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Online Bookstore Customer Experience Survey

Tracks how readers discover, buy, and receive books from your online store — covering selection, pricing, delivery, and format preferences — with an AI follow-up that digs into the story behind their most recent order and their recommendation score. Built for bookstore teams optimizing catalog, fulfillment, and retention.

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샘플 질문

템플릿에 포함된 내용을 미리 확인해 보세요. 모든 질문은 설문 공개 전에 자유롭게 수정할 수 있습니다.

질문 12개 · 약 7분
Q01
메시지

Hi! Thanks for shopping with us. We'd love to hear about your recent experience buying books from (Replace with store name) — it takes about 5 minutes and helps us stock, price, and ship things you'll actually want. Your responses are completely confidential and anonymized.

Q02
객관식필수

In the last 3 months, how many books have you purchased from (Replace with store name)?

  • None
  • 1-2
  • 3-5
  • 6-10
  • More than 10
Q03
객관식

What was your most recent purchase mainly for?

  • My own reading
  • A gift for someone else
  • A book club or class
  • Research or work
  • Collecting (rare/special editions)
  • Other
Q04
매트릭스필수

Thinking about your recent orders, how would you rate each of the following?

6개 행 × 5개 열
  • Book selection and variety
  • Pricing
  • Website or app ease of use
  • Delivery speed
  • Condition of books on arrival
  • 외 1개
열: Poor · Fair · Good · Very good · Excellent
Q05
의견 척도필수

How likely are you to recommend (Replace with store name) to a friend or fellow reader?

척도: 0 – 10
최소:Not at all likely최대:Extremely likely
Q06
AI 인터뷰

Anchor on the respondent's recommendation score and their most recent order: reconstruct what they ordered, how discovery/checkout/delivery actually went, and what specifically drove the score. If the score is low (0-6), probe the single worst moment in that order and what would have fixed it. If high (9-10), probe what specifically earned the loyalty so it can be reinforced elsewhere.

Q07
최선·최악 선택형(MaxDiff)필수

Which of these improvements would make the biggest difference to your experience? Pick your most and least important each round.

  • Faster delivery
  • Lower prices
  • Wider book selection
  • Better personalized recommendations
  • Easier returns/exchanges
  • More formats (ebook, audiobook)
  • A loyalty/rewards program
  • Sturdier packaging
  • Live chat support
세트별 최선·최악 선택최선:Most important to me최악:Least important to me
Q08
점수 배분

Imagine you had 100 points to spend across formats based on how you actually buy books today. How would you split them?

  • Physical/print books
  • E-books
  • Audiobooks
  • Rare or collectible editions
100점 배분
Q09
객관식

What device do you most often use to browse or buy books from us?

  • Desktop or laptop computer
  • Phone browser
  • Phone app
  • Tablet
  • Other
Q10
장문형

What's one thing we could change to make your next order better?

Q11
객관식

What is your age range?

  • Under 18
  • 18-24
  • 25-34
  • 35-44
  • 45-54
  • 55-64
  • 65+
  • Prefer not to say
Q12
메시지

That's everything — thank you for the detailed feedback! We'll use your answers to improve our selection, pricing, and delivery experience for readers like you.

포함된 기능

  • AI 후속 질문

    정형화된 설문이 놓치는 세부 내용을, 주관식 답변에 맞춰 AI가 심층 질문으로 끌어냅니다.

  • 주의력 확인 장치

    성의 없는 답변과 저품질 응답자를 걸러내는 내장 안전장치입니다.

  • AI가 작성한 문안

    문구, 질문 순서, 분기 로직까지 AI가 연구 목표에 맞춰 작성합니다.

  • 자동 리포트

    응답이 모이면 주요 주제, 인용문, 이해하기 쉬운 요약이 자동으로 작성됩니다.

다른 서비스와 비교

다른 설문 도구의 가장 유사한 템플릿을 검토했습니다. 그 도구들이 잘하는 점과, 이 템플릿이 한발 더 나아가는 지점을 정리했습니다.

이 템플릿을 선택하는 이유

  • Includes an AI follow-up interview that anchors specifically on the respondent's recommendation score and their most recent order, digging into the real story behind that purchase rather than stopping at rating scales
  • Combines quantitative structure (multiple choice, matrix ratings, opinion scale, max-diff, and a constant-sum format-allocation exercise) with open-ended and conversational elements for a fuller picture of buying behavior
  • Captures format preference trade-offs directly (constant-sum across formats) and device/channel usage alongside delivery and pricing perceptions, giving catalog and fulfillment teams actionable, cross-cut data
  • Opens and closes with natural chat-style messages that make the survey feel like a conversation with the store rather than a static form, which can support completion and honesty on the open-ended feedback question

QuestionPro

University Bookstore Online Survey Template

This is a static, fielding-ready template built for a university bookstore context rather than a general online bookstore retailer, so questions likely emphasize course materials and campus buyers over general trade-book customers. It's a legitimate comparable in the bookstore survey space, but its scope is narrower and audience-specific compared to a general retail bookstore experience survey.

잘하는 점

  • Purpose-built for a bookstore audience, so question wording and flow are likely tailored to book-buying behavior
  • Comes from an established survey platform with broad template library support and standard question types
  • Ready to deploy as-is for teams wanting a quick, no-setup bookstore survey

아쉬운 점

  • No adaptive AI follow-up interview — respondents can't be probed further on their most recent order or recommendation score beyond fixed questions
  • No voice AI interview option or guided screen-share tasks for deeper qualitative insight
  • As a static form, there's no per-response quality scoring or transparent prompt methodology to review

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