Audience Tutorial

Sections:

Knowledge
- Of organizational context
- Of data, in general and in context
Demographics
Attitudes
- Positive, Negative, Mixed, Indifferent
- Polarized

Reaching Audiences

Reaching audiences requires knowing enough about them to know what will be interesting, motivating, or persuasive to them. While there are more dimensions of audiences than any guide could comprehensively address, this guide provides a framework for learning about audiences in three dimensions: knowledge, demographics, and attitudes.

Knowledge

Knowledge of organizational context

What are libraries as organizations? At the most basic level, libraries are community resources that exist to benefit everyone. They are especially important for those that can’t afford to buy access to books and other media. Knowing the context involves understanding the library itself as an institution, its everyday practices, and the reasons behind them.

This includes understanding some of the professional ethics that inform choices like what libraries do and don’t routinely collect as data. For example, libraries regularly collect numbers representing circulation, registered library cardholders, people and households in communities, foot traffic, public computer use, website access, database access, and much more. However, because of ethical commitments to the privacy of reading, library systems do not perpetually store the names of people who borrowed specific books or accessed certain resources. Or, if the systems in place do store this information, professional library training includes an understanding of patron privacy so that this information is not shared beyond a need for tracking loans.

Knowledge is always as multidimensional and complex as the learning experiences of each person, and so finding out what board members know about libraries as organizations can be challenging. It may be possible to gauge knowledge through asking them to respond to a simple question about experiences. Requesting a show of hands or a quick refresher for yourself about what they know can provide a wealth of information. Consider questions like:

Would you please raise your hand if you remember participating in the last library survey of the community, as a community member?
Who here remembers seeing the last presentation about circulation data?
How many of you have been part of the development of a library budget presentation before?
Can you remind me who here was on the board the last time we started a strategic planning process?

Frame these questions positively, seeking those with likely positive answers. Try to identify those who have knowledge rather than accidentally exposing and embarrassing those who do not. Similar questions can be asked about topics like experience with surveys, budgets, or strategic planning in related work for other organizations, whether in professional roles or on other boards.

Knowledge also varies among people within the library. In very small libraries, one or just a handful of people play all the roles. In very large library systems, there may be multiple data experts and multiple directors that have responsibilities for communicating the purposes of libraries at the levels of systems, branches, programs, and more. Data can be lost in translation between these groups inside libraries. For example, communications and public relations staff may not speak the same language as frontlines library staff who deal with the public. Sometimes the staff responsible for making periodic reports to governing agencies, from budget requests to state-level library data reporting, can be very isolated if library staff and governing boards do not have solid knowledge of library data. Consider training in data literacy for all staff.

Knowledge of data, in general and in context

From the start, the assumption must be that most decision-making audiences need presentations of and about library data that let them learn quickly and easily. Some decision-makers will have knowledge of data from another context, whether professional or non-profit, and that knowledge can translate reasonably well into the library context. Especially when comparing relatively simple data describing countable things—numbers of programs, loans, or cardholders from year to year—practices from other professional contexts can provide similar points of view.

Consider how a few common types of library data might be similar or even analogous to data in other settings.

Example types of library data

Automated systems report, standard or developed for specific uses
Library cards/registered borrowers

Program attendance
Public computer use
Visitors
Service hours
Website visits

Automated systems reports exist in most business and nonprofit settings, and many audiences will be familiar with quarterly reports that assess progress toward yearly goals, rates of budget expenditure, and other metrics. Counting interactions with people (library cards/registered borrowers, program attendance, public computer use, visitors) will be familiar to any public-facing nonprofit or business that serves large numbers of clients. Data about service hours and website visits help to determine when and how to provide ways of connecting. Service hours relate to usage patterns that will be especially familiar to local or regional businesses with brick-and-mortar locations. Website visits will be familiar to any organization with a significant online presence that allows users to access resources online.

If some audience members’ knowledge is lacking, then:

Show as much as possible. Translate data into visuals, and reinforce the most important meanings with images, words, and other annotations.
Caption the images. Doing so is best accessibility practice, but it also forces a final translation back from
Share data visualizations with practice audiences and ask what they think the main takeaways are. Improve visualizations based on this feedback.

Libraries need to differentiate their work from that of businesses. Libraries exist in dialogue with their communities, who are not just consumers of their resources but share a public stewardship of the library that is distinct from a customer’s relationship to a business. Business practices based on statistical inference, including attempted forecasting, can be more controversial for library contexts because libraries must respond to unpredictable demographic shifts. Businesses may presume that market analysis will identify their target audience with relative stability, however, library target audiences are geographically bound and may grow, shrink, or shift demographically over time. Generally, business perspectives that view people as potential markets versus library perspectives that view people as clients to be served.

