Skip to main content
Social Sci LibreTexts

10.2: Approches to Audience Analysis

  • Page ID
  • \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    \( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

    ( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\id}{\mathrm{id}}\)

    \( \newcommand{\Span}{\mathrm{span}}\)

    \( \newcommand{\kernel}{\mathrm{null}\,}\)

    \( \newcommand{\range}{\mathrm{range}\,}\)

    \( \newcommand{\RealPart}{\mathrm{Re}}\)

    \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

    \( \newcommand{\Argument}{\mathrm{Arg}}\)

    \( \newcommand{\norm}[1]{\| #1 \|}\)

    \( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

    \( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

    \( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

    \( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

    \( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vectorC}[1]{\textbf{#1}} \)

    \( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

    \( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

    \( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

    \( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

    \( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

    \(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)

    Whenever thinking about your speech, it is always a good idea to begin with a thorough awareness of your audience and the many factors comprising that particular audience. In speech communication, we simply call this “doing an audience analysis.” An audience analysis is when you consider all of the pertinent elements defining the makeup and demographic characteristics (also known as demographics) of your audience (McQuail, 1997). From the Greek prefix demo (of the people), we come to understand that there are detailed accounts of human population characteristics, such as age, gender, education, occupation, language, ethnicity, culture, background knowledge, needs and interests, and previously held attitudes, beliefs, and values. Demographics are widely used by advertising and public relations professionals to analyze specific audiences so that their products or ideas will carry influence. However, all good public speakers consider the demographic characteristics of their audience, as well. It is thefundamental stage of preparing for your speech. Table 5.1 shows some examples of demographics and how they may be used when developing your speech. Of course, this is not an all-inclusive list. But, it does help you get a good general understanding of the demographics of the audience you will be addressing.

    So now you may be saying to yourself: “Gee, that’s great! How do I go about analyzing my particular audience?” First, you need to know that there are three overarching methods (or “paradigms”) for doing an audience analysis: audience analysis by direct observation, audience analysis by inference, and audience analysis through data collection. Once you get to know how these methods work, you should be able to select which one (or even combination of these methods) is right for your circumstances.

    Nothing has such power to broaden the mind as the ability to investigate systematically and truly all that comes under thy observation in life. ~ Marcus Aurelius


    Direct Observation

    Audience analysis by direct observation, or direct experience, is, by far, the most simple of the three paradigms for “getting the feel” of a particular audience. It is a form of qualitative data gathering. We perceive it through one or more of our five natural senses—hearing, seeing, touching, tasting, and smelling. Knowledge that we acquire through personal experience has more impact on us than does knowledge that we learn indirectly. Knowledge acquired from personal experience is also more likely to affect our thinking and will be retained for a longer period of time. We are more likely to trust what we hear, see, feel, taste, and smell rather than what we learn from secondary sources of information (Pressat, 1972).

    All you really need to do for this method of observation is to examine your audience. If you are lucky enough to be able to do this before speaking to your audience, you will be able to gather some basic reflective data (How old are they? What racial mix does this audience have? Does their non-verbal behavior indicate that they are excited to hear this speech?) that will help you arrange your thoughts and arguments for your speech (Nierenberg & Calero, 1994).

    One excellent way to become informed about your audience is to ask them about themselves. In its most basic form, this is data collection. Whenever possible, have conversations with them – interact with members of your audience – get to know them on a personal level (Where did you go to school? Do you have siblings/pets? What kind of car do you drive?) Through these types of conversations, you will be able to get to know and appreciate each audience member as both a human being and as an audience member. You will come to understand what interests them, convinces them, or even makes them laugh. You might arouse interest and curiosity in your topic while you also gain valuable data.


    For example, you want to deliver a persuasive speech about boycotting farm-raised fish. You could conduct a short attitudinal survey to discover what your audience thinks about the topic, if they eat farm-raised fish, and if they believe it is healthy for them. This information will help you when you construct your speech because you will know their attitudes about the subject. You would be able to avoid constructing a speech that potentially could do the opposite of what you intended.

    Another example would be that you want to deliver an informative speech about your town’s recreational activities and facilities. Your focus can be aligned with your audience if, before you begin working on your speech, you find out if your audience has senior citizens and/or high school students and/or new parents.

    Clearly this cannot be done in every speaking situation, however. Often, we are required to give an unacquainted-audience presentation. Unacquainted-audience presentations are speeches when you are completely unfamiliar with the audience and its demographics. In these cases, it is always best to try and find some time to sit down and talk with someone you trust (or even several people) who might be familiar with the given audience. These conversations can be very constructive in helping you understand the context in which you will be speaking.

    Not understanding the basic demographic characteristics of an audience, or further, that audience’s beliefs, values, or attitudes about a given topic makes your presentation goals haphazard, at best. Look around the room at the people who will be listening to your speech. What types of gender, age, ethnicity, and educationallevel characteristics are represented? What are their expectations for your presentation? This is all-important information you should know before you begin your research and drafting your outline. Who is it that I am going to be talking to?

