9789144123851

EXPERIMENTS IN MARKETING

MAGNUS SÖDERLUND

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Art. No 39818 ISBN 978-91-44-12385-1 First edition 1:1 © The author and Studentlitteratur 2018 studentlitteratur.se Studentlitteratur AB, Lund Translation: Rikard Ehnsiö Cover design: Jens Martin/Signalera Cover illustration: Magnus Söderlund Illustrations: Magnus Söderlund Printed by Lapaprint, Valmiera, Latvia 2018

Contents

Preface 7 Chapter 1 Introduction 9

Mythbusting 10 What is an experiment? 12 Experiments: objectives and benefits 14 Specific tests of causal claims 16 The limitations of experiments 18 Who benefits from conducting marketing experiments? 20 The remaining outline of the book 23

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Chapter 2 Groups of participants 25

From individual to groups 26 The “group” in experiments is rarely a group 29 The participant as an anonymous group member 30 Why did the individual disappear (I)? 31 It is important that groups do not differ from one another initially 32 Randomized allocation of participants to groups 34 Randomization – how to do it in practice 38 Why did the individual disappear (II)? 40 The individual has not completely vanished 41

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Chapter 3 Several groups in the same experiment 43

Factors and levels in experiments 44 The number of groups 47 The number of participants per group 48 Does the number of participants have to be the same in each group? 49 What are the consequences of only including one group of participants? 50 Non-experiments involving several groups 51 Is it a requirement that each participant only receives one treatment? 52 Chapter 4 The treatment (I) 55

The treatment as a translation of a cause variable 56 Similarities and differences between treatments in the same experiment 59 Deception as to what the treatment entails 63 Calibrating the treatment in relation to the participant 67 The spectacular treatment 70 Chapter 5 The treatment (II) 77

You do not need a laboratory to conduct experiments 77 Three types of experiments, three ways of handing out treatments 79 How do we know what a treatment actually covers? 86

Measurable treatment reactions 100 Types of reactions in experiments 103 Mediating variables 110 Common characteristics in reactions studied in experiments 114 Deception when measuring reactions 121

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Chapter 6 Reactions 99

Chapter 7 Additional measurement aspects 123

Screening of participants 123 Participant demographics 124 The participant’s personal characteristics as cause variables 125 Moderating variables 127 Hypothesis guessing 131 Perceived realism 133 Whatever should not be affected should not be affected 134 Reliability and validity in measurements 134 Chapter 8 Comparing groups (I) 139

The development of tests for comparing groups in experiments 140 Choosing a specific test when comparing groups 145 Choice of level of significance 155 The mechanical convention 156 Chapter 9 Comparing groups (II) 159

Criticism of hypothesis testing 160 So what needs to be done? 167 Internal and external validity 171 Skipping group comparisons? 175

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Chapter 10 Critique 179

Experiments are artificial 179 Participants in experiments are not typical 184 Experiments are rarely based on a random sample of participants 189 The sample of treatments is limited 191 Deception 195

References 201 Index 229

Contents ◆ 5

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Chapter 1 Introduction

Much of what we now know (or at least believe we know) concerning how people are influenced by different situations, events or objects originates from studies conducted in the form of experiments. The same applies to our knowledge concerning how marketing influences customers. And much of what we still do not know may be rendered accessible via future experiments.

Whether you are already conducting experiments, are considering starting to use experiments or just want to know more about this method due to its significance in terms of our knowledge of people, there are good reasons for trying to understand what an experiment actually entails and the type of choices faced by an experimenter. And the main purpose of this book is to create such an understanding.

In this chapter, we first engage in some mythbusting, as the word “experi­ ment” may lead to all kinds of associations that do not always correspond with the reality and everyday practices of present-day experimenters. This is followed by a definition of the type of experiment that forms the theme of this book, in addition to an overview of the key components of an experi­ ment. The use of experiments is a method specifically designed for testing causal claims, and this is discussed in the next step. Furthermore, there are several types of individuals who may benefit from using experiments and the chapter ends with some thoughts on this topic, in addition to an outline of the rest of the book.

