Ratings Analysis

Audience Measurement and Analytics

James Webster, Patricia Phalen, Lawrence Lichty, Patricia F. Phalen, Lawrence W. Lichty

• Publisher: Routledge
• Year: 2013
• Print ISBN: 9780415526524
• eBook ISBN: 9781136282126
• Topic: Social Sciences
• Subtopic: Marketing Research
• Index: Social Sciences

This 4th edition of Ratings Analysis describes and explains the current audience information system that supports economic exchange in both traditional and evolving electronic media markets. Responding to the major changes in electronic media distribution and audience research in recent years, Ratings Analysis provides a thoroughly updated presentation of the ratings industry and analysis processes. It serves as a practical guide for conducting audience research, offering readers the tools for becoming informed and discriminating consumers of audience information.

This updated edition covers:

• International markets, reflecting the growth in audience research businesses with the expansion of advertising into new markets such as China.
• Emerging technologies, reflecting the ever increasing ways to deliver advertising electronically and through new channels (social media, Hulu)
• Illustrates applications of audience research in advertising, programming, financial analysis, and social policy;
• Describes audience research data and summarizes the history of audience measurement, the research methods most often used, and the kinds of ratings research products currently available; and
• Discusses the analysis of audience data by offering a framework within which to understand mass media audiences and by focusing specifically to the analysis of ratings data.

Appropriate for all readers needing an in-depth understanding of audience research, including those working in advertising, electronic media, and related industries, Ratings Analysis also has much to offer academics and policy makers as well as students of mass media.

CHAPTER 1

An Introduction to Audience Research

Audiences are the source of the media’s wealth and power. They pay directly for goods and services. And even when audiences choose “free” media, their attention is sold to advertisers for billions of dollars, euros, and yen. Beyond establishing the value of media products, audiences confer social significance on the media through their choices. The programs and websites that succeed in attracting large numbers of followers help set public agendas and shape the cultures in which we live.

However, audiences are elusive. They are dispersed over vast geographical areas, sometimes on a global scale. They are tucked away in homes and businesses, where they move fluidly from one “platform” to the next. For media providers to make sense of their audiences, let alone profit from them, they must be able to see them.

It is audience research that makes them visible. Without it, institutions cannot hope to manage public attention for good or ill. And without an understanding of audience research, media professionals are ill equipped to do their jobs. This research, especially ratings research, is the central focus of this book. In the following pages, we explore audience measurement systems across various countries and what we can learn from these data.

TYPES OF AUDIENCE RESEARCH

To put audience measurement in context, we begin by considering several broad categories of research. These categories are not unique to the study of audiences, nor will we deal with all of them in subsequent chapters. We review them here to provide an overview of research practices, to help readers identify the various motivations and methods of researchers, and to build a vocabulary for talking about the field.

Applied Versus Theoretical

Applied research, sometimes called action research, provides practical information that can guide decision making by describing some phenomenon of interest or by illuminating the consequences of a particular course of action. Applied research is typically concerned with an immediate problem or need, and rarely is there any pretense of offering generalizable explanations about how the world works. Nevertheless, this research can produce useful insights and sometimes forms the basis for more enduring theories about audience behavior.

In media industries, applied research dominates audience analysis. Examples from television include surveys that measure which advertisements are well remembered, which celebrities are well liked, and whether the social media “buzz” about a program suggests high levels of viewer engagement. These insights can affect production and programming decisions. Examples from the Internet include web-based experiments that test the effectiveness of various appeals or offers in getting visitors to click through to a purchase. That can affect the sales of books or DVDs. Of course, both television and the websites depend on ratings data to describe the size, composition, and behaviors of their audiences. These become the metrics used to place and evaluate advertising and, as such, are the essence of applied research.

A special type of applied research, sometimes treated as a separate category, is methodological research. This is, basically, research on research. As we explain in the chapters that follow, many audience research companies, like Nielsen or Arbitron, rose to prominence by developing new research methods. They are, after all, in the business of selling research products. Like any self-interested company, they engage in product testing and development to provide their clients with the data they need in a fast-changing media environment. Methodological audience research might include questions like, “How can we measure television viewing more accurately?” or “How should we recruit people into our panels?” or “How can we track users across media platforms?” Many of the answers to these methodological questions are discussed in our chapters on audience data.

