모든 템플릿

Product Recommendation Experience & Personalization Survey

Measures user satisfaction with recommendation relevance, perceived control, and content diversity. Designed for product teams optimizing personalization engines and recommendation UX.

샘플 질문

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

질문 24개 · 약 11분
Q01
메시지

Welcome! Thank you for participating in this survey about your experience with product recommendations. This survey takes approximately 11 minutes. Your participation is entirely voluntary and you may stop at any time. There are no right or wrong answers — we are interested in your honest opinions. All responses are confidential, will be reported only in aggregate, and will be used to improve the recommendation experience.

Q02
드롭다운

How long have you been using this product?

  • Less than 1 month
  • 1–6 months
  • 7–12 months
  • 1–2 years
  • More than 2 years
  • I do not use it regularly
  • Prefer not to say
Q03
의견 척도

Overall, how useful are our recommendations for achieving your goals?

척도: 17
최소:Not at all useful최대:Extremely useful
Q04
의견 척도

How much control do you feel you have over the recommendations you see?

척도: 17
최소:No control at all최대:Complete control
Q05
의견 척도

How much variety do you typically see across the recommendations shown to you?

척도: 17
최소:Very little variety최대:A great deal of variety
Q06
AI 인터뷰

I'd like to understand your recommendation experience a bit more. Let's start: What stands out most — positively or negatively — about the recommendations you've been seeing recently?

Q07
드롭다운

What is your age group?

  • Under 18
  • 18–24
  • 25–34
  • 35–44
  • 45–54
  • 55–64
  • 65 or older
  • Prefer not to say
Q08
메시지

Thank you for completing this survey — your feedback directly helps us improve your recommendation experience!

Q09
의견 척도

How often do you engage with recommendations when using the product?

척도: 17
최소:Never최대:Very often
Q10
의견 척도

The recommendations I see are relevant to my interests.

척도: 17
최소:Strongly disagree최대:Strongly agree
Q11
객관식

Which of the following controls have you used to influence your recommendations in the last 30 days? (Select all that apply)

  • Like / thumb up
  • Dislike / thumb down
  • Hide or mute items
  • Follow topics or sources
  • Adjust interests or preferences
  • Clear or reset history
  • Mark 'not interested' or skip
  • None of these
Q12
의견 척도

In the last 7 days, how often did you notice near-duplicate recommendations?

척도: 17
최소:Never최대:Very often
Q13
장문형

Based on your experience, what one change would most improve the recommendations you receive?

Q14
드롭다운

Which region do you primarily use the product from?

  • North America
  • Europe
  • Asia-Pacific
  • Latin America
  • Middle East & Africa
  • Prefer not to say
Q15
드롭다운

When did you last see recommendations in our product?

  • Within the last 7 days
  • 8–14 days ago
  • 15–30 days ago
  • More than 30 days ago
  • I don't recall
Q16
의견 척도

The recommendations I see are timely for what I need.

척도: 17
최소:Strongly disagree최대:Strongly agree
Q17
의견 척도

To what extent did the controls you used improve your recommendations?

척도: 17
최소:No improvement at all최대:Major improvement
Q18
순위 매기기

Rank the following in order of how much you would like to see them in your recommendations (most desired first).

  1. Familiar items I already know
  2. New items I haven't seen before
  3. Variety across different topics or categories
드래그하여 순위 지정
Q19
객관식

During that session, did you act on any recommendation (e.g., click, save, or purchase)?

  • Yes
  • No
  • Not sure
Q20
의견 척도

I trust the accuracy of the recommendations I receive.

척도: 17
최소:Strongly disagree최대:Strongly agree
Q21
순위 매기기

Which of the following recommendation controls would be most useful to you? (Rank your top 3)

  1. More visible feedback buttons
  2. Per-item controls (hide/mute)
  3. Topic or source follow/block
  4. Adjustable diversity slider
  5. Profile preference settings
  6. Clearer explanations of why items are recommended
드래그하여 순위 지정
Q22
객관식

What prevented you from acting on a recommendation? (Select all that apply)

  • Did not seem relevant to me
  • Already had enough options
  • Not enough helpful details provided
  • Concerns about accuracy or trust
  • Not timely for what I was doing
  • I prefer finding items myself
  • I was just browsing
  • Other (please specify)
Q23
의견 척도

The recommendations introduce me to items I would not have found on my own.

척도: 17
최소:Strongly disagree최대:Strongly agree
Q24
의견 척도

How often do recommendations lead to a successful outcome for you (e.g., finding what you need, saving time)?

척도: 17
최소:Never최대:Always

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

  • Our template dedicates multiple opinion-scale items specifically to recommendation relevance, timeliness, trust/accuracy, and content diversity (including a near-duplicate detection question), rather than treating recommendations as one line item in a general satisfaction survey
  • It includes two ranking questions so product teams can quantify which recommendation controls and which content types users actually want prioritized
  • It includes an adaptive AI follow-up interview that probes deeper into a respondent's specific recommendation experience based on their prior answers, plus an open-text question capturing one concrete improvement suggestion
  • Responses feed into an automated report with per-response quality scoring, and QuestionPunk's prompts are transparent, unlike static form tools

QuestionPro

Product Satisfaction Survey Questions + Sample Template

This is a general product satisfaction survey template rather than one built specifically around recommendation relevance, control, or content diversity. It's a fielding-ready static template from an established survey platform, useful as a broad satisfaction instrument but not tailored to personalization-engine UX research. Teams would need to heavily customize it to get recommendation-specific insight.

잘하는 점

  • Comes from a mature, full-featured survey platform with a large template library
  • Provides a ready sample question set that teams can deploy quickly
  • Likely supports standard survey logic and reporting common to QuestionPro's toolset

아쉬운 점

  • No adaptive AI follow-up interviewing — it's a fixed, static question set
  • Not focused on recommendation-specific constructs like content diversity, control, or trust in personalization
  • No automated per-response quality scoring or transparent AI prompt methodology

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