We hear "scientific" marketing methods being bandied about everywhere by gurus, gurettos and people who fill hotel conference rooms and arenas telling three bits of nonsense about direct response and brand positioning. Of course, there is nothing scientific about those words. They are just show, theatrical performances with opinions invented out of thin air by other charismatic leaders from whom these have copied words and ideas.

But this should not lead you to think the opposite, which is that marketing is based on point of view. Not at all. In marketing, science exists, in various forms and with different approaches.

The scientific method chosen by Francesco Galvani for his company Deep Marketing and its clients is based on empirical generalizations.

In the following article we will see what empirical generalizations are, how they are created, with what tools. The pros and cons of different sources. It is a somewhat technical read, but we believe it is essential for marketers, executives and enthusiasts.


Empirical generalizations in the marketing sciences are stable relationships between two or more variables that have been observed and studied under a range of conditions. Repeated and non-random relationships. These types of generalizations are used to provide predictions about consumer and market behavior and to guide marketing decisions.

Many studies have been conducted to better understand the dynamics of marketing, and the results of these studies have been used to generate empirical generalizations. Below we will discuss the different types of evidence used to develop empirical generalizations, as well as their advantages and disadvantages. Examples of the types of evidence commonly used will also be given, and we will discuss why some marketers might reject their use. Finally, we will explore the challenges that researchers or practitioners face when developing or exploiting an empirically grounded theory in their work.


In concrete terms, what is an empirical generalization?

Empirical generalizations (EGTs) are statements that describe the relationship between two or more variables based on repeated evidence. They can be used to develop a theory and make predictions about how different variables interact with each other. They are useful in marketing science, as they can help experts understand their target audience and develop strategies that are more likely to succeed. EGTs can also be used to test the validity of existing theories and help identify new trends or insights that can be used in marketing campaigns. These generalizations are based on data from experiments, surveys and other sources and can help marketers make better decisions about their marketing initiatives.

So: empirical generalizations are stable relationships among behavior of people, brands, products, and markets obtained by analyzing sweeping data or doing empirical (field) experiments.

They allow good predictive ability. For example, we can know that by operating certain actions in an advertising campaign and with certain conditions, we might get specific responses from the audience.

How Do Empirical Generalisations Help in Marketing Science?
One of the founding texts of the discipline, from 2013, pencilled by Byron Sharp

How are empirical generalizations useful in marketing science?

Empirical generalizations are a valuable tool for the marketing sciences. By providingevidence-based relationships between variables, they enable marketers to better understand their market and make more informed decisions. These nonsubjective relationships can be used to:

  1. Develop social and social psychology theories in consumption and preferences.
  2. Predicting consumer behavior.
  3. Providing strategic advice to enterprises.

For example, empirical generalizations can be used to discover the relationship between consumer preferences and product prices. By understanding this response curve, a strategic marketing consultant can advise clients on the price of their products based on consumer preferences and the competitive market environment. This helps the company remain competitive and maximize profits.

They can be used to identify causal relationships between different variables. This can help marketers develop more effective strategies. For example, an empirical generalization might reveal that a certain type of advertising is more effective in certain contexts than others. With this knowledge, managers can adjust their campaigns according to the context in which they are placed.

By providing evidence-based relationships between variables, empirical generalizations help marketers make better, less subjective decisions.

The Role of Theory in the Generation of Empirical Generalisations.

The two empirical macro-sources

When it comes to generating evidence for empirical generalizations, a variety of data sources can be used. These usually include surveys, experiments, case studies and observational studies. In general, researchers should consider both qualitative and quantitative evidence.

Qualitative evidence (e.g., interviews, focus groups) can provide insights into consumer behavior, which can then be used to develop hypotheses about how different variables interact with each other.

On the other hand, quantitative tests (e.g., surveys, experiments) can be exploited to test hypotheses and further refine them.

By combining qualitative and quantitative evidence, managers and researchers can develop empirically grounded theory that can serve as the basis for business decisions and strategies.

The use of experiments

Among quantitative tests, experiments are a commonly used method for generating evidence to support empirical generalizations.

Experiments allow researchers to observe how changes in independent variables affect dependent variables, in a controlled environment. According to the dictates of the scientific method. This allows researchers to identify cause and effect relationships (or at least credible correlations) between two or more variables in consumer behavior.

