No matter who your end user is, whether they are a consumer or a traveler, they will have a preference when given a set of choices. Understanding behavior in a complex system—how and why a person chooses between competing alternatives—is critical to the design and positioning of all goods and services. This is true across a range of industries and activities, from the decision to purchase a durable good to the decision to take a tolled expressway.

What is Choice Modeling?

Discrete choice modeling is an analytical method used to understand and to predict this real-world decision-making behavior.

When you partner with RSG, we develop customized surveys and controlled experiments and use passively collected data (“big data”) to gather information on travel behavior, choice probability, revealed preferences, and stated preferences. With this data, our team of advanced analytics experts creates custom models to provide insight and to forecast actual choice behavior for your unique product, service, or offering.

Advanced Choice Modeling from RSG

Since our founding, we have pioneered the application of innovative and powerful methods for modeling choice behavior. We develop, refine, and apply discrete choice modeling to create robust forecasting models of people’s preferences among choice alternatives.

Our discrete choice models provide rich descriptions of how product or service features, designs, and other attributes—such as price and positioning—affect individuals’ selection behavior. This choice data is derived from specially designed surveys that include choice-based stated preference exercises, and data on actual decisions from market research.

At RSG, our choice exercise and discrete choice modeling solutions go well beyond what you can accomplish using commercially available off-the-shelf software. We provide solutions tailored to your needs. Our fully customizable methods use efficient experimental designs to reduce survey burden and consider multiple aspects, such as:

  • Past respondent choice decisions.
  • Different underlying preference distributions and model structures.
  • Attribute interactions.

We also develop and apply complementary methods to model the myriad other considerations in fully understanding behavior, including the following:

  • How do people decide what products and services to consider in a choice set and which are irrelevant alternatives?
  • What influences how much/how often people will use a product or service?
  • How does a set of alternatives influence people’s choice decisions?
  • What drives people’s satisfaction, loyalty, and repurchase or reuse?

Choose the Experts in Choice Modeling.

RSG offers the power of discrete choice models to help your organization gain valuable insights into decision behavior and drive better strategies and outcomes. View our selected solutions below to see what we offer or contact our team today for more information.

Choice-Based Conjoint Analysis Exercises:

  • Discrete choice
  • Choice-based conjoint
  • Adaptive choice-based conjoint
  • Maximum Difference Scaling (MaxDiff)

Discrete Choice Modeling Approaches:

  • Mixed logit models
  • Latent choice/latent variable (LCLV) models
  • Multiple Discrete and Continuous Extreme Value (MDCEV) models
  • Classical, simulation-based, Maximum Composite Marginal Likelihood (MACML) and hierarchical Bayesian estimation methods
  • TURF (Total Unduplicated Reach & Frequency) models
  • Likelihood (MACML) and hierarchical Bayesian estimation methods 
  • TURF (Total Unduplicated Reach & Frequency) models 

Can we stay connected?

Sign up for RSG emails to keep up with our news & insights.

Share this page