All templates
Marketing & Growth

Multi-Touch Attribution Trust & Bias Assessment

Measures marketer trust in multi-touch attribution outputs and identifies perceived channel biases. Designed for marketing, analytics, and media professionals who work with attribution data to inform budget and optimization decisions.

Sample questions

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

24 questions · ~11 min
Q01
Message

Welcome! This survey explores your experience with multi-touch attribution (MTA) and how it informs your marketing decisions. It should take approximately 11 minutes to complete. 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 only in aggregate. Results will be used for internal research to improve measurement practices. Please answer based on your experience over the past 3–6 months.

Q02
Multiple Choice

In the past 12 months, have you personally used, reviewed, or made decisions based on multi-touch attribution data or results?

  • Yes
  • No
Q03
Multiple Choice

Which of the following attribution or measurement approaches has your organization used in the past 12 months? Select all that apply.

  • Multi-touch attribution (rules-based)
  • Multi-touch attribution (algorithmic/data-driven)
  • Marketing mix modeling (MMM)
  • Last-click attribution
  • First-touch attribution
  • Position-based/heuristic models
  • None of the above
  • Not sure
Q04
Opinion Scale

Over the past 3 months, how much did you trust the MTA results you used to inform decisions?

Scale: 17
Min:Not at allMax:Completely
Q05
Ranking

Rank the following potential biases in MTA from most concerning to least concerning in your context. Drag the most concerning to the top.

  1. Over-crediting branded search or direct traffic
  2. Self-attribution by walled gardens
  3. Incomplete tracking due to privacy/consent gaps
  4. Recency bias toward last touches
  5. Touchpoint inflation from ad stacking/high frequency
  6. Model overfitting or instability
  7. Selection bias in conversion data
Drag to rank
Q06
Multiple Choice

Which of the following steps has your organization taken to reduce bias in MTA outputs? Select all that apply.

  • Apply lookback windows or decay functions
  • Exclude brand search or direct from credit
  • Deduplicate conversions across platforms
  • Calibrate with MMM or causal lift studies
  • Run holdouts or geo experiments
  • Commission independent or vendor audit
  • Review model transparency and features
  • Data quality checks (consent, IDs, events)
  • Other (please specify)
  • None of the above
Q07
Ranking

Rank the following evidence sources by how much they increase your trust in MTA results. Drag the most trust-building source to the top.

  1. First-party site/app analytics
  2. Ad platform logs
  3. CRM/transactional data
  4. Offline sales data
  5. Experiments/holdouts
  6. Third-party measurement
  7. Panel/survey data
Drag to rank
Q08
AI Interview

We'd like to explore your experiences with MTA trust and bias in a bit more depth. An AI moderator will ask you a couple of follow-up questions based on your earlier responses.

Q09
Multiple Choice

Which of the following best describes your primary role?

  • Marketing leadership
  • Performance marketing
  • Growth/Acquisition
  • Data science/Analytics
  • Media/Activation
  • Product/MarTech
  • Consultant/Agency
  • Other (please specify)
Q10
Message

Thank you for completing this survey—your insights are greatly appreciated and will help improve MTA measurement practices.

Q11
Multiple Choice

How is MTA primarily delivered in your organization?

  • Vendor product
  • In-house model
  • Agency-provided
  • Combination of approaches
  • Not sure
Q12
Opinion Scale

Looking ahead, how confident are you that MTA will produce reliable results for your organization?

Scale: 17
Min:Not at all confidentMax:Extremely confident
Q13
Long Text

Please share a brief example of how bias in MTA has shown up in your work and what impact it had.

Q14
Multiple Choice

Which of the following would meaningfully increase your confidence in MTA results? Select all that apply.

  • Transparent methodology and assumptions
  • Third-party audit or validation
  • Alignment with MMM or causal lift studies
  • Regular back-testing and out-of-sample validation
  • Access to raw signals and feature importances
  • Better identity resolution or clean-room integrations
  • Clear conversion deduplication policy
  • Geo or cell-level experiments
  • Other (please specify)
Q15
Ranking

Rank the following areas by how much MTA influences your decisions. Drag the most influenced area to the top.

