Choice Modeling Consulting for Research Agencies and Advisory Teams
Red Analytics provides independent conjoint analysis consulting for market research agencies, management consulting firms, and senior business decision-makers. We help clients define the decisions their research must support, establish the evidence requirements, and develop conjoint models that connect customer preferences to business consequences.
Our role extends beyond experimental design and statistical estimation. We provide the senior analytical judgment needed to ensure that conjoint research produces a defensible foundation for strategic recommendations.
Conjoint Analysis for Pricing, Innovation, and Portfolio Decisions
Conjoint analysis helps businesses evaluate decisions involving pricing, product innovation, competitive positioning, and portfolio management. Red Analytics works with clients to define the relevant business alternatives, establish the competitive context, and design studies capable of informing those decisions.
Common Business Applications
- Pricing: Evaluate price changes, demand response, willingness to pay (WTP), revenue, and profitability.
- Product innovation: Identify attractive product configurations and evaluate opportunities for new products and services.
- Portfolio management: Estimate substitution, cannibalization, and incremental profit across product portfolios.
- Market expansion: Evaluate opportunities to attract additional buyers and enter adjacent markets.
Four Profit Horizons
Conjoint analysis can support decisions across four distinct horizons:
- Short-term optimization: Improve the profitability of existing offerings and portfolios.
- Innovation expansion: Identify new products and services that generate incremental profit.
- Penetration and accessibility expansion: Reach additional buyers by addressing barriers to purchase.
- Future growth and adjacencies: Evaluate opportunities beyond the current portfolio or category.
The appropriate research design, competitive context, and analytical approach should follow the economic decision being made.
Conjoint Market Simulation and Financial Decision Support
Conjoint analysis estimates how customers value competing alternatives and how their choices may change under different market conditions. Market simulators translate these estimates into scenarios that decision-makers can evaluate.
However, predicted market share is not the ultimate objective. A product can gain share without creating incremental profit, particularly when it cannibalizes existing products or requires substantial investment. Financially useful conjoint analysis connects simulated customer choices to assumptions about category volume, substitution, contribution margins, and costs. This allows decision-makers to evaluate economic consequences rather than simply identify the most preferred product.
The objective is not to produce an impressive conjoint simulator. It is to establish a defensible analytical foundation for decisions that affect profitability.
Conjoint Research Design and Bayesian Modeling
Conjoint analysis encompasses several approaches to studying customer preferences. Choice-based conjoint (CBC) uses discrete choice experiments in which respondents select among competing alternatives. Discrete choice modeling also extends beyond conventional conjoint applications.
Conjoint analysis consulting includes experimental design, survey research specifications, statistical estimation, and market simulation. The appropriate method depends on the business decision, target population, and competitive environment.
Dual-response designs can be particularly useful when distinguishing preference among alternatives from the decision to purchase.
Bayesian Conjoint Model Estimation
Our modeling work includes hierarchical Bayes (HB), HB-MNL, and mixed logit models. Hamiltonian Monte Carlo (HMC) provides a modern alternative to traditional Gibbs sampling for Bayesian estimation.
Commercial conjoint platforms such as Sawtooth Software, Conjointly, Displayr, and Qualtrics provide tools for common research applications. Red Analytics maintains an independent modeling codebase, allowing estimation methods and analytical approaches to be adapted to the specific decision rather than limited by the capabilities of a general-purpose software platform.
Methodological Development and Evidence Standards
Red Analytics maintains an independent research environment for evaluating conjoint methods, experimental designs, and model estimation procedures.
Emerging approaches are evaluated against explicit evidence standards before being incorporated into client work. The objective is not to offer the longest menu of conjoint techniques. It is to select methods that have demonstrated their value for the decisions clients need to make.
Conjoint Model Validation and Predictive Performance
A conjoint model that fits observed survey responses well does not necessarily predict choices accurately for new respondents. In-sample fit and predictive generalization represent different evidence requirements.
Testing Generalization
Our preferred validation approach evaluates predictive performance using respondents excluded from model estimation.
This provides a more demanding test of generalization than holding out additional choice tasks from respondents whose other responses contributed to estimation.
Economic Coherence
Predictive accuracy is necessary but not sufficient. Conjoint models used for pricing and market simulation should also produce economically coherent results.
These include plausible price-response relationships, revenue curves, and substitution patterns appropriate to the business context.
Minimum Evidence Standard
Red Analytics maintains an original research program called the Minimum Evidence Standard (MES) for discrete choice modeling.
The program examines two distinct requirements: predictive generalization to new respondents and defensible inference from estimated models. These requirements are directly relevant to choice-based conjoint analysis, particularly when research findings are used to justify pricing, product, and portfolio decisions.
The objective is to establish whether a model provides sufficient evidence for its intended decision rather than relying on conventional model fit statistics alone.
Evidence of Conjoint Expertise and Original Research
Red Analytics combines practical conjoint analysis consulting with an independent program of original methodological research.
Our work examines how choice experiments are designed, preference models are estimated, predictions are validated, and research findings are translated into business decisions. This includes published studies of respondent covariates, predictive generalization, and the evidence required for reliable model-based inference.
Our research informs the methods we recommend and the analytical judgments we make in client engagements. For more information about our consulting experience and original publications, visit our About Red Analytics page.
Conjoint analysis consulting services are available for individual analytical engagements and larger research programs. Red Analytics works directly with business decision-makers and as a specialist partner to research agencies and consulting firms.