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Mythbusting Some people may react negatively when hearing the word “experiment,” as many atrocities endured by people against their will have been labeled experiments. As a matter of fact, there are plenty of early studies that are

carried out by a limited number of researchers with extensive technical resources. But experiments do not require laboratories. Experiments may for instance be carried out in stores and via websites that may be accessed by the participant through his or her own computer or cellphone. Nor does the measurement equipment need to be all that advanced. In fact, many experiments are conducted by means of data being collected via survey questions.

Alternatively, the word “experiment” gives associations of natural sciences and thus something that is primarily carried out when studying dead objects, plants or animals. At least this was the case when the New York Times listed the ten most “beautiful” scientific experiments during the last two millennia: none of them involved people (Johnson 2002). Contrary to what one might think, however, experiments involving human partici­

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definitely ethically reprehensible as well as in fact criminal – and there is no point in pretending that these never existed. Far from all of these studies, however, have been experiments – in terms of the definition used in this book – even if they have been labeled as such. Nonetheless, a typical, contemporary marketing experiment does not include anything in the way it is conducted that will hurt its participants.

Another common reaction is mildly negative and curious. This reaction should be seen in light of the fact that some studies involving strange elements have been carried out in the form of experiments. But some experiments that seem bizarre – as if they were carried out by the “mad scientist” of popular culture – are not all that bizarre when put in relation to normal methodological practices used in experiments.

The word “experiment” may also result in associations of laboratories with scientists wearing white coats, soundproofed rooms, machines with flashing lights, one-way mirrors and wires connected to various spots on the test subjects – which may indicate that experiments require both a specific location and advanced equipment, and thus that experiments may only be

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pants include a number of elements that have not been developed in the natural sciences.

The word “experiment” may also result in associations of activities that each and every one of us are able to carry out by ourselves, such as when we say “I’m experimenting with eating nothing but fruit for breakfast” or “I’m experimenting with adding two days of rest between my workouts.” In this case, the word experiment may be associated with various lifestyle activities rather than aspects related to research methods. As we shall see below, a key component of an experiment is in fact that the experimenter plays an active role, in that he or she initiates a sequence of events. Nevertheless, this in itself is not sufficient for something to be labeled an experiment (at least not in this book).

A further aspect of experiments is that they by definition require participants. A matter of terminology may be noted already at this point: The language has changed over time, from subject to participant, which is why I from now on refer to the individuals used for collecting data in an experi­ ment as participants. In any event, such participants must be recruited (and sometimes even paid) and they must be subject to instructions. In particular, several errors may occur in the experimenter’s interaction with participants.

Researchers who have not carried out experiments may find all of this so complicated that they instead prefer to use some other method. But as we shall see in this book, there are plenty of experiments where the relationship between the experimenter and participants is not that different from when a researcher uses surveys, interviews or observes the way people act in a given situation.

Already here, we see that there are different ways of defining what an experiment actually entails. What an experiment really involves, especially when it includes human participants, has also changed over time (Brown 1997, Danziger 2000, Winston & Blais 1996). Using experiments is in fact one of the most misunderstood methods when it comes to studying how people react in various situations. This is not all that strange, as large portions of the existing body of knowledge concerning experiments are inaccessible to the layman, but also difficult to access even for experimenters who regularly carry out studies they refer to as experiments. Just as misunderstandings 1. Introduction ◆ 11

are understandable, they are nevertheless also unfortunate, as experiments represent an extremely powerful method that may contribute with more relevant knowledge compared to other methods.

What is an experiment?

