Rely on our extensive experience in providing Conjoint Strategy and Implementation such as:

• Create experimental designs with high realism and data model efficiency simultaneously.
• Use techniques for reducing the hypothetical bias among respondents - introducing the Dual None option, follow-up rating questions, and incentives management that would drive more realistic respondents' choices.
• Implement optimization methods like exhausted search and genetic and random algorithms to search for the most valuable product according to different objectives, such as maximal profit, revenue, and/or penetration rate.
• Choose options for fine-tuning the market-predicted shares.
• Advise on the benefits and weaknesses of using prohibitions, conditional pricing, partial profile, and alternative-specific designs.
• Pick up from a set of alternatives to build better shelf test designs.
• Implement strategies for dynamic designs dealing with many products features. Build dynamic lists of attributes, levels, and/or implement ACBC (adaptive conjoint).
• Create complex designs and model prediction of more than one dependent variable: how to implement menu-based conjoint, situational choice experiments.

We will build your conjoint and will navigate you through your data.