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Library Patron Satisfaction & Improvement Survey

Measures how patrons rate the library's collection, staff, spaces, and digital resources, and which improvements would matter most, with an AI follow-up that digs into the real story behind their satisfaction score. Built for public and academic libraries planning budgets, hours, or renovations.

Sample questions

A preview of what’s in the template. Every question is editable before you launch.

11 questions · ~6 min
Q01
Message

Thanks for taking a moment to share your experience with the library! This helps us decide what to fix and what to invest in next. Should take about 7 minutes.

Q02
Multiple ChoiceRequired

In the last 3 months, how often have you visited a library location or used its online services?

  • Not at all
  • Once
  • A few times
  • About weekly
  • Almost daily
Q03
Multiple ChoiceRequired

What was the main reason for your most recent visit or online session?

  • Borrowing books, movies, or other media
  • Studying or working quietly
  • Using computers or wifi
  • Attending a program or event
  • Getting research or reference help
  • Bringing children for activities
  • Returning materials only
Q04
MatrixRequired

How satisfied are you with each of the following?

7 rows × 5 columns
  • Selection of books and materials
  • Helpfulness of library staff
  • Hours of operation
  • Quiet study or work spaces
  • Digital resources (e-books, databases, streaming)
  • +2 more
Columns: Very dissatisfied · Dissatisfied · Neutral · Satisfied · Very satisfied
Q05
Opinion ScaleRequired

How likely are you to recommend this library to a friend or colleague?

Scale: 010
Min:Not at all likelyMax:Extremely likely
Q06
Best–Worst Trade-off (MaxDiff)Required

Which of these improvements would matter most to you, and which would matter least?

  • Extended evening or weekend hours
  • Larger or more current book/media collection
  • More quiet study space
  • Faster, more reliable wifi
  • More programs and events
  • Expanded digital resources (e-books, streaming, databases)
  • More self-service checkout options
Pick best & worst per setBest:Most valuable improvementWorst:Least valuable improvement
Q07
AI Interview

Explore the reasoning behind the respondent's recommendation score: ask for a specific recent moment or interaction that shaped how they feel, whether it was a standout positive experience or a frustration. If they picked a top improvement in the trade-off question, probe what would actually change about their behavior if that improvement were made. If their score is neutral or low, ask directly what would need to happen for them to recommend the library enthusiastically.

Q08
Long Text

Is there anything specific you'd change about this library if you could?

Q09
Multiple Choice

Do you currently hold a library card for this library?

  • Yes
  • No
  • Not sure
  • Prefer not to say
Q10
Multiple Choice

Which age range best describes you?

  • Under 18
  • 18-24
  • 25-34
  • 35-49
  • 50-64
  • 65 or older
  • Prefer not to say
Q11
Message

That's everything — thank you! Your feedback feeds directly into decisions about hours, collections, and services at this library.

What’s included

  • AI follow-ups

    Adaptive probes on open-ended answers that pull out detail a static form would miss.

  • Attention checks

    Built-in safeguards against rushed answers and low-quality respondents.

  • AI-drafted copy

    Wording, ordering, and branching written by the AI — tuned to your research goal.

  • Auto report

    Themes, quotes, and a plain-English summary write themselves once responses come in.

How it compares

We reviewed the closest templates from other survey tools. Here’s what they do well — and where this template goes further.

Why this template

  • Combines structured rating questions (visit frequency, satisfaction matrix, recommendation score, max-diff prioritization of improvements) with an AI follow-up interview that probes the real reasoning behind a patron's recommendation score, not just the number itself
  • Opens and closes with plain-language chat messages that set expectations and explain how the feedback will be used, making the experience feel conversational rather than a static form
  • Includes a max-diff exercise so libraries can see which improvements (hours, collection, digital resources, space) matter most vs. least, useful for budget and renovation planning
  • Captures library card status and age range alongside satisfaction data, letting libraries segment results by patron type without adding extra survey length

Jotform

Library Satisfaction Survey Form Template

A ready-to-use, static form template covering standard library satisfaction questions. It's built on Jotform's drag-and-drop form builder, so it's easy to customize fields and branding, but it functions as a fixed questionnaire rather than an adaptive interview. Good for quick deployment, less suited to uncovering the 'why' behind a rating.

What it does well

  • Fielding-ready template that can be customized via Jotform's widely-used drag-and-drop builder
  • Likely integrates with Jotform's broader form ecosystem (payment, notifications, storage add-ons)

Where it falls short

  • No adaptive follow-up questioning — every respondent sees the same fixed question set regardless of their answers
  • No mention of voice interviews or automated per-response quality scoring
  • No transparent, publishable prompt methodology since it isn't an AI-driven interview tool

SurveySparrow

Library Satisfaction Survey Template | For Academic or Public Libraries

Explicitly targets both academic and public libraries, matching QuestionPunk's audience well. SurveySparrow's conversational form format gives a friendlier feel than a plain grid, but it remains a pre-set question flow rather than a true AI-driven interview that adapts based on responses.

What it does well

  • Purpose-built for library audiences, covering both academic and public library contexts
  • Conversational UI format that feels more engaging than a traditional multi-page form

Where it falls short

  • No adaptive AI follow-up — the conversational format is scripted, not dynamically generated per respondent
  • No voice-based interview option or automated quality scoring of open-ended responses
  • No published academic pricing tier or transparent prompt-level methodology to review

Ready to launch?

Open this template in the editor. Every part is yours to change before the first respondent sees it.