At the core, experiments are about attempts (in Latin, experimentum means attempt) to learn something important about the workings of the world – and this is a task one may address in a variety of ways. At the same time, I have already indicated that not everything referred to as an “experiment” is in fact an experiment, at least not in light of the development of methodology that has taken place over the years. The time has thus come to clarify this by saying that this book concerns experiments as a method containing the following components: An experiment means that individuals are randomly allocated to groups, which receive different treatments, followed by a comparison of the group

This does not include any particular drama or mystery, nor is there any particular negative tension. There is nothing in this process that by definition results in participants being injured, nor does it require advanced machines or that the experimenter studies cells and atoms rather than people. This is about a basic procedure when carrying out studies, nothing more and nothing less. And let me furthermore already at this stage say something about the key components of this procedure: First, the existence of groups of participants constitutes an essential component in experiments. That is why a typical contemporary experiment does not contain any information on individual reactions, and the participating individuals are also anonymous. The existence of more than one group of participants is a particularly important component, as the process of comparing group reactions is how we learn how people are affected by various objects and situations.

Second, a random allocation of participants to groups means that the

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reactions after the treatments.

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probability of each participant ending up in either of the groups is the same for everyone. This is also a good time to introduce a technical term: the experimental procedure that is my focus in this book, which uses chance for allocating participants to groups, is referred to as a true experiment.

As we shall see below, the use of chance in this regard represented a real innovation, which meant that the ability of researchers to make reasonably clear-cut conclusions from experiments was increased.

Third, a treatment is something initiated by the experimenter himor herself within the framework of an experiment. This means that the researcher plays a more active role in experiments compared to methods based on the role of the researcher primarily being that of an observer. The treatment is generally focused on a limited number of causes for possible human reactions. The trick for achieving this is to create treatments that are as similar as possible when it comes to factors other than the ones in focus (i.e., trying to keep these other factors constant). In experimental terminology, you say that the experimenter manipulates a factor when he or she creates a treatment that exposes participants to this factor. There is a potential source of confusion in this book in that the word treatment also carries other meanings – especially when it comes to tending to patients.

In this book, however, an experimental treatment is something initiated by the experimenter him- or herself for study purposes, not a component in therapy. Nevertheless, it should be noted here that a treatment in a marketing context may take many different forms, such as exposing participants to advertising, retail environments, prices, product names and the behavior of staff in service contexts.

Fourth, group comparison is the activity that reveals if there is influence. If there is a difference between the groups in terms of the level of an effect variable, it indicates influence in causal terms. And the strength of experiments is precisely their ability to produce conclusions regarding causality. Comparing groups to get indications of causality, however, may seem a cumbersome task. So why not simply ask people if they are influenced by one particular object or event (e.g., an ad or a store environment)? Well, this is seldom a fruitful option, as people would generally find this a difficult question. And in a marketing context, many decisions made by consumers 1. Introduction ◆ 13

are more or less automatic and/or subject to low information processing.

In addition, consumers are typically motivated to perceive themselves as immune to persuasion attempts (Eisend 2015). After all, marketing has been accused of creating many evils in society – and an independent, savvy and critical mindset is seen as the ideal by many contemporary consumers.

An experimental procedure including these components results in a relatively narrow view of what is to be considered an experiment, and in light of this procedure, I would argue that not everything referred to as “experiments” in different contexts are in fact experiments. I return to the question of why experiments involving these components are particularly valuable at a later stage, as they are discussed in more detail in later chapters.

In order to create order in the blooming, buzzing confusion surrounding us, we need one thing: causal claims (i.e., claims regarding cause and effect).

These are some examples of such claims: “If I exercise, I’ll improve my stamina.” “My friend doesn’t feel good today because he went out drinking yesterday.” “Without oxygen, you die.” “Customer satisfaction leads to customer loyalty.”

In fact, we seem to be programmed to make causal claims; we are capable of making them quickly and almost automatically. This ability never takes a break, never takes a vacation, it is impossible to turn off. The benefit is that it gives us a sorting mechanism for identifying which clues in our environment we need to pay particular attention to, in addition to providing us with information in terms of what we may influence (Garcia-Retamero & Hoffrage 2006, Garcia-Retamero et al. 2007). Nevertheless, our claims regarding causa­ lity are frequently colored by different forms of bias. Many such biases are relatively well-documented, such as that our initial beliefs in one particular cause often persevere despite the fact that additional information suggests that other causes also exist; we tend to think that a major, disruptive effect must have a major cause; our prior expectations of a relationship between two variables may often lead to a perceived correlation when such a correlation in fact does not exist; we think that a cause visible in a particularly vivid

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Experiments: objectives and benefits

case is also representative of the cause in many other cases; and we are often overconfident in our own ability to control causes (“illusion of control”).

