Retail Returns Experience & Satisfaction Survey
Measures customer satisfaction, perceived fairness, and friction points across the retail return and refund process to inform policy improvements and reduce post-purchase churn.
샘플 질문
템플릿에 포함된 내용을 미리 확인해 보세요. 모든 질문은 설문 공개 전에 자유롭게 수정할 수 있습니다.
When was your most recent retail return or exchange?
- Within the last 30 days
- 31–90 days ago
- 3–6 months ago
- 7–12 months ago
- I have not made a return in the past 12 months
Thinking about your most recent return, how easy or difficult was it to find the return policy and determine your eligibility?
Overall, how satisfied or dissatisfied are you with your most recent return experience?
Please rank the following factors from most important to least important to you when making a return.
- Speed of receiving my refund or replacement
- Ease and simplicity of the process
- No out-of-pocket return costs
- Clear communication and status updates
- Flexible return window (time allowed to return)
Based on your most recent return experience, what one change would most improve the returns process for you?
What is your age group?
- 18–24
- 25–34
- 35–44
- 45–54
- 55–64
- 65+
- Prefer not to say
Thank you for completing this survey! Your feedback will be used to improve the retail returns experience. Your responses are confidential and will be reported only in aggregate.
How did you complete your most recent return?
- In-store return counter
- Carrier drop-off location (e.g., UPS, FedEx)
- Courier home pickup
- Parcel locker or kiosk
- Mailed directly to the retailer
- Other (please specify)
How easy or difficult was it to initiate the return (e.g., requesting a return label or authorization)?
How satisfied were you with the speed of your refund or exchange?
We'd like to explore your returns experience in a bit more depth. Please share what stood out to you — positively or negatively — about your most recent return.
Which gender do you identify with?
- Woman
- Man
- Non-binary
- Prefer not to say
What type of product did you return? Select all that apply.
- Clothing or shoes
- Electronics or appliances
- Home goods or furniture
- Beauty or personal care
- Food or beverages
- Books, media, or toys
- Other (please specify)
How easy or difficult was it to package and prepare the item for return?
Overall, how fair or unfair did the returns process feel?
Where do you currently live?
- United States
- Canada
- United Kingdom
- European Union
- Australia or New Zealand
- India
- Other
How easy or difficult was it to drop off or ship the return?
Based on your returns experience, how likely are you to purchase from this retailer again?
Approximately how many days passed from when you dropped off or shipped your return to when you received your refund or replacement?
- Same day
- 1–3 days
- 4–7 days
- 8–14 days
- 15–30 days
- More than 30 days
- I have not yet received it
- Not sure
Compared with other retailers you have returned items to, how would you rate this retailer's returns process?
Which of the following costs, if any, did you personally pay for this return? Select all that apply.
- No out-of-pocket cost
- Return shipping fee
- Restocking fee
- Packaging or materials
- Transportation or parking to store
- Other (please specify)
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
- Breaks the return journey into distinct opinion-scale questions (finding the policy, initiating the return, packaging, drop-off/shipping) instead of asking for one overall rating, so friction points can be pinpointed step by step.
- Includes an AI follow-up interview question that adaptively probes respondents to elaborate on their specific returns experience, going beyond a single static open-text box.
- Captures concrete cost and process data via a multiple-choice question on fees paid and a ranking question on what customers value most in returns, giving actionable inputs for policy changes.
- Pairs quantitative satisfaction, fairness, and repurchase-likelihood scales with automated reporting, so results are analysis-ready without manual coding of open-ends.
QuestionPro
Website Retailer Satisfaction Survey TemplateThis is a general retailer/website satisfaction template rather than one built specifically around the return-and-refund journey, so it likely needs heavy customization to probe returns-specific friction points. QuestionPro is an established survey platform with broad customization and reporting features. It's a fielding-ready static template, not one built for returns-process diagnostics.
잘하는 점
- Backed by a mature, full-featured survey platform with broad question-type support
- Template is retail-domain specific, giving relevant starting question themes
- Likely offers standard analytics/dashboarding on responses
아쉬운 점
- Static question set with no adaptive AI follow-up probing into individual answers
- No indication of per-response automated quality scoring
- No published, transparent prompt/methodology behind how questions were generated
Jotform
200+ Customer Satisfaction Evaluation FormsThis is a large directory/category page of generic customer satisfaction forms rather than a single template built for retail returns specifically, so a researcher would need to search, select, and heavily edit a form to fit the returns-and-refund use case. Jotform's strength is its form-builder ecosystem and sheer volume of templates across industries. It is not purpose-built for measuring return-process fairness or friction.
잘하는 점
- Very large template library covering many industries and use cases
- Drag-and-drop form builder allows easy customization of layout and fields
- Forms can be embedded and integrated with other Jotform tools
아쉬운 점
- No template specifically designed around the retail return/refund journey
- Static forms lack any adaptive AI interviewing or voice-based follow-up
- No automated per-response quality scoring or transparent AI prompt methodology
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