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Chapter 25 ✦ Cataloging Audiences
✦ Who are these people objectively? What are their ages, interests, jobs, and other
relevant data?
✦ What does this audience already know and believe about this subject?
✦ What are people’s needs and desires for new information in your subject area?
✦ What kind of presentation style are they likely to respond to favorably?
✦ What publications do they already trust, and to which are they likely to compare
yours?
✦ What’s the author’s relationship to this audience? Is she a peer, an expert, or an
outsider?
✦ How do you establish credibility with this audience? What do audience members
consider good information sources, arguments, and examples?
✦ What tasks and purposes do your audience members have in mind as they approach
your material?
This sort of analysis has motivated communicators from the ancient Greek rhetoricians to
the modern technical writer and journalist. I believe that it’s a pretty good list of the sorts of
information required to understand how to communicate with an audience. Most of the time,
you conduct this analysis quite informally, and it results in an intuitive feel that gives the
communicator a sense of how to approach an audience. For a CMS audience analysis, you can
make the answers to these questions explicit and relate them to the parts of the CMS that
they’re going to help structure.
Audiences and marketing
I rarely hear marketing people use the word audience, but I hear them talk about target mar-
kets all the time. A market itself is a group of people with common concerns that motivate
their behaviors —basically, it’s an audience. Within the broad market that an organization
serves are market segments that consist of subgroups with identifiable traits and targetable
needs.
Marketers are getting more and more precise in how they construct and manage segment
data and how they target individuals. Today’s merchandizing and campaign-management sys-
tems are very sophisticated in the ways that they divide people into categories (or segments)
based on the data that they can collect or acquire. These systems match profiles to the mate-
rials that each group is to receive. Profiles are sets of traits and trait values that you can
group together to define a kind of person.
Traits are another form of metadata. They consist of data about a person.
Traits such as age, sex, interests, pages viewed, job type, and time on the site, for example,
may be at your disposal and you can use them to define segments. You may create a segment
that you call Info Addicts, for example, that consists of males between the ages of 16 and 25
who spent a lot of time on your site. Based on the age and sex of a visitor (which you ask or
otherwise obtain) and the time that visitor spends on the site (which you measure), you can
determine who is and who’s not an Info Addict. Of course, the next question is, “So what?”
What do you do differently with an Info Addict than with any other visitor?
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