These forms of bias are precisely why we need systematic studies for testing whether causal claims correspond to an empirical reality.

So, how far may a study of causal claims take you? Is it really possible to determine that one particular variable is the cause of another? The academic system, as it manifests itself in courses, textbooks and research reports, often has the ambition to promote critical thinking. Sometimes, this ambition results in a highly critical attitude with regard to what is possible to achieve in terms of identifying causes. In some parts of this system, there is even such a widespread skepticism as to what a study is actually capable of showing in terms of causality to make you wonder if you should not just give up.

What is the problem? The problem is that satisfying all conditions for causality in one and the same study may prove difficult. And there are three basic conditions that must be met in order to say that a certain variable X is a cause of another variable Y. These are the three conditions:

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1 X must precede Y. 2 X, and no other causes, is what affects Y. 3 X must covary with Y. The message of this book, however, is not to capitulate to the arguments of the sceptics when it comes to the elusive element of causality. Compared to many other methodologies, experiments are good for studying causal claims, because they are able to address the three conditions for causality relatively well.

I have already mentioned that an experimental treatment (X) by definition precedes a reaction (Y), which means that this element enables the experimenter to satisfy the first condition. Experimenters are thus able to avoid the situation experienced by some other researchers when their cause variable follows the effect variable, while they nevertheless claim to have found signs of causality. Reversing the order of time between cause and effect may perhaps be entertaining in science fiction, but not in science.

Avoid this by all means. I have also discussed that the typical experiment 1. Introduction ◆ 15

means that you keep things constant as much as possible, except for the cause (X) you want to study. This is thus a means of addressing the second condition. With regard to the third condition, covariation, there are statistical methods designed precisely for identifying covariation between X and Y in experimental data, which is why using such methods in experiments comes naturally (more on this later).

In other words, the advantage of using experiments compared to some other methodologies is that experiments allow you to test causal claims in a rigorous manner. Or as stated by Aronson et al. (1985, p. 443): “…for subjecting theory-inspired hypotheses about causal relationships to potential confirmation or disconfirmation, the experiment is unexcelled in its ability to provide unambiguous evidence about causation…” Having knowledge of causal relationships in turn results in relatively clear guidelines if we want to intervene in our environment and change it. If, for example, we find in an experiment that X in fact constitutes an important cause of Y, and if we want to see the appearance of more Y, then we are in a position to make sure that this occurs by introducing more X.

The experimental procedure outlined above may be perceived as limited in relation to all the various activities appearing under the heading of “experi­ ments.” However, the procedure is still flexible, as it may be used for testing causal claims in several ways.

The basic application is to test whether a particular claim of the type “X leads to Y” corresponds to an empirical reality. If you, for instance, want to know what types of images in advertising – color or black and white – result in the most favorable customer reactions, then you may test this by means of experiments (Lee et al. 2014). And what makes customers perceive a product as “cool”? This may also be tested by means of experiments (Warren & Campbell 2014). Moreover, when depicting products in advertisements, it could be that the distance between a product and, for example, an image of a person plays a role for how the customer perceives this product. This has been tested by Chae et al. (2013).

