Martyn Richard Jones – Madrid 16th January 2025

Choice modelling attempts to model the decision process of an individual or segment via revealed preferences or stated preferences made in a particular context or scenario. Typically, it attempts to use discrete choices (A over B; B over A, B & C) in order to infer positions of the items (A, B and C) on some relevant latent scale (typically “utility” in economics and various related fields). 

http://en.wikipedia.org/wiki/Choice_modelling

Consider this: Choice modelling (also called discrete choice analysis) is a statistical and analytical technique used to study and predict how individuals make decisions when presented with multiple options. It assesses the trade-offs people are willing to make and identifies the factors that most influence their choices. The process consists of defining the objectives, identifying relevant attributes and levels, creating choice sets, surveying the market participants, collecting data, analysing it, and interpreting results.

One frequently cited example is about a car manufacturer who wants to design a new electric vehicle that will appeal to customers. So, they must decide which features (e.g., passive and active safety, battery range, price, charging speed, and comfort) to prioritise.

Another typical example is a hotel chain that wants to maximise its occupancy stats and revenue by better tailoring what it offers in terms of what the clients will most appreciate and would be willing to pay for.

When to use: Choice modelling is most appropriate when you want to understand, predict, or quantify decision-making processes that involve trade-offs between multiple options. It is a valuable tool for trying to understand consumer preferences, designing and pricing products, for creating different scenarios and behaviours, and for planning public policies and projects.

Strengths: Choice modelling is a powerful method for understanding decision-making processes and optimising strategies across all sectors. Its ability to quantify preferences and predict behaviours makes it an essential tool for data-driven decision-making, product development, and policy design.

Weaknesses: Choice modelling may not be appropriate in the following cases:

• When you lack sufficient data: If you do not have a representative sample or enough responses, the results will not be reliable.
• For open-ended decisions: It does not work well for decisions with unclear or undefined alternatives.
• When factors are not quantifiable: If the attributes of the options cannot be measured or compared, other methods, such as focus groups or qualitative research, may be better.
• For more straightforward decisions: If the decision is simple and does not involve significant trade-offs, choice modelling may be overkill.

Passing comments: “May your choices reflect your hopes, not your fears.” – Nelson Mandela