Theoretical research tests more generalized explanations about how the world operates. If those explanations, or theories, are broad and well supported by evidence, they can be useful in many different settings. As Kurt Lewin, a well-known communication researcher, said, “Nothing is as practical as a good theory” (Rogers, 1994, p. 321). Although theoretical research is sometimes conducted in industry, it is more common in academic settings. Examples include experiments designed to identify the effects of watching violence on television or the factors that determine which songs people download from websites. These studies typically go beyond the specific problems of individual organizations.

Neither applied nor theoretical research is reliably defined by the type of method used by the investigator. Surveys, experiments, in-depth interviews, content analyses, and other methods can all serve applied or theoretical purposes. To make matters even more complicated, a specific research project could conceivably serve either purpose depending on who is reading the study and the lessons they learn. This flexibility is probably a good thing, but it does mean that the boundary between applied and theoretical research is sometimes difficult to determine.

Quantitative Versus Qualitative

Industry researchers and academics alike often make a distinction between quantitative and qualitative research. A good deal of ambiguity surrounds the use of these terms. Strictly speaking, quantitative research reduces the object of study to numbers. This allows researchers to analyze large groups of people and to use statistics to manage the data. Qualitative research produces non-numeric summaries such as field notes or comments transcribed from an interview. While qualitative methods allow an investigator to dig deeply into a given topic, it is often hard to generalize the findings to larger populations. Ideally, the two approaches are used in tandem. Qualitative studies provide rich details and unexpected insights, and quantitative studies provide generalizability.

Unlike the differences between theoretical and applied research, qualitative and quantitative categories tend to be associated with particular research methods. Quantitative studies rely heavily on surveys, experiments, and content analyses. These methods identify variables of interest and assign numbers to people, or other units of analysis, based on those attributes. For example, a survey researcher might record people’s ages and keep track of their gender by assigning a “1” to males and a “2” to females. An experimenter might quantify physiological responses like heart rates or eye movements to identify response patterns. Similarly, someone studying political communication might record the number of times each politician is quoted in news reports during a presidential campaign, to identify reporting biases.

Qualitative methods such as group interviews or participant observation usually produce non-numeric results like transcripts or field notes. However, to make sense of these records, a bit of quantification can enter the picture. Investigators sometimes categorize and count (i.e., quantify) their observations. For example, an investigator might want to track the prevalence of ideas or phrases. Thus, the richness of open-ended comments and idiosyncratic behaviors are reduced and summarized in a way that looks like quantitative research.

The distinction between qualitative and quantitative becomes even murkier as the terms are used in industry. Many media professionals equate the term “quantitative research” with “audience ratings.” As we will see in the chapters that follow, ratings act as a kind of “currency” that drives media industry revenues. Any research that does not provide the hard numbers used to value audiences is rather casually referred to as qualitative research, which includes studies that address less routine audience characteristics such as lifestyles, values, opinions, and product preferences. While these data usually do not replace ratings as the currency used to buy and sell media, they are technically “quantitative” because they reduce the characteristics of interest to statistical summaries.

That said, there are many examples of true qualitative work in industry. Focus groups, which involve gathering a small group of people to talk about some topic of interest, are widely used. Krueger and Casey (2000) define this type of study as “a carefully planned discussion designed to obtain perceptions on a defined area of interest in a permissive, non-threatening environment” (p. 5). Focus groups are a popular way to assess radio station formats, news personalities, and program concepts. For example, Warner Bros. routinely tests television pilots using this technique. A skilled moderator probes to determine how prospective audience members react to the various program elements—what works and what does not. These insights can be used to inform decisions about character development, plot lines, and programming.

In the past three decades, another family of qualitative approaches, broadly termed audience ethnography, has gained in popularity. Some ethnographies are very much like focus groups. Some involve nonstructured, one-on-one interviews with media users. Others involve studying what people, like fans, are saying on social media sites. Still other ethnographies introduce observers into places of interest like households or fan conventions. In 2008, the Council for Research Excellence (CRE) funded a study that had trained observers to follow people throughout an entire day to better understand how they used different media platforms like televisions, computers, and mobile devices. At the extreme end of the spectrum, ethnographers might immerse themselves in the site of study for months or even years. The best ethnographies can produce a depth of understanding that is hard to match with quantitative methods.

