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Product & UX

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.

Sample questions

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

24 questions · ~11 min
Q01
Message

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
Dropdown

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
Opinion Scale

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

Scale: 17
Min:Not at all usefulMax:Extremely useful
Q04
Opinion Scale

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

Scale: 17
Min:No control at allMax:Complete control
Q05
Opinion Scale

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

Scale: 17
Min:Very little varietyMax:A great deal of variety
Q06
AI Interview

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
Dropdown

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
Message

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

Q09
Opinion Scale

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

Scale: 17
Min:NeverMax:Very often
Q10
Opinion Scale

The recommendations I see are relevant to my interests.

Scale: 17
Min:Strongly disagreeMax:Strongly agree
Q11
Multiple Choice

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
Opinion Scale

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

Scale: 17
Min:NeverMax:Very often
Q13
Long Text

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

Q14
Dropdown

Which region do you primarily use the product from?

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

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
Opinion Scale

The recommendations I see are timely for what I need.

Scale: 17
Min:Strongly disagreeMax:Strongly agree
Q17
Opinion Scale

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

Scale: 17
Min:No improvement at allMax:Major improvement
Q18
Ranking

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
Drag to rank
Q19
Multiple Choice

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

  • Yes
  • No
  • Not sure
Q20
Opinion Scale

I trust the accuracy of the recommendations I receive.

Scale: 17
Min:Strongly disagreeMax:Strongly agree
Q21
Ranking

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
Drag to rank
Q22
Multiple Choice

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
Opinion Scale

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

Scale: 17
Min:Strongly disagreeMax:Strongly agree
Q24
Opinion Scale

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

Scale: 17
Min:NeverMax:Always

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

  • 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.

What it does well

  • 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

Where it falls short

  • 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

Ready to launch?

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

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