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!
Our conjoint analysis consulting services support conjoint market research with advanced discrete choice modeling, helping market research teams predict customer preferences, optimize pricing, evaluate product features, and make confident product and portfolio decisions.
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.
Conjoint analysis is one of the most effective market research techniques for understanding how customers evaluate products, pricing, and feature combinations before they make purchasing decisions. At The Analytics Team, our conjoint analysis consulting services help market research teams design robust studies, estimate reliable models, and translate complex data into actionable business recommendations. Whether you’re launching a new product, optimizing pricing, or refining your product portfolio, we deliver advanced conjoint market research that supports confident decision-making.
Measures real-world customer preferences through realistic product and pricing trade-off decisions.
Personalizes choice tasks to capture deeper customer preferences for complex products.
Evaluates customer decisions to optimize products, pricing, and market positioning strategies.
Creates statistically efficient surveys that generate reliable, decision-ready market research insights.
Quantifies customer preferences to measure attribute importance and trade-offs accurately.
Predicts market share, pricing outcomes, and product performance across competitive scenarios.
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.
Our choice modeling combines advanced analytics, study design, data modeling, and market simulation to support confident business decisions that are actionable in the real world, not just statistically interesting.
Against
Failures
Years Experience
Solutions Evaluated
Typing Accuracy
Against Failures
Want to see this with your data? Request a demo and we’ll run a short mapping and show a live simulation.
Our choice modeling consulting services have been applied across a wide range of industries, helping organizations uncover customer preferences, evaluate competitive trade-offs, and make data-driven decisions tailored to their industry.
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
Choice Modeling Consulting helps organizations design, analyze, and interpret choice experiments to understand customer preferences, optimize products, improve pricing, and support confident business decisions through data-driven insights.
A Discrete Choice Modeling Consultant designs choice experiments, estimates customer preference models, builds market simulations, and delivers actionable insights for product development, pricing, and market strategy.
Conjoint Analysis Consulting helps market research teams design conjoint studies, estimate customer preferences, evaluate feature trade-offs, and optimize products, pricing, and portfolio decisions.
Use Conjoint Market Research when you need to understand how customers value product features, pricing, brands, or service attributes before launching a new product or making strategic decisions.
Choice-Based Conjoint (CBC) presents fixed choice tasks to all respondents, while Adaptive Choice-Based Conjoint (ACBC) customizes questions based on previous answers, making it ideal for more complex products and purchase decisions.
Yes. We regularly partner with market research agencies as market research analytics experts, providing Choice Modeling Consulting, Conjoint Analysis Consulting, advanced modeling, and market simulation expertise.
Yes. We develop interactive market simulators that forecast customer preferences, market share, pricing outcomes, and product performance under different business scenarios.
When designed correctly with appropriate sample sizes and realistic choice tasks, discrete choice models provide highly reliable predictions of customer preferences and real-world purchase behavior.
Sample size depends on your study design, number of attributes, and research objectives. We recommend the optimal sample size for each Choice Modeling or Conjoint Analysis study to ensure statistically reliable results.
If you see an opportunity to provide a choice model or conjoint solution, please email or call. The team will help you win the opportunity and provide any necessary materials or support throughout the proposal process.
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!
