Alpine faced a decisive moment: a new product concept was on the table, with several possible design directions. The challenge was to identify which combination of features would genuinely matter to future buyers. Traditional feedback alone wasn’t enough. To cut through assumptions and internal debate, Alpine needed evidence of what customers would actually choose, rather than what they say they prefer. Discrete Choice Modelling (DCM) provides the solution.

What is Discrete Choice Modelling? 

DCM is a specific method within conjoint analysis which simulates real-world decision-making by asking consumers to choose between product variants, each made up of different combinations of attributes. By analyzing these choices, you can gain insights into actual customer behavior and preferences. This helps you identify which product features provide the most value and which are less important.

Why Discrete Choice Modelling is a game changer for product development 

DCM offers several advantages for organizations looking to innovate with a customer-focused approach:

• Evidence-based insight: No assumptions. Only data that reveals which features truly make a difference.

• Focused innovation: Focus on features that matter most to the customer.

• Value optimisation: Discover which feature combinations are the most attractive, such as combinations of price, material, and color. 

• Stronger positioning: Mapping differences in preferences between target groups. 

• Reduced risk: Make decisions based on facts, not gut feelings or internal preferences.

Our approach with Alpine 

We built a tailored choice experiment around Alpine’s design options. Instead of asking respondents what they liked, we observed how they navigated trade-offs. Each participant repeatedly chose between product variants that differed in material, acoustic performance, price, colour options and other relevant features. 

The resulting model quantified the importance of each attribute and exposed which combinations maximised appeal. Alpine gained a behavioural map of consumer decision-making, not just surface-level preferences. 

What Alpine gained from It 

Thanks to the insights from the model, Alpine was able to prioritize more effectively. They could base design choices on concrete customer preferences, leading to a product that better matched market needs and offered stronger differentiation. 

This approach gave Alpine the confidence to make decisions that were widely supported within the team because they were based on solid data. 

Do you also want to know what truly matters to your customers? 

With Discrete Choice Modelling, you turn customer behavior into concrete, data-driven decisions. Discover what this approach can mean for your brand. Feel free to contact us. We’re happy to help!