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Specific tests of causal claims

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It is relatively easily to expand on this in an experiment in order to test which of several possible causes (for example X 1 or X 2) have the strongest impact on a given effect variable Y. In this case, you thus have a number of cause variables compete against each other. What, for instance, has the strongest impact in relative terms on the willingness of customers to book a room at a specific hotel in an online setting? Is it room scarcity, the ratings of previous guests or the number of reservations made by others during the last 24 hours? Park et al. (2017) studied this question using an experiment (and it turned out that the ratings of other guests was the most important factor). Some scholars argue that tests involving several cause variables represent a higher stage of knowledge compared to the relatively simple case of testing a single possible cause. In this case, it is argued, being able to show that X 1 leads to Y is a good thing, while simultaneously being able to test whether other factors (such as X 2) either contribute or do not contribute at all is even better. By extension, experiments may also be used for testing whether a given theory provides better explanations compared to another theory; in this case, you let different theories compete against one another (a theory here refers to a conceptual system including multiple causal claims).

Kaplan (1964) and Katz (1972) are among those who believe that experiments including rival explanations are particularly important for the development of knowledge. Armstrong (2003) argues along the same lines. At any rate, including several different treatments in an experiment is relatively simple, so that each treatment represents a distinct and separate possible cause.

An experiment may also test whether a particular activity (X) affects a number of different customer reactions (Y1, Y2, Y3, etc.) and whether they are affected to the same extent. This is particularly interesting if different re­­ actions represent different degrees of desirable outcomes. For example, using bright colors instead of dark colors on food packaging may signal that the contents are healthy. At the same time, however, bright colors may activate detrimental taste inferences (Mai et al. 2016). Another example comes from the field of robot journalism: Would an article about one particular event written by a robot journalist produce different reader reactions compared to when the article is written by a human journalist? An experiment where this was tested shows that the robot’s article was perceived as more credible 1. Introduction ◆ 17

– but that the human’s article was perceived to be more readable (Graefe et al. 2016).

Another application is the so-called stress test, referred to as “the boundary experiment” by Kaplan (1964). There are at least two varieties of this test. In both varieties, we once again start off with a claim of the type “X leads to Y,” but rig the experiment in such a way as to enable us to test the limits of when this claim is valid. If you for instance start with a claim that an employee who is smiling (X) when meeting customers creates customer satisfaction (Y), one might wonder whether there are situations where this claim does not apply. In the first version of the stress test, you focus on whether the level of X plays a role in terms of its impact on Y; if, for example, turning up the intensity of X to a high level still results in X affecting Y. Will an employee bursting into laughter with an open mouth have the same effect on customer satisfaction as a smiling employee? This may be tested by creating treatments representing various degrees of smiling. In the second variety of the stress test, you test what happens to the impact of X on Y when another variable (Z) is included in the experiment. For example, does the smile (X) of a stewardess (Z1) have the same impact on customer satisfaction (Y) as a smiling funeral director (Z2) and a smiling claims adjuster from an insurance company (Z3)? In other words, this concerns variables that might affect the strength of a correlation, which may be tested by creating treatments that explicitly include such variables.

The experimental procedure is narrow in a special sense of the word, despite the fact that it may be used for testing several types of causal claims: It is based on the experimenter already from the outset having an idea with regard to the specific claims to be tested. In other words, the process requires the experimenter to adopt a certain degree of theoretical focus. Leon Festinger, a key researcher when it comes to inspiring others to engage in social psychology experiments, primarily in a laboratory environment, expressed this as follows: “Before one can successfully do a laboratory experiment, one must already know quite a bit about the phenomena one is investigating”

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The limitations of experiments

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(Festinger 1953, p. 143). A researcher without any particular claims upon which to base his or her study and who wants to try – with an open mind unblemished by theory – what happens, and if anything happens at all, when he or she initiates an activity would thus be better off doing something else than an experiment.

An experiment is also narrow in an additional sense: It is not possible to test an infinite number of causal claims within the framework of the same experiment. This is due to restrictions inherent in the process itself, especially due to the fact that what is compared are group reactions. As shown later in this book, this means that each additional cause variable not only requires its own treatment variant, but also a separate group of participants, which is resource-intensive in itself. Trying to interpret the results when the number of cause variables (and groups) is large could also turn out to be an arduous task for the experimenter. A researcher who wants to manage a larger number of cause variables at the same time, within the framework of the same study, would thus be better off not conducting experiments. If this researcher still wishes to use experiments, it is also better if the study includes a series of experiments rather than a single experiment. Indeed, the trend in marketing experiments is that experimenters increasingly carry out a whole series of experiments to be reported within the framework of the same study (see, for instance, Chae et al. 2013, Lee et al. 2014 and Warren & Campbell 2014).

