideal when you need to make decisions about both product features and pricing. It helps you to determine the combination of features and price that will maximize market share or profitability.
• To Simulate Market Scenarios: This method allows you to simulate different market scenarios and predict how changes in price or features might affect consumer choices.

Limitations:

• Complexity: Conjoint Analysis is more complex to design and analyze than the other two methods. It requires careful consideration of the attributes included and often involves advanced statistical techniques to interpret the results.
• Requires More Data: Because it tests multiple attributes, Conjoint Analysis typically requires a larger sample size and more detailed data collection, which can take more time and budget.
• Translating Results into Strategies: While Conjoint Analysis provides valuable insights, implementing them effectively—especially in dynamic or competitive markets—can be complex. To bridge the gap it is important to make sure the insights are not only understood but also seamlessly integrated into strategies, turning data-driven models into practical, impactful pricing decisions. Advanis has helped marketers bridge the gap between research and practice.

New Payment Card Adoption Example: 

• Our financial services client needed to determine which features and benefits would improve uptake, penetration across customer segments at different price points, and perceptions of the card offer and acquisition targets. 

We conducted an online survey with 1000 customers and prospects in various target segments using choice-based conjoint to determine features and benefit uptake, reward levels, demand elasticity based on reward levels, and optimal card configuration across segments. 

Our client used the model inputs to feed into a pricing model to develop the optimal new card design amongst specific customer segments. 

For guidance on the best pricing research technique for your new product or service, please contact Advanis today.