Experiments can also be used to test the validity of existing theories and to develop new ones. Experiments can be conducted in the laboratory or in the field. In either case, it is important to ensure that the sample size is large enough to allow valid conclusions to be drawn. In addition, experiments should be designed to eliminate any potential bias or confounding factors. To ensure the validity of the results, experiments should be conducted with replications and multiple trials.

Data Sources for Generating Evidence for Empirical Generalisations.
An all-too-simple empirical generalization such as drawing the price elasticity curve can be generated from many sources (bottom left box) and have many independent variables (top left box).

Other empirical data sources

Surveys are a common way for professionals to gather information about consumer attitudes and behaviors. They rely on respondents to answer questions accurately, providing an understanding of what customers think and feel.

Observational studies are another frequently used type of research. Through this method, trained observers watch people interact in their natural environment and record their behavior. This is an undirected way of collecting information that provides researchers with information about how people behave "naturally."

Neuroscience tools can also be harnessed to study consumer behavior and the effect of different stimuli on their decision making.

Finally, we must not forget access to data in the literature, databases, graphs, tables, so-called archival data. These are existing data that can be used to evaluate theories or generate evidence. Then there are secondary data, which refer to archival data already collected and published by other researchers or organizations.

Generally, a small consulting firm will prefer to exploit archival data, secondary data, and conduct surveys. Having no funds for experiments and observational studies.

These tools help recreate real-world situations and can provide valuable information about human behavior, emotions, attitudes and experiences.

Preparing a survey is no easy matter

Pros and cons of different sources

Using experiments to generate evidence for empirical generalizations can provide solid, verifiable data on how a certain marketing concept or strategy works in a given context. As we have seen, experiments also provide an opportunity to test hypotheses and draw conclusions from the results. However, using these methods to generate evidence for empirical generalizations has some fundamental drawbacks.

First, experiments are expensive and require significant time and resources. In addition, they can only be conducted under controlled conditions, which means that the results generated may not be applicable to real-world situations. Experiments may be difficult to replicate, and thus the data generated may not be reliable.

Despite these drawbacks, experiments remain an important tool for understanding the effects of marketing strategies and tactics in a given context.

On the other hand, the use of data sources such as surveys, interviews, and focus groups can provide large amounts of data quickly and inexpensively. These can be used to identify patterns and trends in consumer behavior and inform marketing decisions. On the other hand, they may be unrepresentative in nature. Moreover, they may not provide the granularity needed to generate evidence for more complex empirical generalizations.

The reasons why some marketers reject the use of theory to develop an EGT

For some marketers, the idea of using theory to develop empirically grounded scientific theory (EGT) may seem overwhelming. Some may hesitate to take this approach because of the complexity of the concepts involved and the difficulty of understanding how they relate to each other. Other marketers may be reluctant to commit the time and effort required to use an EGT, especially if they lack the skills or resources needed for such an undertaking.

In addition, some marketers may reject the use of theory to develop an EGT because of a lack of confidence in the scientific method or skepticism about its ability to produce reliable results. Finally, they may simply not want to accept theinherent uncertainty in science and prefer instead a more traditional approach that relies on intuition and experience. Or, even worse, reporting the words of some guru.

Whatever the reason, it is important for marketers to consider the potential benefits of using theory to develop an EGT before rejecting it outright. This is unfortunately not happening, especially in Italy.

Alas, in marketing too often the theories of a guru with a lot of self-esteem are preferred over empirical generalizations

Challenges faced by marketers in developing an EGT

Developing an empirically based theory to explain or predict a particular marketing phenomenon is a complex process that is not without its challenges. Marketers must consider numerous factors when seeking to develop and exploit an EGT. These include the need to ensure that the evidence used to generate the generalization is valid and reliable, the need for sufficient data and resources to support the theory, and the need for a comprehensive understanding of the underlying concepts and theories.

They must also be aware of the potential for bias in data collection and analysis due to errors in data analysis and information cleaning, as well as the need to ensure that the models developed are applicable to different contexts and populations.

Ultimately, developing an EGT requires marketers to take a highly creative and analytical approach in order to generate a generalization that has broad applicability.