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Industry-Specific

Retail Shopping and Product Search Behavior Survey

Maps how shoppers browse, search, and decide across in-store, online, and marketplace channels — where product searches succeed or break down, which purchase factors carry the most weight, and what an AI follow-up interview uncovers about the last time someone couldn't find what they wanted. Built for retail, e-commerce, and merchandising teams optimizing search, navigation, and discovery.

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샘플 질문

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질문 14개 · 약 7분
Q01
메시지

Hi! We're trying to understand how people actually shop and search for products these days — in stores, online, or both. Your responses are completely confidential and anonymized. This will take about 6 minutes, and there are no right or wrong answers.

Q02
객관식필수

In the last 30 days, how did you mostly shop for the products you buy? (Replace with your product category if you want responses focused on it.)

  • Only in physical stores
  • Mostly in-store, some online
  • Roughly equal mix of both
  • Mostly online, some in-store
  • Only online
Q03
객관식필수

When you're looking for a specific product, where do you usually start your search first?

  • The retailer's own website or app search bar
  • A general search engine (e.g., Google)
  • A marketplace site (e.g., Amazon)
  • Social media or short-video platforms
  • Walking into a physical store to browse
  • Asking friends or family
  • Other
Q04
의견 척도필수

In the last 30 days, how easy was it to find the specific products you were looking for?

척도: 1 – 7
최소:Very difficult최대:Very easy
Q05
최선·최악 선택형(MaxDiff)필수

When deciding what to actually buy, which of these matter most versus least to you?

  • Price
  • Customer reviews and ratings
  • Brand reputation
  • Product is in stock and available now
  • Shipping speed and cost
  • Return policy
  • Product images and descriptions
세트별 최선·최악 선택최선:Matters most최악:Matters least
Q06
매트릭스

How satisfied are you with each of the following when searching for products, whether online or in-store?

6개 행 × 5개 열
  • Accuracy of search results
  • Filter and sort options
  • Product recommendations shown to you
  • Reviews and ratings available
  • Product images and descriptions
  • 외 1개
열: Very dissatisfied · Dissatisfied · Neutral · Satisfied · Very satisfied
Q07
객관식필수

In the last 30 days, how often did you give up on a search or abandon a cart because you couldn't find what you wanted?

  • Never
  • Once
  • A few times
  • Many times
Q08
객관식

Which device do you use most often when searching for products to buy?

  • Smartphone
  • Laptop or desktop computer
  • Tablet
  • In-store kiosk or staff assistance
  • Voice assistant
Q09
AI 인터뷰

Reconstruct the most recent time this respondent searched for a product and it did NOT go smoothly — what were they looking for, where did they search first, and exactly where did the search break down (bad results, no filters, out of stock, confusing site, unhelpful staff)? Anchor on specifics: what they clicked or asked, how long they tried before giving up, and what they did next (switched retailer, bought a substitute, abandoned the purchase entirely). If they said searches usually go fine, probe the single feature that would most improve how they discover products.

Q10
메시지

Just a few quick questions about you so we can compare shopping habits across groups — all optional.

Q11
객관식

What is your age range?

  • Under 18
  • 18-24
  • 25-34
  • 35-44
  • 45-54
  • 55-64
  • 65 or older
  • Prefer not to say
Q12
객관식

What is your gender?

  • Woman
  • Man
  • Non-binary
  • Prefer to self-describe
  • Prefer not to say
Q13
객관식

What is your annual household income?

  • Under $25,000
  • $25,000-$49,999
  • $50,000-$74,999
  • $75,000-$99,999
  • $100,000-$149,999
  • $150,000 or more
  • Prefer not to say
Q14
메시지

Thank you for sharing how you shop and search! Your answers feed into a report our product and merchandising teams use to fix broken search paths and prioritize the features that help you find things faster.

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 a dedicated AI follow-up interview that reconstructs the most recent time the respondent couldn't find a product, digging into what broke down and why
  • Combines quantitative measures (opinion scale on search ease, MaxDiff on purchase-decision factors, matrix satisfaction ratings) with open-ended AI probing in one flow
  • Captures channel behavior (in-store, online, marketplace) and device usage alongside search abandonment frequency, giving a full funnel view of discovery and drop-off
  • Ends with a clear respondent-facing explanation that answers feed into a cross-group comparison report, plus standard demographic segmentation (age, gender, income)

QuestionPro

Retail shopping and search behavior survey questions + Sample questionnaire template

This is a static questionnaire template covering retail shopping and search behavior topics, similar in subject scope to ours. It reads as a fixed question list meant for manual customization rather than an adaptive interview experience, and there's no indication of AI-driven follow-up or automated scoring.

잘하는 점

  • Directly topic-matched to retail shopping and search behavior
  • Backed by a large established survey platform with broad question-library support
  • Likely includes sample questions researchers can copy or adapt quickly

아쉬운 점

  • Static question set with no adaptive AI follow-up probing into individual responses
  • No mention of per-response quality scoring or transparent AI prompt methodology
  • No voice-based interview option for richer qualitative capture

Jotform

Shopping Behavior Survey Form Template

A form-builder template focused on general shopping behavior, useful for quick deployment and easy customization within Jotform's drag-and-drop editor. It's a standard form rather than a conversational or AI-assisted interview, so depth on any single answer depends entirely on how many questions you manually add.

잘하는 점

  • Easy drag-and-drop customization typical of Jotform's builder
  • Simple, quick to deploy for basic shopping behavior data collection
  • Wide integration options common to Jotform's ecosystem

아쉬운 점

  • No adaptive AI follow-up interview to explore why a search or purchase failed
  • No automated quality scoring of open-ended responses
  • No voice AI interview capability for deeper qualitative insight

SurveyMonkey

Market Research Product Survey Questions

This is a broader market research product survey template rather than one specifically built around retail search and discovery behavior. It's a fixed questionnaire suited to general product feedback, with SurveyMonkey's standard analytics layered on top rather than an AI-guided interview.

잘하는 점

  • Backed by SurveyMonkey's mature analytics and reporting dashboard
  • Broad applicability across general product market research use cases
  • Established platform with strong distribution and panel options

아쉬운 점

  • No adaptive AI interview to reconstruct a specific recent shopping search failure
  • Static question format without automated per-response quality scoring
  • No guided task or screen-share capability for observing search behavior directly

SurveySparrow

Product Market Research Survey Template

A conversational-style product market research template that covers general product feedback rather than retail-specific search and discovery behavior in depth. It offers a chat-like UI for respondents but relies on pre-set questions rather than AI-generated follow-up probing.

잘하는 점

  • Conversational chat-style UI that may feel more engaging than plain forms
  • General product market research focus with broad applicability
  • Likely mobile-friendly given SurveySparrow's product positioning

아쉬운 점

  • No true adaptive AI follow-up interview reconstructing a specific search breakdown
  • No published methodology or transparent AI prompt structure
  • No automated quality scoring or voice AI interview option

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