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.
In the past 12 months, have you personally used, reviewed, or made decisions based on multi-touch attribution data or results?
- Yes
- No
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
Over the past 3 months, how much did you trust the MTA results you used to inform decisions?
Rank the following potential biases in MTA from most concerning to least concerning in your context. Drag the most concerning to the top.
- Over-crediting branded search or direct traffic
- Self-attribution by walled gardens
- Incomplete tracking due to privacy/consent gaps
- Recency bias toward last touches
- Touchpoint inflation from ad stacking/high frequency
- Model overfitting or instability
- Selection bias in conversion data
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
Rank the following evidence sources by how much they increase your trust in MTA results. Drag the most trust-building source to the top.
- First-party site/app analytics
- Ad platform logs
- CRM/transactional data
- Offline sales data
- Experiments/holdouts
- Third-party measurement
- Panel/survey data
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.
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)
Thank you for completing this survey—your insights are greatly appreciated and will help improve MTA measurement practices.
How is MTA primarily delivered in your organization?
- Vendor product
- In-house model
- Agency-provided
- Combination of approaches
- Not sure
Looking ahead, how confident are you that MTA will produce reliable results for your organization?
Please share a brief example of how bias in MTA has shown up in your work and what impact it had.
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)
Rank the following areas by how much MTA influences your decisions. Drag the most influenced area to the top.
- Budget allocation across channels
- Channel and media mix planning
- Bidding and optimization
- Audience and targeting
- Creative and messaging
- Experiment design and validation
- Reporting and KPI setting
Based on your responses in this survey, please share any additional thoughts about trust or bias in MTA at your organization.
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
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
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
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
Approximately how many employees does your company have?
- 1–49
- 50–249
- 250–999
- 1,000–4,999
- 5,000+
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
In which region is your organization primarily based?
- North America
- Latin America
- Europe
- Middle East & Africa
- Asia-Pacific
- Other
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.
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