User Preference

Author:
Seth Orsborn, Peter Boatwright, Jonathan Cagan
Company/School:
School of Management , Bucknell University, Tepper School of Business, Carnegie Mellon University

USER PREFERENCE
BETTER DESIGN BY LEVERAGING MARKETING AND ENGINEERING TOOLS

Seth Orsborn, School of Management , Bucknell University
Peter Boatwright, Tepper School of Business, Carnegie Mellon University
Jonathan Cagan, Department of Mechanical Engineering, Carnegie Mellon University

Within organizations there are always inherent conflicts when divisions with differing goals attempt to reach a consensus. While a fully integrated interdisciplinary design process is ideal, very few industries have adopted this method which is commonly found in design firms and taught in design schools. One of the greatest sources of conflict is in how different disciplines approach problems and determine which solutions are valid. While design understands the value of qualitative research, informed intuition, and focus on the user, engineering and marketing tend to rely upon quantitative measures that produce numbers they can “trust”. Recently, there has been some acceptance of design methods by engineering and marketing, which has in turn improved relationships between the disciplines. But, there is significant potential for design to further improve relationships while plundering marketing’s toolbox for their own use. This would give design some of the analytical reasoning to convince engineering and marketing of the value of their intuition while providing further insight into the needs, wants, and desires of users.

One tool in particular has the potential to significantly improve designer insight while providing designers with the leverage they need to convince marketing and engineering: utility functions. Utility functions were first used by economists to describe the hypothetical rational consumer. They are accepted by both engineering and marketing as an analytical method for determining preference, though neither of these disciplines use utility functions for qualitative or aesthetic design purposes. Any product attribute that can be represented with a numerical function (e.g. shape or color) can be represented using a utility function and user preference for rough concepts can be determined through carefully constructed user research. While this will never produce the “optimal” design that engineers are looking for, it can present designers with a constrained design space that users are most likely to prefer. This information can then be used by designer to inform, but not limit, their design decisions as they move through to finalized designs. Furthermore, this information can be leveraged by designers to support their design decisions and provide validation to prevent engineering and marketing from making detrimental design modifications.

 

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