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Retail Store Visit Experience Evaluation

Captures how shoppers rate a specific store visit — cleanliness, staff, stock, layout, checkout — plus overall satisfaction and likelihood to recommend or return. An AI follow-up interview digs into the real friction behind any low ratings, like a specific stockout or a bad checkout interaction, so store ops teams know exactly what to fix.

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샘플 질문

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질문 14개 · 약 7분
Q01
메시지

Thanks for shopping with us! We'd love to hear how your visit today went — your feedback helps us improve this store. Your responses are completely confidential and anonymized. It'll take about 8 minutes. (Template note: replace 'this store' with your store/banner name before launching.)

Q02
객관식필수

In the last 3 months, how often have you shopped at this store?

  • This was my first visit
  • 1-2 times
  • 3-5 times
  • 6-10 times
  • More than 10 times
Q03
객관식필수

What was the main reason for your visit today?

  • Planned purchase of a specific item
  • Browsing / no specific item in mind
  • Picking up an online order
  • Return or exchange
  • Other
Q04
매트릭스필수

Rate the store on each of the following:

5개 행 × 5개 열
  • Store cleanliness
  • Staff friendliness and helpfulness
  • Product availability and stock levels
  • Ease of finding items (layout and signage)
  • Checkout speed and efficiency
열: Very poor · Poor · Average · Good · Excellent
Q05
객관식필수

Did you find what you were looking for today?

  • Yes, found everything I needed
  • Partially - found some items but not all
  • No, could not find what I needed
Q06
의견 척도필수

Overall, how satisfied were you with your visit today?

척도: 1 – 5
최소:Very dissatisfied최대:Very satisfied
Q07
의견 척도필수

How likely are you to recommend this store to a friend or family member?

척도: 0 – 10
최소:Not at all likely최대:Extremely likely
Q08
AI 인터뷰

Reconstruct the specific moment that most shaped this respondent's satisfaction and recommendation score. If they rated staff, stock, or checkout poorly in the ratings, get the concrete details: what happened, which item or department was involved, and what an employee did or failed to do. If their scores were high, ask what one thing would have made the visit even better. Do not let them stay abstract — anchor on this specific visit, not general impressions of the brand.

Q09
최선·최악 선택형(MaxDiff)

If this store could only fix a few things, which would matter most to you?

  • Faster checkout lines
  • Better product availability / fewer out-of-stock items
  • Clearer signage and easier store layout
  • More knowledgeable or attentive staff
  • Better pricing and promotions
  • Cleaner restrooms and store maintenance
  • Easier returns and exchanges
세트별 최선·최악 선택최선:Most important to improve최악:Least important to improve
Q10
객관식필수

How likely are you to shop at this store again in the next month?

  • Definitely will return
  • Probably will return
  • Not sure
  • Probably will not return
  • Definitely will not return
Q11
장문형

Anything else about today's visit you'd like us to know?

Q12
객관식

Which age range do you fall into?

  • Under 18
  • 18-24
  • 25-34
  • 35-44
  • 45-54
  • 55-64
  • 65+
  • Prefer not to say
Q13
객관식

How do you describe your gender?

  • Woman
  • Man
  • Non-binary
  • Prefer to self-describe
  • Prefer not to say
Q14
메시지

Thank you for sharing your feedback! Your responses go directly to the store team and help shape improvements to staffing, stock, and store layout.

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

  • Goes beyond static ratings on cleanliness, staff, stock, layout, and checkout with an AI follow-up interview that reconstructs the specific moment (e.g., a stockout or bad checkout interaction) that drove a low score, so store ops know exactly what to fix.
  • Combines standard satisfaction and recommend/return-likelihood questions with a MaxDiff prioritization question, giving stores a ranked view of what to fix first rather than just averages.
  • Captures shopping frequency and visit intent alongside experience ratings, so results can be segmented by loyal vs. occasional shoppers.
  • Runs on a platform with automated per-response quality scoring and auto-generated reports, plus a free tier to start — with transparent prompts showing exactly what the AI asked and why.

QuestionPro

Comparative Retail Store Evaluation Survey Template

This template is built around comparing multiple store locations or visits side by side, which is useful for multi-site retail chains. It's a fielding-ready static questionnaire with standard rating items rather than a conversational or adaptive tool. Follow-up on any specific low rating would require manual survey logic or separate open-text questions.

잘하는 점

  • Framed specifically for cross-store or cross-visit comparison
  • Backed by QuestionPro's established survey platform and reporting tools
  • Likely includes standard Likert/rating scales covering common store attributes

아쉬운 점

  • No adaptive AI follow-up interview to probe the real cause behind a low score
  • No indication of prompt-level transparency into how questions were generated or scored
  • No automated per-response quality scoring mentioned

SurveySparrow

Retail Store Evaluation Survey Template

SurveySparrow's template uses its conversational, chat-style survey format, which can make static rating questions feel more engaging than a typical form. It's still a fixed question set, not a truly adaptive interview that changes based on answers. Good for a friendlier respondent experience, less suited to uncovering the specific incident behind a complaint.

잘하는 점

  • Conversational chat-style delivery for a more engaging respondent experience
  • Marketing-template context suggests built-in analytics dashboards
  • Likely mobile-friendly given SurveySparrow's product focus

아쉬운 점

  • No adaptive AI follow-up interview or voice interview option
  • Conversational UI is scripted, not dynamically branching based on response content
  • No published methodology for how quality or sentiment scoring works

Typeform

Retail Store Feedback Form Template

Typeform's template offers its signature clean, one-question-at-a-time form design, which tends to improve completion rates over dense grid-style surveys. It's a static feedback form, not an interview, so any low rating (e.g., on checkout or stock) won't be automatically probed further. Best suited for lightweight, quick-turnaround feedback rather than root-cause diagnosis.

잘하는 점

  • Polished, distraction-free one-question-per-screen design
  • Simple to deploy for quick customer feedback capture
  • Likely integrates with Typeform's broader form and logic-jump ecosystem

아쉬운 점

  • No AI-driven follow-up interview to dig into the reason behind a poor rating
  • No voice interview or guided task/screen-share option
  • No automated quality scoring of individual responses

Jotform

Store Visit Report Form Template

This Jotform template appears oriented toward internal store visit reporting — e.g., a field auditor or manager documenting store conditions — rather than a customer-facing satisfaction survey. It's relevant to the broader 'store visit evaluation' space but serves a different audience and purpose than a shopper feedback survey. Worth noting the target respondent differs from ours before treating it as a direct substitute.

잘하는 점

  • Structured for operational/audit-style documentation of a store visit
  • Jotform's form builder supports file/photo uploads useful for compliance checks
  • Easy to customize fields for internal reporting workflows

아쉬운 점

  • Not designed to capture customer satisfaction or recommend/return likelihood
  • No adaptive AI follow-up interview or customer-facing voice interview
  • No automated quality scoring or AI-generated insight reports

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