RUM and RRM? No, we’re not talking about a pirate’s favorite refreshment and its weird cousin. Random Utility Model and Random Regret Model are two of the models we use in conjoint analysis to predict choice.
RRM is based on the assumption that instead of choosing the option with the best combination of product characteristics (as is the case with RUM), consumers make choices in order to avoid the potential regret of missing out on something. They will take the context of a choice into account: choosing an option that minimizes regret on a few pertinent options, rather than maximizing utility. For some consumers, RRM may better represent how choices are made.
In their Quirk’s article, Jeroen Hardon and Kees van der Wagt, two research heavyweights at SKIM, share how we can incorporate behavioral economics in our models, in order to improve predictions. Using two case studies, one on health insurance and another on tablets, they show how context matters and we are not always rational in our choices. That said, RRM also has its own limitations. So, perhaps a hybrid solution is the answer. Read on to get the answers.
Using RUM and RRM to improve the predictive validity of conjoint results
“In conjoint analysis we can use different models to predict choice. The one most often used is a random utility model (RUM), in which consumers select the product that offers them the best combination of product characteristics. But RUM has some shortcomings. This article explores the integration of RUM with another model, random regret modelling (RRM), to create a more robust and accurate model. RRM assumes that, rather than going for the optimal product, consumers aim to minimize the potential regret of missing out on certain product characteristics.
Both approaches have their pros and cons and both are more or less suitable in different market situations. In an effort to improve the predictive validity of conjoint results, SKIM researchers compared and combined RUM and RRM by means of two case studies and posed the question: Would the hybrid solution lead to more realistic results?”