Enable your clients to predict how their market will react to changes before they make them –
put your customers mind in a computer and ask it how it will react to future scenarios
Choice models and conjoint analysis are survey-based methods that simulate real customer decision-making by having respondents make trade-offs between product features, prices, and benefits. Used by product teams, marketers, and business strategists, these models provide data-driven guidance for designing or refining products and services. They matter because they reveal which changes truly drive customer preference and demand, helping businesses prioritize features, optimize offerings, and craft communications that maximize market impact and profitability.
Choice models take typical scale-based survey measurement of needs and priorities to the next level of:
Conjoint choice models are significantly more predictive than traditional importance or attitude questions because they measure actual decision-making, not stated opinions. By observing how customers make trade-offs between features and prices, these models reveal true preferences and provide much more accurate forecasts of market behavior.
Conjoint choice methods are all about trade-offs and as such provide a much more practical and tangible roadmap and decision priorities than traditional scale-based measures. By having attributes and specific levels of those attributes tested, these models are specifically built to address specific managerial questions of what and how to offer products and services.
Rigorous, flexible methods to model real-world choices and forecast market behavior.
Simulates customer decision-making by having respondents choose between competing products or features, revealing true preferences.
Efficiently handles large feature sets by showing only relevant attributes per choice task, reducing respondent fatigue.
Combines binary choice and preference ranking to improve accuracy in capturing true market demand.
Ensures respondents can manage complex choice tasks without sacrificing data quality or reliability.
Guides how many respondents are needed to achieve robust, statistically valid insights across target segments.
Accounts for unrealistic or overly dominant options in the choice set to maintain realistic results.
Measures how changes in price impact demand and preference, enabling optimized pricing strategies.
Confirms model reliability by testing predictions across multiple scenarios, ensuring actionable and accurate insights.
Want to see this with your data? Request a demo and we’ll run a short mapping and show a live simulation.
1 week after data collection and cleaning
Additional time may be needed for updates or custom refinements
Provided only for specialized needs; standard programming is not included
We typically set up a training session for simulator use and continue to provide support for questions about the model, its interpretation, and practical application.
Invoicing is on a Net-30 basis: 50% is invoiced when work begins on the project, and 50% is invoiced upon delivery of the simulator and model results.
Every couple of months, receive insightful blog entries that use case studies, examples and demonstrations to show you how to get the most out of your advanced analytics and to see dangers you can avoid. This is a new series of posts that come directly from real experience happening right now!
