The purpose of quantitative models is to summarize marketing research data such that useful conclusions can be drawn. Typically the conclusions concern the impact of explanatory variables on a relevant marketing variable, where we focus only on revealed preference data. To be more precise, the variable to be explained in these models usually is what we call a marketing performance measure, such as sales, market shares or brand choice. The set of explanatory variables often contains marketing-mix variables and household- specific characteristics.

This chapter starts by outlining why it can be useful to consider quantitative models in the first place. Next, we review a variety of performance measures, thereby illustrating that these measures appear in various formats. The focus on these formats is particularly relevant because the marketing measures appear on the left-hand side of a regression model. Were they to be found on the right-hand side, often no or only minor modifications would be needed. Hence there is also a need for different models. The data which will be used in subsequent chapters are presented in tables and graphs, thereby highlighting their most salient features. Finally, we indicate that we limit our focus in at least two directions, the first concerning other types of data, the other concerning the models themselves.

Quantitative models

The first and obvious question we need to address is whether one needs quantitative models in the first place. Indeed, as is apparent from the table of contents and also from a casual glance at the mathematical formulas in subsequent chapters, the analysis of marketing data using a quantitative model is not necessarily a very straightforward exercise.