Another limitation when it comes to using experiments is that there are many potential pitfalls for the experimenter. As expressed by Sigall et al.

(1970, p. 4): “In building an experiment, there are literally scores of problems that an experimenter must solve and potential pitfalls that he must avoid.”

However, examining the work of others and how they have addressed various forms of problems may help you avoid such pitfalls – and this is precisely the kind of guidance this book wants to offer.

1. Introduction ◆ 19

Who benefits from conducting marketing experiments?

The group engaging in marketing experiments the most consists of academic researchers, whose main task is to develop theories concerning consumer behavior. If these researchers believe that a theory needs to be subjected to

launch. Another situation where pretesting is frequently beneficial is when you want to evaluate a number of various designs of a particular ad. Here, you test which variety produces the best effect by conducting an experiment with a limited number of customers in order to get guidance before picking the design of the real campaign. Companies also frequently find it fruitful to carry out experiments for evaluating already ongoing activities in order to get information regarding possible improvements. A side effect of this is that experiments – which require some conceptual discipline in the form of theoretical grounding of what to test – may contribute to a managerial mindset leading to a systematic approach to learning (Anderson & Simester 2011), which is undoubtedly beneficial in a context where the complexity in the market environment is rapidly increasing (Day 2011).

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empirical tests, then they find it natural to use experiments. As a matter of fact, causal claims are found at the core of most theories. At any rate, several analysts have noticed that the proportion of experiment-based marketing studies in academic research is increasing over time in relation to studies carried out using other methods (Koschate-Fisher & Schandelmeier 2014).

And in some specific marketing fields, such as advertising, performing experiments appears to be the dominant method (Kim et al. 2014).

Marketers in the field also find experiments useful. It has been shown in many cases that companies may improve their profits by conducting relatively simple marketing experiments (Anderson & Simester 2011, Thomke & Manzi 2014). And firms such as Amazon, Google and Microsoft, with a vast number of visits on their websites, engage in extensive experimentation with visitors as participants (Kohavi et al. 2009, Regalado 2014). One application used by practitioners is pretests, where you evaluate the effects of a particular activity before it is launched in full scale. For example, some movie produ­ cers create different versions of the same movie, with different endings, and use experiments involving test audiences to determine which version to

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Furthermore, marketing experiments may also be conducted in training and educational contexts. As a complement to a teacher or instructor saying that X affects Y, the students may learn this for themselves by experiencing this effect in an experiment. In other words, one may look upon an experi­ ment and its outcomes as material for case method teaching. This method is popular in business administration contexts, where the case typically involves a description of a firm or a decision maker in a specific situation. Yet an experiment in which the students are participants may represent a more personally relevant case compared to, for instance, a case from Harvard Business School – as the participants themselves are the case. Kaplan (1964) refers to this as “illustrative experiments.” I have used experiments in this way on many occasions when teaching marketing, and I can simply say this: It is much easier to get people to realize that there is a causal relationship between X and Y if you can show them that this relationship is valid as far as they themselves are concerned – in an experiment where they are the participants. Another aspect of experiments in educational contexts is that students may benefit from conducting their own experiments – especially when it comes to writing a thesis. In my work as a professor and supervisor in marketing, I see a clear increase in the number of theses containing experi­ ments. So there is no reason to stay away from this method because you are “just” a student. As a student, you can definitely conduct experiments in the same manner as you can conduct interviews or collect data by means of surveys. Over time, a large number of students having completed their theses have shown that this is true.

