모든 템플릿

Marketer Trust in Identity Resolution & Graph Accuracy

Measures marketing professionals' confidence in identity resolution accuracy, validation practices, and vendor evaluation criteria. Designed for B2B research targeting practitioners and decision-makers responsible for customer identity strategy.

샘플 질문

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질문 30개 · 약 13분
Q01
메시지

Welcome! This survey (approximately 13 minutes) explores how marketing professionals evaluate identity resolution and identity graph accuracy. 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 and will be reported in aggregate only for research purposes.

Q02
객관식

Which of the following best describes your current marketing role?

  • Brand / Content marketing
  • Performance / Demand generation
  • CRM / Lifecycle / Retention
  • Product marketing
  • Growth / Acquisition
  • Media / Advertising
  • Marketing operations
  • Marketing analytics / Insights
  • Other (please specify)
Q03
객관식

Does your organization currently use identity resolution or a customer identity graph?

  • Yes, in production
  • Piloting / testing
  • No, but considering
  • No, not considering
  • Not sure
Q04
의견 척도

How confident are you in the overall accuracy of your organization's identity resolution?

척도: 17
최소:Not at all confident최대:Extremely confident
Q05
객관식

Which of the following methods has your organization used in the past 12 months to validate identity resolution accuracy? (Select all that apply)

  • Holdout / ground-truth testing
  • Manual review / spot checks
  • Independent third-party audit
  • Match against deterministic keys (e.g., login / email)
  • Incrementality / lift experiments
  • Vendor-provided accuracy proof
  • Customer service feedback / returns
  • Production monitoring alerts
  • None of the above
Q06
순위 매기기

Please rank the following factors from most to least important when evaluating trust in identity resolution.

  1. Underlying data quality and coverage
  2. Deterministic vs. probabilistic matching method
  3. Recency and freshness of the graph
  4. Transparency and documentation
  5. Independent third-party validation
  6. Privacy compliance and controls
  7. Ongoing monitoring and SLAs
드래그하여 순위 지정
Q07
의견 척도

How likely is your organization to evaluate or expand its identity resolution capabilities in the next 12 months?

척도: 17
최소:Not at all likely최대:Extremely likely
Q08
장문형

Based on your responses in this survey, what would most increase your trust in identity graphs and match accuracy?

Q09
객관식

What is your current seniority level?

  • Individual contributor
  • Manager
  • Director
  • VP / Senior Director
  • C-level / Head of Marketing
  • Consultant / Agency
  • Other (please specify)
Q10
메시지

Thank you for completing this survey! Your insights will help advance transparency and accuracy in identity resolution. If you have any questions about this research, please contact the research team.

Q11
객관식

Which of the following solutions support identity resolution at your organization? (Select all that apply)

  • Customer Data Platform (CDP)
  • Master Data Management (MDM)
  • Marketing cloud suite
  • Advertising ID graph (ad-tech)
  • Homegrown / custom solution
  • Data clean room
  • CRM / DMP
  • Other (please specify)
Q12
객관식

What minimum match accuracy would you require before deploying identity resolution in production?

  • Below 70%
  • 70–79%
  • 80–89%
  • 90–95%
  • 96–99%
  • 100% (only exact matches)
  • Don't know / haven't defined a threshold
Q13
의견 척도

How important is each of the following when selecting an identity graph provider? Match accuracy and precision

척도: 17
최소:Not at all important최대:Extremely important
Q14
AI 인터뷰

We'd like to explore your experiences with identity resolution in a bit more depth. An AI moderator will ask you a couple of follow-up questions.

Q15
드롭다운

How many years of experience do you have in marketing?

  • Less than 2 years
  • 2–5 years
  • 6–10 years
  • 11–15 years
  • 16–20 years
  • More than 20 years
Q16
객관식

To the best of your knowledge, approximately what percentage of your resolved identities were correct in the past 6 months?

  • Below 50%
  • 50–69%
  • 70–79%
  • 80–89%
  • 90–95%
  • 96–100%
  • Don't know
Q17
의견 척도

In the past 12 months, how problematic has each of the following been for your identity resolution efforts? Duplicate or fragmented customer profiles

척도: 15
최소:Not at all problematic최대:Extremely problematic
Q18
의견 척도

How important is each of the following when selecting an identity graph provider? Scale and coverage of identity data

척도: 17
최소:Not at all important최대:Extremely important
Q19
객관식

Approximately how many employees are in your company?

  • 1–49
  • 50–249
  • 250–999
  • 1,000–4,999
  • 5,000+
Q20
객관식

What are the main reasons your organization is not currently using identity resolution? (Select all that apply)

  • Insufficient budget
  • Privacy / compliance risk
  • Data quality / coverage concerns
  • Integration complexity
  • Lack of internal expertise
  • Unclear ROI or business case
  • Vendor credibility / trust
  • Stakeholder misalignment
  • We do not need it
  • Competing priorities
  • Other (please specify)
Q21
의견 척도

In the past 12 months, how problematic has each of the following been for your identity resolution efforts? Incorrect cross-device or cross-channel matching

척도: 15
최소:Not at all problematic최대:Extremely problematic
Q22
의견 척도

How important is each of the following when selecting an identity graph provider? Ease of integration with existing tech stack

척도: 17
최소:Not at all important최대:Extremely important
Q23
객관식

Which industry best describes your organization?

  • Retail / Ecommerce
  • Financial services
  • Technology / SaaS
  • Media / Entertainment
  • Healthcare / Pharma
  • Travel / Hospitality
  • Telecom
  • Consumer packaged goods
  • Automotive
  • Other (please specify)
Q24
의견 척도

In the past 12 months, how problematic has each of the following been for your identity resolution efforts? Stale or outdated identity graph data

척도: 15
최소:Not at all problematic최대:Extremely problematic
Q25
의견 척도

How important is each of the following when selecting an identity graph provider? Privacy compliance certifications and controls

척도: 17
최소:Not at all important최대:Extremely important
Q26
객관식

In which region are you primarily located?

  • North America
  • Europe
  • Asia-Pacific
  • Latin America
  • Middle East / Africa
Q27
의견 척도

In the past 12 months, how problematic has each of the following been for your identity resolution efforts? Privacy or compliance violations related to identity data

척도: 15
최소:Not at all problematic최대:Extremely problematic
Q28
의견 척도

How important is each of the following when selecting an identity graph provider? Transparency into matching methodology

척도: 17
최소:Not at all important최대:Extremely important
Q29
의견 척도

In the past 12 months, how problematic has each of the following been for your identity resolution efforts? Lack of transparency into how matches are made

척도: 15
최소:Not at all problematic최대:Extremely problematic
Q30
의견 척도

How important is each of the following when selecting an identity graph provider? Ongoing accuracy monitoring and SLAs

척도: 17
최소:Not at all important최대:Extremely important

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.

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차별화 포인트

  • Includes a dedicated adaptive AI follow-up interview segment that lets marketers elaborate on identity resolution pain points beyond fixed-choice answers
  • Combines quantitative opinion-scale batteries (match accuracy confidence, vendor evaluation criteria, problem areas) with an open-text question on what would increase trust in identity resolution
  • Captures firmographic and role-based segmentation (seniority, company size, industry, region) so results can be cut by respondent type
  • Built for B2B practitioner research with transparent, auto-scored responses and an auto-generated report, rather than being a generic identity or demographic form

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