Demographics

Example Types of Demographic Data

Overall numbers of constituents, whether patrons or students
Languages spoken in households
Household income levels
Neighborhoods or other geographic factors, such as colleges within a university
Ages or age ranges
Cultural groups with which people actively affiliate, whether ethnic, religious, social, or something else
Work roles and positions, including sectors and industries where people are employed

Some demographic data is vital to understanding audiences. For example, libraries have a responsibility to make information accessible in the languages that people speak. Understanding the intersection of the number of people who speak a language other than English in a community will provide a direct way to assess collections. If languages in the collection are not proportional to those who speak them in the community, then there’s a major opportunity for improvement. Further investigating how languages spoken may intersect with people lower household income levels adds a rationale for bolstering language-accessible collections, and advertising them.

However, data ethics are important when considering how to represent people. Categories like household income may imply poverty, which carries stigma. Arguments based on household income should be made carefully and with an emphasis on humanizing people and without exploiting suffering in story to justify library ends. There is rarely a need to tell another person’s story of suffering, and it is always best practice for library workers to frame their arguments in terms of likely needs rather than individual stories of impoverishment. The data doesn’t always require a human story if that story would negatively characterize a person or group. Retelling stories of suffering tends to dehumanize, turning people into statistics or mere representatives of disaster rather than whole human beings. Stories of negative or difficult experiences should be anonymized unless shared by a person themselves, in their own voice or language, with enthusiastic consent.

Demographic data that might be especially useful to libraries may come from many sources, inside the library’s own records, from the community, and from comparisons or contrasts with other libraries.

Attitudes

Positive, negative, mixed, and indifferent audiences have attitudes based on what they understand to be different interpretations of the same reality, situation, or data. They agree, for instance, that refreshing a computer lab would help the library provide better service, but they disagree about whether that expense is worth it. A positive audience agrees that the expense is worth it. A negative audience believes that the expense is not worth it, or that there are other things that are higher priority than, for example, refreshing a computer lab. A mixed audience has some of both.

Polarized audience, on the other hand, requires a different strategy to work with.

Click to select an attitude below.

Positive

Positive audiences want to understand the data so that, like all stakeholders, they can explain to other people why they voted the way they did. They are the audience that is poised to retell the story, so it’s important to give them a good story to tell.

Goals for positive audiences:

Build trust
Don’t take audience sympathy for granted
Make the story easy to retell

Negative

Negative audiences may be possible to persuade. The goal is to find a point of agreement and build a proposal together, hoping to persuade or to locate some common ground in order to reach agreement. Even if they are not persuaded, it may still be possible to compromise and make some progress by preparing differently for next time.

Goals for Negative Audiences:

Focus on persuasion.
Restate concerns in positive terms to build common ground.
Find compromises, even if disagreements continue.
Slow down to de-escalate conflict.

Polarized

Polarized audiences consist of people who hold wildly divergent points of view about, for instance, what a public institution is for, or whether there should be public institutions at all. These are people who may be spreading false information, and it is absolutely crucial to not repeat or amplify false information in the process of trying to address it. Repetition reinforces, and other audience members are more likely to remember something that is repeated, even if it is false, than they are to remember a more nuanced truth or compromise.

Goals for Polarized Audiences:

Stay on message.
Do not repeat false information.
Consider the value of debate strategically.
If the intent is to confuse the voters, avoid debate

Indifferent

Indifferent audiences need data--patterns that represent experiences, ideas, or people--that they might be persuaded to care about. Start with why it matters for these specific people, and be specific about how this library or this data already affect their lives. Remain engaged and model the interest that you hope to instill. Ask questions and wait through uncomfortable silence for responses.

Goals for Indifferent Audiences

Start with why the data story matters for these specific people.
Model the interest that you hope to instill in your audience.
Keep your message simple.
Ask questions of the audience and wait for responses.

Mixed

Mixed audiences are very common, consisting of some people with positive attitudes about the story and some with negative. Tell a coherent data story designed to persuade, crafting that story with the input of rehearsal audiences. If negative audience members are persuaded, they will need a great story to retell about why they changed their minds. If the story is compelling, then, occasionally, positive audience members may work to persuade negative audience members.

Goals for Mixed Audiences:

Sustain positive attitudes with a focus on persuasion toward agreement.
If persuaded, consider the story those who changed their attitudes from negative to positive will need to tell others about why.

Steps for engaging polarized audiences:

Identify the problem. How are people being divided against each other?
1. Do audience members consistently deflect from a common ground understanding of the data and instead bring up controversies that are not directly related to the data?
2. Do attempts to discuss interpretations of data quickly turn into veiled or direct accusations about who is wrong or immoral?
Determine tactics and tone. What strategies will keep the decision-making process moving forward?
1. Project calmness.
2. Consider the strategic value of debate. Is there genuine debate, or are polarized audience members attempting to confuse the issue or create unnecessary controversy in order to defer or destroy the decision-making process?
Stay on message. Repeating the same message while being goaded to debate may seem hostile, but, with a calm tone, it is a peaceful tactic to preserve the possibility of decision-making.

Audience Attitudes Summary

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