An experimental approach may also be used for evaluating social effects of marketing, which then concerns stakeholders in the world of politics and administration. In areas other than marketing, things like the effects of seatbelt laws, using fluoride, speed limits and car tolls have been analyzed by means of experiments. This is about trying to create important insights when building a better society for all citizens (Campbell 1969). Such experi­ ments, sometimes referred to as social experiments (Burtless 1995), have in practice primarily come to include various social programs aimed at improving the lives of groups already from the outset experiencing a less favorable starting position in terms of, for example, unemployment or low 1. Introduction ◆ 21

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income (Greenberg et al. 1999). Marketers are sometimes blamed for the fact that the aggregated mass of some of their activities (e.g., ads where young, scantily clad women with extremely slim bodies are seen over and over again in advertising from a large number of companies) may lead to adverse social effects (e.g., eating disorders and impaired self-confidence among those repeatedly exposed to this type of advertising). This could be tested by means of experiments, but so far only a few experimenters have attempted to do so. Nor have marketers been particularly good at highlighting possible positive social effects of marketing, perhaps a result of the fact that only a few studies have addressed this question. Who knows, perhaps it is good for citizens to be exposed to a lot of advertising – perhaps it results in them becoming better at interpreting symbols and dealing with information in general? And perhaps it benefits customers to be exposed to employees in stores and service companies who are friendly – perhaps these customers then become friendlier in their own social relations (Söderlund 2015)? This too could be tested by means of experiments.

Researchers, practitioners, teachers, students and people who want to improve society – all of these groups may thus benefit from conducting experiments. In this book, I do not focus on any of these groups specifically.

Instead, I think of the target group of this book as consisting of curious individuals with an inquiring mind. Nevertheless, the fact that I happen to be a researcher could result in the book being somewhat tilted toward researchers as a target group. As discussed above, the idea is also that those who want to know more about how marketing activities affect customers are a major target group of this book. However, it should be noted that experiments are used in several disciplines and that social psychologists have been particularly diligent when it comes to developing and using the experimental method. This means that examples, quotes from scholars and so on originating from several areas other than marketing will be given plenty of space in the book.

The remaining outline of the book In this book, an experiment is thus seen as a procedure where individuals are randomly allocated to groups, which receive different treatments, followed by a comparison of the group reactions after the treatments. However, each component in this process means that there is a great deal to consider, both for those wanting to carry out their own experiments and for those wanting to get a background for evaluating conclusions made in experiments carried out by others. And this book wants to be your guide in these respects.

I begin by examining the component that is based on the notion that what you compare when conducting experiments are groups – to which participants have been allocated randomly (Chapter 2). After that, I emphasize the importance of groups in the plural, as an experiment requires at least two groups of participants (Chapter 3). I then discuss a number of aspects of the treatment – the cause variable in an experiment – which the groups are exposed to (Chapters 4 and 5). Such an experimental treatment is followed by a reaction – the effect variable in an experiment. These reactions, and how they may be measured, are the focus in the next step (Chapter 6).

However, the experimenter may also need to measure other things within the framework of an experiment, which is why such things are also discussed (Chapter 7). Following this, I introduce the component related to comparing group reactions. As we shall see, such a comparison is the key for testing

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causal claims when carrying out experiments (Chapters 8 and 9). Since there are some people with negative views on the experimental method, the book ends with a discussion on a number of aspects related to such criticism (Chapter 10).

1. Introduction ◆ 23

Magnus Söderlund is Professor of Marketing and head of Center for Consumer Marketing at Stockholm School of Economics in Sweden.

EXPERIMENTS IN MARKETING This is a book targeting actors in the field of marketing – academic researchers, practitioners, market analysts, teachers and students – with an interest in how customers are influenced by marketing.

The main argument is that the experiment constitutes a powerful method for revealing the influence of various marketing activities.

Basically, an experiment means that individuals are randomly allocated to groups, which receive different treatments, followed by a comparison of the groups’ reactions after the treatments.

To conduct experiments along these lines, however, involves several choices and challenges, and the ambition with this book is to offer guidelines to experimenters who wish to come to terms with influence-related issues in a marketing context.

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