Choice Modeling Consulting for Research Agencies and Advisory Teams
Red Analytics provides independent choice modeling consulting for market research agencies, management consulting firms, and senior business decision-makers. We help clients define the decisions their research must support, determine what evidence is required, and develop choice models that connect customer preferences to business consequences. Our role extends beyond analytical execution to ensuring that the research provides a defensible foundation for strategic recommendations.
Choice Modeling for Pricing, Innovation, and Portfolio Decisions
Choice modeling helps businesses evaluate decisions about pricing, product innovation, competitive positioning, and portfolio management. Red Analytics works with clients to identify the decisions that matter, establish the relevant competitive context, and develop models that inform 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 new product opportunities.
- Portfolio management: Understand substitution, cannibalization, and incremental profit.
- Market expansion: Evaluate opportunities to reach additional buyers and enter adjacent markets.
Four Profit Horizons
Choice modeling can support decisions across four distinct horizons:
- Short-term optimization: Improve the profitability of existing products and portfolios.
- Innovation expansion: Identify new offerings that create incremental profit.
- Penetration and accessibility expansion: Reach additional buyers by addressing barriers to purchase.
- Future growth and adjacencies: Evaluate opportunities beyond the current product portfolio or category.
The research design, competitive context, and analytical approach should follow the economic decision being made, not the other way around.
Market Simulation and Financial Decision Support
Choice models produce estimates of how customers distribute their preferences among competing alternatives. Market simulators translate those estimates into scenarios that decision-makers can explore.
But predicted market share is not the ultimate objective. A product can gain share without generating incremental profit, particularly when it cannibalizes existing products or requires substantial investment.
Financially useful choice modeling connects simulated choices to assumptions about category volume, substitution, margins, and costs. This allows decision-makers to evaluate the economic consequences of their alternatives rather than simply identify the most preferred option.
The objective is not to produce an impressive simulator. It is to provide a defensible analytical foundation for decisions that affect profitability.
Discrete Choice Experiments and Bayesian Modeling
Discrete choice experiments (DCE), also known as choice-based conjoint (CBC), measure how customers make trade-offs among competing alternatives. Research designs are developed around the business decision, the target population, and the competitive environment. Dual-response designs are often useful when distinguishing product preference from the decision to purchase.
Bayesian Choice Model Estimation
Our modeling work includes hierarchical Bayes (HB), HB-MNL, and mixed logit models, with Hamiltonian Monte Carlo (HMC) providing a modern alternative to traditional Gibbs sampling.
Commercial platforms such as Sawtooth Software, Displayr, Qualtrics, and Conjointly provide tools for common research applications. In contrast, our independent modeling codebase allows us to adapt estimation methods and analytical approaches to the specific decision being investigated rather than the constraints of a general-purpose software platform.
Methodological Development and Evidence Standards
Red Analytics maintains an independent research environment for evaluating new choice modeling methods, experimental designs, and estimation routines. Emerging approaches are tested against explicit evidence standards before being incorporated into client work.
The objective is not to offer the longest menu of techniques. It is to use methods that have demonstrated their value for the decisions clients need to make.
Choice Model Validation and Predictive Performance
A choice model that fits the observed survey data well does not necessarily predict choices accurately for new respondents. In-sample fit and predictive generalization are different requirements.
Testing Generalization
Our preferred validation approach evaluates predictive performance on respondents excluded from model estimation. This is a more demanding test of generalization than holding out additional choice tasks from respondents whose other responses were used to estimate the model.
Economic Coherence
Predictive accuracy is not sufficient on its own. Models used for pricing and market simulation should also produce economically coherent results, including plausible price-response and revenue relationships.
Minimum Evidence Standard
Red Analytics is adding to its original research program called the Minimum Evidence Standard (MES) for discrete choice modeling. The program examines two distinct requirements: generalization to new respondents and defensible inference from estimated models.
The objective is to establish whether a model provides sufficient evidence for the decisions it is intended to support, rather than relying on model fit or conventional diagnostics alone.
Evidence of Expertise and Original Research
Red Analytics combines practical choice modeling experience with an independent program of original methodological research. Our work examines how discrete choice models are designed, estimated, validated, and translated into business decisions.
This research includes published studies of respondent covariates, predictive generalization, and the evidence required to support reliable model-based inference. Findings inform our consulting practice and the analytical methods we recommend.
For more information about our experience, original research, and publications, visit our About Red Analytics page.
Choice modeling 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.