Alpine
Discrete Choice Modelling at Alpine: converting customer preferences into sharp product choices
Information about the case
Product development becomes stronger when you see which choices customers actually make.
Discrete Choice Modelling helps organisations understand which product features have the most influence on customer choices. For Alpine, Sprint used this method to test design options for a new product. This allowed Alpine to make choices based on customer behaviour, rather than just assumptions, internal preferences, or voiced opinions.
The question
Alpine wanted to know which product features make the difference
Alpine faced an important product choice. A new product was under development, with several possible design options. The question was which combination would best meet the needs of the target group.
In product development, customer feedback, experience, and internal expertise often play a major role. However, these sources do not provide a complete picture of choice preferences. What people say they find important sometimes differs from what they choose when they have to make trade-offs.
Therefore, Alpine needed insight into actual choice behaviour. Which characteristics carry the most weight? Which combinations are most convincing? And which product features add less value than expected?
The challenge
Product development requires evidence of customer behaviour
Developing new products means making choices. About shape, material, colour, price, performance, and other features. Every choice influences attractiveness, positioning, and distinctiveness.
At the same time, these choices are often difficult to prioritise. Internal preferences can differ. Customers may mention multiple features as important during conversations. Furthermore, it is only in a realistic choice situation that it becomes clear what people actually go for.
Discrete Choice Modelling then provides guidance. The method lets customers choose between different product variants. This creates a clearer picture of the trade-offs they make and the features that drive their choice.
The approach
Discrete Choice Modelling as a choice experiment
Sprint set up a Discrete Choice Modelling study for Alpine regarding the design options for the new product. Respondents were shown product variants with different combinations of features multiple times.
Instead of just asking what they found attractive, participants had to choose. This made it clear how they made trade-offs between product features.
The analysis then showed which features most strongly drive the choice process. It also became clear which combinations had more persuasive power and which features were less decisive for preference.
This approach is well-suited to issues surrounding product development, proposition development, and portfolio optimisation. Additionally, DCM helps with choices where price, design, material, colour, or functional features together determine how attractive a product is.
<span data-metadata=""><span data-buffer="">the result
Alpine received direction for design choices
The research gave Alpine a clearer picture of the product features that customers find important when making a choice. This allowed the team to prioritise more effectively in the further development of the product.
The insights helped to base design choices on concrete customer preferences. This gave direction to the product strategy and clarified which combinations best met market needs.
Exact results and model outcomes remain outside this case. The core is that with Discrete Choice Modelling, Alpine gained more control over the choices that matter to the target group.
Consequence of the result
how the insight led to action.
Better-informed choices.
Alpine was able to assess design options based on customer behaviour instead of personal preferences.
Sharp priorities.
The research showed which product features deserved priority and which combinations were most promising.
Customer-centric product development.
The insights gave direction to decisions regarding market fit, positioning, and differentiation.
In practice.
What is Discrete Choice Modelling?
Discrete Choice Modelling is a research method where respondents choose between different product variants. By analysing those choices, you see which features drive customer preferences.
How does Discrete Choice Modelling help with product development?
DCM shows which product features add value for customers. This allows teams to prioritise more sharply and make choices that better align with the market.
What is the difference between DCM and regular customer feedback?
Regular customer feedback lets you hear what people say. Discrete Choice Modelling shows what people choose when they have to weigh up multiple options.
For which issues is DCM suitable?
DCM is suitable for product development, pricing strategy, proposition development, portfolio optimisation, and choices between feature combinations.
Want to know more?
Do you want to know which product features make the difference for your customers?
Please contact Jean-Pierre. We can then look together at how Discrete Choice Modelling can sharpen your product choices.
