Audience Tutorial

Sections:

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

Reaching Audiences

Effectively reaching library audiences requires a sophisticated understanding of what will engage, motivate, or persuade them. This guide examines three dimensions of audience analysis: knowledge, demographics, and attitudes. While no single resource can address all audience complexities, this framework provides a foundation for strategic communication with library stakeholders.

Knowledge

Organizational Context of Libraries

Libraries function as essential community resources designed to benefit all people, with particular emphasis on services for those who can’t afford access to books and other media. Understanding libraries as institutions requires knowledge of their fundamental practices and the ethical principles that guide their operations. For example, due to professional ethical commitments to reader privacy, libraries typically avoid permanent storage of personally identifiable information connected to specific borrowed materials. Even when technical systems retain such data, professional training emphasizes strict confidentiality protocols.

Libraries systematically collect many types of quantitative data. For example:

circulation,
registered library cardholders,
user demographics,
facility usage,
digital access metrics,

and numerous other indicators.

Library directors manage staff, and they are in turn responsible to an appointed or elected group, usually called a board of trustees. Assessing board members' knowledge about library operations presents challenges due to the multidimensional nature of individual learning experiences. Effective techniques for gauging knowledge include targeted questions about previous experiences:

"Would you please raise your hand if you participated in the last community library survey?"
"Who here remembers our most recent circulation data presentation?"
"How many of you have experience with library budget development?"
"Can you remind me who was present during our last strategic planning initiative?"

These inquiries should be framed positively to identify those with relevant knowledge rather than potentially embarrassing those without it. Similar questions can explore transferable experiences from other professional or non-profit contexts–leadership, survey expertise, program impact, etc.

Knowledge distribution within the library organization itself varies significantly. Small libraries often have staff fulfilling multiple roles, while large systems employ specialized personnel for data analysis, management, and communication functions across system, branch, and program levels. This specialization can create communication barriers between departments. For example, public relations staff may employ different terminology than frontline personnel who interact directly with patrons. Staff responsible for governmental reporting may become isolated without organization-wide data literacy. Knowledge gaps between library staff can be ameliorated through training. Comprehensive data literacy training for all staff members provides preparation for effective and collaborative data storytelling.

Data Knowledge in Context

Most decision-making audiences require library data presentations optimized for rapid comprehension. Consider how common library data types parallel metrics in other organizational settings:

Example Library Data Types:

Automated systems reports (standard or customized)
Registered borrowers/library cardholders
Program attendance
Public computer utilization
Visitor counts and statistics
Service hours
Website traffic

Automated systems reports exist in most business and nonprofit environments, with many audiences familiar with quarterly progress assessments, budget expenditure rates, and similar metrics. Data on human interactions (such as cardholders, program attendance, computer use, and visitor counts above) resembles metrics used by any public-facing organization serving numerous clients. Service hours data corresponds to usage patterns familiar to businesses with physical locations, while website traffic analysis is common to any organization with significant online resources.

For audiences with limited organizational or data literacy:

Maximize visualization of data, translating numbers into visual formats and reinforcing key messages with images, text, and annotations.
Include comprehensive captions with visualizations to ensure accessibility and reinforce interpretation.
Test visualizations with practice audiences to identify comprehension gaps and make improvements based on feedback.

Libraries must differentiate their data practices from purely business-oriented approaches. Unlike commercial entities, libraries exist in relationship with communities that are not merely consumers but co-stewards of a public resource. Statistical forecasting methods common in business settings may be problematic in library contexts, which must respond to unpredictable demographic shifts. While businesses often identify relatively stable target markets, libraries serve geographically defined populations that may undergo significant demographic transformation over time. This fundamental difference reflects contrasting perspectives: businesses viewing people as potential markets versus libraries approaching them as patrons deserving service.

Demographics

Example Demographic Data Types:

Total constituent population (patrons or students)
Household language distribution
Income level stratification
Geographic factors (neighborhoods or institutional divisions)
Age distribution
Cultural affiliations (ethnic, religious, social)
Occupational roles and industry sectors

Some demographic data can be analyzed together for an insight into patterns that inform service. For example, further examination of language demographics in relation to household income provides additional rationale for enhancing and promoting language-accessible resources.

However, data ethics must guide demographic representation. Categories like household income may imply poverty, which carries stigma. Arguments based on economic indicators should be presented carefully, emphasizing human dignity rather than exploiting narratives of hardship to justify library goals. Library workers should generally frame arguments in terms of probable community needs rather than individual narratives of deprivation. Data analysis often requires no individual human stories, particularly if such anecdotes might negatively characterize individuals or groups. Retelling stories of suffering tends to dehumanize, turning people into statistics or mere representatives of disaster rather than whole human beings. When personal experiences must be shared, they should be thoroughly anonymized unless presented directly by the affected individuals with their explicit and enthusiastic consent.

Some demographic data can provide essential context for service provision. For instance, libraries must ensure information accessibility in languages spoken within their communities. Analyzing the proportion of non-English speakers allows direct assessment of collection adequacy. If collection language distribution fails to reflect community linguistic diversity, this indicates a clear opportunity for improvement.

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.

Divided 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.

Divided

Divided 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 Divided 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 divided 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 divided 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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