Micro Versus Macro

Audience research can operate at different “levels of analysis.” Social scientists often draw a distinction between micro- or macro-level research. Micro-level studies, like ethnographies, look at audiences from the inside out, by adopting the perspective of an individual audience member. Macro-level studies look at audiences from the outside in, to understand how they behave as large, complex systems. Like the other distinctions we have reviewed, telling the difference can be tricky because macro-level systems, like markets or social networks, are assembled by aggregating individual media users. Knowing when you have moved from one level to the next is not always obvious. Still, it is an important distinction to keep in mind.

Micro-level studies focus on individuals—their traits, predispositions, and media-related behaviors. They frame research questions on an intuitively appealing, human scale. It is natural for us to think about audiences in this way, because we all have experience as media users and, through introspection, can imagine what might explain someone else’s actions. Micro-level research often operates on the assumption that if we could only figure out what makes individual media users tick, then we will understand audience behavior. After all, audiences are just collections of people.

Focusing on individuals, though, causes researchers to turn a blind eye to factors that are not person specific. We have known for a long time that program-scheduling practices can affect program choices, sometimes overriding individual program preferences. And now, with the growth of social media, we see patterns of media consumption, like “herding,” that are not easily explained by individual traits. As Duncan Watts, a noted sociologist and researcher at Microsoft, observed, “You could know everything about individuals in a given population—their likes, dislikes, experiences, attitudes, beliefs, hopes, and dreams—and still not be able to predict much about their collective behavior” (2011, p. 79).

But most audience research, especially ratings research, is about collective behavior. Audience analysts usually want to make statements about what large numbers of people have done or will do. They generally do not care if Bob Smith in Cleveland sees a newscast, but they do care how many men aged 35 to 64 are watching. This interest in mass behavior, which is typical of macro-level research, turns out to be a blessing. Trying to explain or predict how any one person behaves, on a moment-to-moment, day-to-day basis, can be an exercise in frustration. But when you aggregate individual activities, the behavior of the mass is often quite predictable—and the business of selling audiences to advertisers is built on predictions.

This science of studying large populations has been called statistical thinking. It was developed in eighteenth-century Europe by, among others, insurance underwriters. Consider, for example, the problem of life insurance. Predicting when any one person will die is almost impossible, but if you aggregate large numbers, you can estimate how many people are likely to expire in the coming year. You need not predict the outcome of each individual case to predict an outcome across the entire population. In the same sense, we do not need to know what Bob Smith will do on a given evening to predict how many men his age will be watching television.

When we focus on macro-level phenomena, media use becomes much more tractable. We can identify stable patterns of audience size and flow. We can develop mathematical equations, or models, that allow us to predict media use. Some have even gone so far as to posit “laws” of audience behavior. These laws, of course, do not bind each person to a code of conduct. Rather, they are statements that mass behavior is so predictable that it exhibits law-like tendencies. This kind of reasoning is typical of most commercial audience research, and it underlies many of the analytical techniques we discuss in later chapters.

There is no one right way to study audiences. Like qualitative and quantitative methods, each level of analysis has its virtues and limitations. And, as was true with our discussion of methods, using both micro- and macro-level approaches generally leads to a deeper understanding of media use.

Syndicated Versus Custom

The final distinction we will draw is between syndicated and custom research. Syndicated research offers a standardized product that is sold to multiple subscribers. Audience ratings reports, for instance, serve many users in a given market. Custom research is tailored to meet the needs of a single sponsor.

Syndicated research is common anywhere audiences are sold to advertisers, which, these days, is just about everywhere. Table 1.1 lists major suppliers of syndicated audience research around the world, as well as the ki...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Dedication
• Table of Contents
• Preface
• Ratings Analysis: Audience Measurement and Analytics
• 1 An Introduction to Audience Research
• PART I: AUDIENCE MEASUREMENT
• PART II: AUDIENCE ANALYTICS
• PART III: APPLICATIONS
• Appendix A: DMA Market Rankings
• Glossary
• Bibliography and Additional Sources
• Author Index
• Subject Index