Preference modelling allows measuring the relative importance of the key factors influencing the purchasing decision. Preference simulation makes it possible to predict the effect that changes in one or more of theses factors would have on demand .


Level I Objetives

	Get to know the main purchasing and consumption habits, identify spontaneous and suggested purchasing motives and evaluate the degree to which consumers are aware of the offers existing in the market.
	Identify the different aspects that configure the marketing mix of the main brands of reference in the market and evaluate consumer identification with each of them.
	Quantify the preferences for specific product attributes or marketing communications.

Level II Objetives

	Estimate the relative importance of the key factors influencing the purchasing decision. Understand the relations of preference and substitution between brands.
	Estimate the utility associated with each level of the key factors, as well as their main and crossed effects. Get to know the sensibility of the market to variations in each of them and identify the most attractive configurations.
	Segment the market on basis of the relevant key factors, estimate the size of each segment. Identify their respective most and least attractive attributes and levels. Understand their specific needs and describe the profile that differentiates them.
	Program a market simulator to predict the impact of new product launches or communications on relative preference and demand. Use optimisers to maximise churns and revenues at the same time.

 

Employed Techniques

In young markets a previous exploratory phase based on qualitative techniques is recommended, to identify all the relevant key factors influencing the purchasing decision. Statistically speaking preference analysis requires the frequent direct o hierarchical use of Conjoint Analysis, Maximum Difference Scaling, Hierarchical Bayes Estimation and Turf Analysis.