  1. Budget allocation across channels
  2. Channel and media mix planning
  3. Bidding and optimization
  4. Audience and targeting
  5. Creative and messaging
  6. Experiment design and validation
  7. Reporting and KPI setting
Drag to rank
Q16
Long Text

Based on your responses in this survey, please share any additional thoughts about trust or bias in MTA at your organization.

Q17
Multiple Choice

How many years of experience do you have working with attribution or MTA?

  • Less than 1 year
  • 1–2 years
  • 3–4 years
  • 5–7 years
  • 8+ years
Q18
Multiple Choice

How would you describe your involvement in decisions informed by MTA?

  • I make final decisions
  • I influence decisions
  • I consume results but don't decide
  • I implement/operate MTA
  • Not involved
Q19
Multiple Choice

In the past 3 months, how often did MTA results disagree with other measurement approaches (e.g., MMM, experiments)?

  • Never
  • Rarely (less than monthly)
  • Sometimes (about monthly)
  • Often (weekly or more)
  • Not applicable—did not compare
Q20
Dropdown

What minimum confidence level do you typically require before acting on MTA findings?

  • Below 50%
  • 50–59%
  • 60–69%
  • 70–79%
  • 80–89%
  • 90–95%
  • Above 95%
  • I don't use a specific threshold
  • Not sure
Q21
Multiple Choice

Approximately how many employees does your company have?

  • 1–49
  • 50–249
  • 250–999
  • 1,000–4,999
  • 5,000+
Q22
Multiple Choice

What is your organization's approximate annual paid media spend?

  • Under $1M
  • $1M–$4.9M
  • $5M–$19.9M
  • $20M–$99.9M
  • $100M+
  • Prefer not to say
Q23
Multiple Choice

In which region is your organization primarily based?

  • North America
  • Latin America
  • Europe
  • Middle East & Africa
  • Asia-Pacific
  • Other
Q24
Multiple Choice

Which industry best describes your organization?

  • Retail/E-commerce
  • Consumer services
  • B2B/Enterprise
  • Technology/Software
  • Media/Entertainment
  • Financial services
  • Travel/Hospitality
  • Healthcare/Pharma
  • Other
  • Prefer not to say

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.

Why this template

What this template is built to do — we found no directly comparable template from other survey tools to review.

What sets it apart

  • Includes multiple ranking exercises that force respondents to prioritize specific MTA biases, trusted evidence sources, and decision areas—yielding relative, not just absolute, bias signals
  • Combines quantitative trust and confidence opinion-scale questions with an open-text prompt asking for a concrete example of bias, plus a follow-up open-text reflection at the end
  • Uses an AI follow-up interview to probe deeper into individual trust/bias experiences after the structured questions, something a static form cannot replicate
  • Segments respondents by role, experience, company size, media spend, region, and industry, enabling cross-cuts of trust and bias perception by professional context

Ready to launch?

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

Related templates

More studies from the same category.

See all
Marketing & Growth

MMM Readiness & Data Governance Assessment

Evaluates an organization's Marketing Mix Modeling maturity across data foundations, governance practices, validation methods, and resourcing. Designed for marketing, analytics, and media professionals involved in or planning MMM initiatives.

View template
Marketing & Growth

Campaign Goals & Attribution Practices Audit

A 90-day diagnostic survey for ecommerce teams to evaluate campaign goal-setting, attribution methods, tool effectiveness, and cross-functional alignment — surfacing gaps in ROAS, CAC measurement, and reporting workflows.

View template
Marketing & Growth

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.

View template
Marketing & Growth

Email Deliverability Practices Assessment

Assesses email marketers' authentication setup, list hygiene, monitoring habits, and inbox-placement confidence to identify deliverability gaps and training needs.

View template
Marketing & Growth

Entrepreneur Tool Marketing: Message Recall & Channel Effectiveness Survey

Measures unaided message recall, channel exposure, and message resonance among entrepreneurs and small-business professionals who encountered software marketing in the past four weeks. Use this to identify which channels drive the strongest awareness and behavioral intent.

View template
Marketing & Growth

Direct Mail Campaign Response & Effectiveness Survey

Measures how recipients actually notice, read, and act on direct mail pieces — postcards, catalogs, letters — versus digital channels, with a best-worst exercise on what drives response and an AI follow-up that reconstructs what happened after a specific piece landed in someone's mailbox. Built for marketing teams and direct mail agencies evaluating campaign performance.

View template