    If we knew what it was we were doing, it would not be called research, would it? ~ Albert Einstein


    Audience analysis by inference is merely a logical extension of your observations drawn in the method above. It is a form of critical thinking known as inductive reasoning, and another form of qualitative data gathering. An inference is when you make a reasoned tentative conclusion or logical judgment on the basis of available evidence. It is best used when you can identify patterns in your evidence that indicate something is expected to happen again or should hold true based upon previous experiences. A good speaker knows how to interpret information and draw conclusions from that information. As individuals we make inferences—or reasonable assumptions—all the time. For example, when we hear someone speaking Arabic, we infer that they are from the Middle East. When we see this person carrying a copy of The Koran, we infer that they are also a follower of the Muslim faith. These are reasoned conclusions that we make based upon the evidence available to us and our general knowledge about people and their traits.

    When we reason, we make connections, distinctions, and predictions; we use what is known or familiar to us to reach a conclusion about something that is unknown or unfamiliar for it to make sense. Granted, of course, inferences are sometimes wrong. Here’s a familiar example: You reach into a jar full of jelly beans, and they turn out to be all black. You love black jelly beans. You reach back into the jar and take another hand full, which turn out to be, again, all black. Since you can’t see the jelly beans inside the jar you make an assumption based on empirical evidence (two handfuls of jelly beans) that all of the jelly beans are black. You reach into the jar a third time and take a hand full of jelly beans out, but this time they aren’t any black jelly beans, but white, pink, and yellow. Your conclusion that all of the jelly beans were black turned out to be fallacious.

    Data Sampling

    Unlike audience analysis by direct observation and analysis by inference, audience analysis by data sampling uses statistical evidence to quantify and clarify the characteristics of your audience. These characteristics are also known as variables (Tucker, et al, 1981), and are assigned a numerical value so we can systematically collect and classify them. They are reported as statistics, also known as quantitative analysis or quantitative data collection. Statistics are numerical summaries of facts, figures, and research findings. Audience analysis by data sampling requires you to survey your audience before you give your speech. You need to know the basics of doing a survey before you actually collect and interpret your data.


    If you make listening and observation your occupation, you will gain much more than you can by talk. ~ Robert Baden-Powell

    Basic Questionnaire

    There are a great number of survey methods available to the speaker. However, we will cover three primary types in this section because they are utilized the most. The first type of survey method you should know about is the basic questionnaire, which is a series of questions advanced to produce demographic and attitudinal data from your audience.

    Clearly, audience members should not be required to identify themselves by name on the basic questionnaire. Anonymous questionnaires are more likely to produce truthful information. Remember, all you are looking for is a general read of your audience; you should not be looking for specific information about any respondent concerning your questionnaire in particular. It is a bulk sampling tool, only.

    While you can easily gather basic demographic data (examples of demographic questions are shown in the chart following this section), we need to adjust our questions a bit more tightly, or ask more focused questions, in order to understand the audience’s “predispositions” to think or act in certain ways. For example, you can put an attitudinal extension on the basic questionnaire (examples of attitudinal questions are shown in Figure 5.1).

    These questions probe more deeply into the psyche of your audience members, and will help you see where they stand on certain issues. Of course, you may need to tighten these questions to get to the heart of your specific topic. But, once you do, you’ll have a wealth of data at your disposal that, ultimately, will tell you how to work with your target audience.


    Ordered Categories

    Another method of finding out your audience’s value set is to survey them according to their value hierarchy. A value hierarchy is a person’s value structure placed in relationship to a given value set (Rokeach, 1968). The way to determine a person’s value hierarchy is to use the ordered categories sampling method. Here, each audience member is given a list of values on a piece of paper, and each audience member writes these values on another piece of paper in order according to their importance to him/her. Each response is different, of course, because each audience member is different, but when analyzed by the speaker, common themes will present themselves in the overall data. Accordingly, the speaker can then identify with those common value themes. (Examples of an Ordered Value Set appear in Figure 5.1).


    Likert-Type Testing

    The final method of assessing your audience's attitudes deals with Likert-type testing. Likert-type testing is when you make a statement, and ask the respondent to gauge the depth of their sentiments toward that statement either positively or negatively, or neutrally. Typically, each scale will have 5 weighted response categories, being +2, +1, 0 -1, -2. What the Likert-type test does, that other rests do not do, is measure the extent to which attitudes are held. See how the Likert-type test does this in the example on "unsolicited email" in Figure 5.1.

    A small Likert-type test will tell you where your audience, generally speaking, stands on issues. As well, it will inform you as to the degree of the audience's beliefs on these issues. The Likert-type test should be used when attempting to assess a highlt charged or polarizing issue, because it will tell you, in rough numbers, whether or not your audience agrees or disagrees with your topic.

    No matter what kind of data sampling you choose, you need to allow time to collect the information and then analyze it. For example, if you create a survey of five questions, and you have your audience of 20 people complete the survey, you will need to deal with 100 survey forms. At high levels such as political polling, the audience members quickly click on their answers on a webpage or on a hand-held "clicker," and the specific survey software instantly collects and collates the information for the researchers. If you are in a small community group or a college class, it is more likely that you will be doing your survey "the old-fashioned way" -- so you will need some time to mark each individual response on a "master sheet" and then average or summarize the results in an effective way to use in your speech-writing and speech-giving.

    10.2: Approches to Audience Analysis is shared under a CC BY-NC-ND license and was authored, remixed, and/or curated by LibreTexts.

    • Was this article helpful?